07-cod-RNAseq-DESeq2
Kathleen Durkin
2024-03-19
Differential gene expression analysis for Pacific cod RNAseq data.
trimmed reads generated in
05-cod-RNAseq-trimmingReads aligned to transcriptome downloaded from NCBI, stored here as a part of lab genomic resources.
0.0.1 Install and load packages
## clear
rm(list=ls())
## Install Rtools directly from (https://cran.r-project.org/bin/windows/Rtools/), then install these on first run:
# install.packages("BiocManager")
# BiocManager::install("DESeq2")
# BiocManager::install("vsn")
# BiocManager::install("tidybulk")
# BiocManager::install("goseq")
# BiocManager::install("affycoretools")
# BiocManager::install("EnhancedVolcano")
# BiocManager::install("pcaExplorer")
# BiocManager::install("apeglm")
# BiocManager::install("PCAtools")
# List of packages we want to install (run every time)
load.lib<-c("DESeq2","edgeR","goseq","dplyr","GenomicFeatures","data.table","calibrate","affycoretools","data.table","vsn","tidybulk","ggplot2","cowplot","pheatmap","gplots","RColorBrewer","EnhancedVolcano","pcaExplorer","readxl","apeglm","ashr","tibble","plotly","sqldf","PCAtools","ggpubr","beepr","genefilter","ComplexHeatmap","circlize","scales", "tidyverse", "gridextra'")
# Select only the packages that aren't currently installed (run every time)
# install.lib <- load.lib[!load.lib %in% installed.packages()]
# And finally we install the missing packages, including their dependency.
# for(lib in install.lib) install.packages(lib,dependencies=TRUE)
# After the installation process completes, we load all packages.
sapply(load.lib,require,character=TRUE) DESeq2 edgeR goseq dplyr GenomicFeatures
TRUE TRUE TRUE TRUE TRUE
data.table calibrate affycoretools data.table vsn
TRUE TRUE TRUE TRUE TRUE
tidybulk ggplot2 cowplot pheatmap gplots
TRUE TRUE TRUE TRUE TRUE
RColorBrewer EnhancedVolcano pcaExplorer readxl apeglm
TRUE TRUE TRUE TRUE TRUE
ashr tibble plotly sqldf PCAtools
TRUE TRUE TRUE TRUE TRUE
ggpubr beepr genefilter ComplexHeatmap circlize
TRUE TRUE TRUE FALSE TRUE
scales tidyverse gridextra'
TRUE TRUE FALSE I found the DESeq2 vignette and the HBC DGE training workshop super helpful in figuring out how to use the DESeq2 package!
1 Load data
1.1 Load count data
Load in the count matrix we generated after kallisto pseudoalignment using the Trinity abundance_estimates_to_matrix.pl script. We also need to slightly reformat the count matrix to make all of the estimated counts integers, as required for DESeq2.
# Read in counts data. This is a gene-level counts matrix generated from kallisto transcript abundances using Trinity
cod_counts_data_OG <- read_delim("../output/06-cod-RNAseq-alignment/kallisto/kallisto.isoform.counts.matrix")
head(cod_counts_data_OG)# A tibble: 6 × 81
...1 kallisto_quant_100 kallisto_quant_107 kallisto_quant_108
<chr> <dbl> <dbl> <dbl>
1 XR_009522893.1 6 1 15.7
2 XM_060040253.1 2959 1479 3329
3 XM_060042128.1 242 372 272
4 XM_060040193.1 0 0 0
5 XR_009528221.1 2.40 0 3.62
6 XM_060066729.1 1310. 307. 1094.
# ℹ 77 more variables: kallisto_quant_109 <dbl>, kallisto_quant_10 <dbl>,
# kallisto_quant_110 <dbl>, kallisto_quant_117 <dbl>,
# kallisto_quant_118 <dbl>, kallisto_quant_119 <dbl>,
# kallisto_quant_11 <dbl>, kallisto_quant_120 <dbl>,
# kallisto_quant_121 <dbl>, kallisto_quant_127 <dbl>,
# kallisto_quant_128 <dbl>, kallisto_quant_129 <dbl>,
# kallisto_quant_12 <dbl>, kallisto_quant_131 <dbl>, …1.2 Count data munging
# # We need to modify this data frame so that the row names are actually row names, instead of comprising the first column
cod_counts_data <- cod_counts_data_OG %>%
column_to_rownames(var = "...1")
# Additional formatting
# Round all estimated counts to integers
cod_counts_data <- round(cod_counts_data, digits = 0)
# Remove the "kallisto_quant_" portion of the column names, to leave just the sample names
colnames(cod_counts_data) <- sub("kallisto_quant_", "sample_", colnames(cod_counts_data))
# Reorder the coumns into alphabetical order (to make it easier to create an associated metadata spreadsheet)
cod_counts_data <- cod_counts_data[, order(colnames(cod_counts_data))]
cod_sample_names <- names(cod_counts_data)
head(cod_counts_data) sample_1 sample_10 sample_100 sample_107 sample_108 sample_109
XR_009522893.1 5 67 6 1 16 0
XM_060040253.1 1848 4159 2959 1479 3329 2901
XM_060042128.1 260 216 242 372 272 339
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 1 3 2 0 4 1
XM_060066729.1 4870 1382 1310 307 1094 961
sample_11 sample_110 sample_117 sample_118 sample_119 sample_12
XR_009522893.1 4 2 7 3 10 12
XM_060040253.1 3118 2775 5439 2523 3603 2938
XM_060042128.1 228 295 247 215 337 210
XM_060040193.1 0 1 0 0 0 0
XR_009528221.1 0 0 0 2 2 6
XM_060066729.1 663 829 734 2147 271 691
sample_120 sample_121 sample_127 sample_128 sample_129 sample_13
XR_009522893.1 9 1 2 0 6 26
XM_060040253.1 5525 2720 4464 1931 244 2544
XM_060042128.1 210 255 283 219 51 213
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 1 0 5 7 16 0
XM_060066729.1 390 302 1266 566 81 914
sample_131 sample_137 sample_138 sample_139 sample_140
XR_009522893.1 22 2 6 0 5
XM_060040253.1 1966 1482 555 1666 3106
XM_060042128.1 116 149 131 128 131
XM_060040193.1 0 0 0 0 0
XR_009528221.1 0 0 0 1 2
XM_060066729.1 506 156 133 425 300
sample_147 sample_148 sample_149 sample_150 sample_18 sample_19
XR_009522893.1 4 1 101 40 29 5
XM_060040253.1 1260 3560 8866 3845 2018 2227
XM_060042128.1 147 407 1075 404 183 259
XM_060040193.1 0 0 2 1 0 0
XR_009528221.1 0 0 29 2 6 3
XM_060066729.1 670 1848 2158 1251 1961 310
sample_19-G sample_19-S sample_2 sample_20 sample_20-G
XR_009522893.1 17 6 10 14 91
XM_060040253.1 759 520 2352 3003 785
XM_060042128.1 672 392 211 171 673
XM_060040193.1 37 5 0 1 54
XR_009528221.1 0 3 0 0 9
XM_060066729.1 169 72 915 1616 233
sample_20-S sample_21 sample_28 sample_29 sample_3 sample_30
XR_009522893.1 27 1 6 1 3 9
XM_060040253.1 376 1852 2449 4368 2739 4588
XM_060042128.1 334 184 203 170 204 248
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 1 3 1 4 1 2
XM_060066729.1 99 1029 500 967 621 860
sample_31 sample_37 sample_38 sample_39 sample_4 sample_40
XR_009522893.1 1 1 11 1 2 1
XM_060040253.1 2309 2899 4284 3392 2915 3570
XM_060042128.1 200 355 425 397 190 425
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 34 0 0 0 2 1
XM_060066729.1 17829 365 329 348 4537 279
sample_41 sample_47 sample_48 sample_49 sample_5 sample_50
XR_009522893.1 11 1 1 30 74 2
XM_060040253.1 2806 5470 1262 4381 1772 5604
XM_060042128.1 191 304 439 251 227 226
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 4 0 0 4 0 0
XM_060066729.1 1242 468 315 1067 3191 408
sample_57 sample_57-G sample_57-S sample_58 sample_58-G
XR_009522893.1 3 43 3 7 11
XM_060040253.1 4808 939 132 4099 934
XM_060042128.1 260 945 57 229 972
XM_060040193.1 0 8 0 0 8
XR_009528221.1 0 0 33 6 8
XM_060066729.1 578 140 78 650 161
sample_58-S sample_59 sample_60 sample_67 sample_68 sample_69
XR_009522893.1 0 2 35 26 3 3
XM_060040253.1 245 3161 6914 7081 8208 5075
XM_060042128.1 377 413 203 307 287 252
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 0 1 8 1 13 2
XM_060066729.1 65 471 659 530 798 434
sample_70 sample_78 sample_79 sample_80 sample_83 sample_88
XR_009522893.1 2 0 7 29 5 1
XM_060040253.1 3281 1860 2683 2548 1171 1734
XM_060042128.1 247 221 211 219 236 257
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 4 0 1 1 0 1
XM_060066729.1 686 495 910 751 389 340
sample_90 sample_91 sample_92 sample_97 sample_98 sample_99
XR_009522893.1 4 5 1 4 17 2
XM_060040253.1 2380 2404 2205 422 684 1176
XM_060042128.1 350 219 150 386 367 135
XM_060040193.1 0 0 0 0 0 0
XR_009528221.1 0 11 1 1 13 2
XM_060066729.1 152 1469 617 508 236 2265
sample_RESUB-116 sample_RESUB-156 sample_RESUB-36
XR_009522893.1 3 6 7
XM_060040253.1 3164 2599 1391
XM_060042128.1 124 172 172
XM_060040193.1 0 0 1
XR_009528221.1 6 4 7
XM_060066729.1 1943 2065 222
sample_RESUB-76 sample_RESUB-94
XR_009522893.1 2 25
XM_060040253.1 6760 3726
XM_060042128.1 195 161
XM_060040193.1 1 0
XR_009528221.1 2 0
XM_060066729.1 586 1696cod_sample_names [1] "sample_1" "sample_10" "sample_100" "sample_107"
[5] "sample_108" "sample_109" "sample_11" "sample_110"
[9] "sample_117" "sample_118" "sample_119" "sample_12"
[13] "sample_120" "sample_121" "sample_127" "sample_128"
[17] "sample_129" "sample_13" "sample_131" "sample_137"
[21] "sample_138" "sample_139" "sample_140" "sample_147"
[25] "sample_148" "sample_149" "sample_150" "sample_18"
[29] "sample_19" "sample_19-G" "sample_19-S" "sample_2"
[33] "sample_20" "sample_20-G" "sample_20-S" "sample_21"
[37] "sample_28" "sample_29" "sample_3" "sample_30"
[41] "sample_31" "sample_37" "sample_38" "sample_39"
[45] "sample_4" "sample_40" "sample_41" "sample_47"
[49] "sample_48" "sample_49" "sample_5" "sample_50"
[53] "sample_57" "sample_57-G" "sample_57-S" "sample_58"
[57] "sample_58-G" "sample_58-S" "sample_59" "sample_60"
[61] "sample_67" "sample_68" "sample_69" "sample_70"
[65] "sample_78" "sample_79" "sample_80" "sample_83"
[69] "sample_88" "sample_90" "sample_91" "sample_92"
[73] "sample_97" "sample_98" "sample_99" "sample_RESUB-116"
[77] "sample_RESUB-156" "sample_RESUB-36" "sample_RESUB-76" "sample_RESUB-94" 1.3 Import sample metadata sheets
# Read in the csv file as a data frame
cod_sample_info_OG <- read.csv("~/project-cod-temperature/data/DESeq2_Sample_Information.csv")
cod_experiment_alldata_OG <- read.csv("~/project-cod-temperature/data/temp-experiment.csv")
head(cod_sample_info_OG) sample_name sample_number tank temp_treatment tissue_type
1 sample_1 1 1 16 Liver
2 sample_10 10 2 16 Liver
3 sample_100 100 15 9 Liver
4 sample_107 107 16 9 Liver
5 sample_108 108 16 9 Liver
6 sample_109 109 16 9 Liverhead(cod_experiment_alldata_OG) Microchip.ID SL_11212022 WWT_11212022 Tank Temperature SL_12272022
1 620 93 8.53 1 16 101
2 1164 88 7.06 1 16 96
3 1476 102 10.70 1 16 108
4 9387 87 7.83 1 16 95
5 9407 100 11.51 1 16 117
6 9415 92 8.68 1 16 100
WWT_12272022 MortDate DissectionDate SL_mm WholeBodyWW_g TOTAL_Liver_WW_mg
1 11.12 2/8/23 119 16.15 0.4945
2 8.64 2/8/23 105 10.89 0.1997
3 12.25 2/8/23 110 12.97 0.1715
4 10.16 2/8/23 116 15.40 0.3625
5 14.98 2/8/23 127 17.98 0.3482
6 10.96 2/8/23 114 14.02 0.2343
LiverforLipids_WW_mg MuscleWWforLipids_mg GeneticSamplingCount
1 0.1546 0.3495 8
2 0.1091 0.3328 5
3 0.1107 0.3834 4
4 0.1681 0.3262 6
5 0.1210 0.3434 2
6 0.1342 0.2776 9
DissectionComments
1
2
3
4
5
6 # Rename the "GeneticSamplingCount" column of the experimental data to "sample_number"
cod_experiment_alldata <- cod_experiment_alldata_OG
names(cod_experiment_alldata)[names(cod_experiment_alldata) == "GeneticSamplingCount"] <- "sample_number"
# Calculate length difference and weight difference (end-beginning)
cod_experiment_alldata$SL_diff_mm <- cod_experiment_alldata$SL_mm - cod_experiment_alldata$SL_11212022
cod_experiment_alldata$WWT_diff_g <- cod_experiment_alldata$WholeBodyWW_g - cod_experiment_alldata$WWT_11212022
# Merge the two data frames to get experimental data for all of our RNAseq'd samples.
# This should include all rows from cod_sample_info_OG and matching rows from cod_experiment_alldata based on the shared sample_number column. Sample number duplicates (e.g. from different tissue types) should be retained.
cod_sample_info <- merge(cod_sample_info_OG, cod_experiment_alldata, by = "sample_number", all.x = TRUE)
# Reorder the data frame into alphabetical order by the sample names, so that the rows are in the same order as our count matrix columns
cod_sample_info <- cod_sample_info[order(cod_sample_info$sample_name), ]
# Again, we need to reformat so that the data in the first column becomes the row names
rownames(cod_sample_info) <- cod_sample_info$sample_name
# Remove duplicate columns (artifact of merging data frames with multiple shared columns and of making sample_name the rownames instead of a variable)
cod_sample_info <- subset(cod_sample_info, select=-Temperature)
cod_sample_info <- subset(cod_sample_info, select=-Tank)
#cod_sample_info <- subset(cod_sample_info, select=-sample_name)
head(cod_sample_info) sample_number sample_name tank temp_treatment tissue_type
sample_1 1 sample_1 1 16 Liver
sample_10 10 sample_10 2 16 Liver
sample_100 100 sample_100 15 9 Liver
sample_107 107 sample_107 16 9 Liver
sample_108 108 sample_108 16 9 Liver
sample_109 109 sample_109 16 9 Liver
Microchip.ID SL_11212022 WWT_11212022 SL_12272022 WWT_12272022
sample_1 9443 99 10.54 108 12.94
sample_10 9518 95 9.45 105 12.67
sample_100 9483 70 4.54 78 5.23
sample_107 4236 94 9.15 104 11.44
sample_108 9416 81 6.26 91 7.87
sample_109 9481 89 7.77 95 9.49
MortDate DissectionDate SL_mm WholeBodyWW_g TOTAL_Liver_WW_mg
sample_1 2/8/23 114 14.39 0.0896
sample_10 2/8/23 120 16.22 0.3854
sample_100 2/9/23 93 8.33 0.2558
sample_107 2/9/23 119 16.41 0.5612
sample_108 2/9/23 106 11.67 0.3650
sample_109 2/9/23 116 11.45 0.3088
LiverforLipids_WW_mg MuscleWWforLipids_mg DissectionComments
sample_1 0.0704 0.3899 lipid inserts
sample_10 0.1285 0.2967
sample_100 0.1143 0.3483
sample_107 0.1503 0.3322
sample_108 0.1125 0.3612
sample_109 0.1090 0.3062
SL_diff_mm WWT_diff_g
sample_1 15 3.85
sample_10 25 6.77
sample_100 23 3.79
sample_107 25 7.26
sample_108 25 5.41
sample_109 27 3.681.4 Sample metadata munging
# Factor variables
cod_sample_info$temp_treatment <- factor(cod_sample_info$temp_treatment)
cod_sample_info$tank <- factor(cod_sample_info$tank)
cod_sample_info$tissue_type <- factor(cod_sample_info$tissue_type)
# Remove bad samples
# MuliQC report: 149, 129
# PCA outliers: 31, 41 (per Laura)
cod_sample_info <- cod_sample_info[!(row.names(cod_sample_info) %in% c("sample_149", "sample_129", "sample_31", "sample_41")),]
cod_counts_data <- as.matrix(subset(cod_counts_data, select=-c(sample_149, sample_129, sample_31, sample_41)))
# Check that the column names of our count data match the row names of our sample info sheet
ncol(cod_counts_data)[1] 76nrow(cod_sample_info)[1] 76all(colnames(cod_counts_data) %in% rownames(cod_sample_info))[1] TRUEall(colnames(cod_counts_data) == rownames(cod_sample_info))[1] TRUECombine counts data and sample metadata for later analyses
# Melt cod_counts_data to long format
cod_counts_data_long <- as.data.frame(cod_counts_data)
cod_counts_data_long$gene <- rownames(cod_counts_data_long)
cod_counts_data_long <- cod_counts_data_long %>%
pivot_longer(cols = starts_with("sample"),
names_to = "sample_name",
values_to = "count")
colnames(cod_counts_data_long)[1] "gene" "sample_name" "count" colnames(cod_sample_info) [1] "sample_number" "sample_name" "tank"
[4] "temp_treatment" "tissue_type" "Microchip.ID"
[7] "SL_11212022" "WWT_11212022" "SL_12272022"
[10] "WWT_12272022" "MortDate" "DissectionDate"
[13] "SL_mm" "WholeBodyWW_g" "TOTAL_Liver_WW_mg"
[16] "LiverforLipids_WW_mg" "MuscleWWforLipids_mg" "DissectionComments"
[19] "SL_diff_mm" "WWT_diff_g" # Merge cod_counts_data_long with df_C to get treatment info
cod_countsdata_plus_sampleinfo <- left_join(cod_counts_data_long, cod_sample_info, by = "sample_name")2 Preliminary PCA visualization (liver tissue)
2.1 DESeq object
# Filter data
infosub_L <- cod_sample_info %>% filter(tissue_type == "Liver")
countsub_L <- subset(cod_counts_data, select=row.names(infosub_L))
# Calculate DESeq object
dds_L <- DESeqDataSetFromMatrix(countData = countsub_L,
colData = infosub_L,
design = ~ temp_treatment)
# Run differential expression analysis
# (Note that this DESeq() function runs all necessary steps, including data normalization,
# estimating size factors, estimating dispersions, gene-wise dispersion estimates, mean-dispersion
# relationship, final dispersion estimates, fitting model, and testing)
dds_L <- DESeq(dds_L)
resultsNames(dds_L) # lists the coefficients[1] "Intercept" "temp_treatment_5_vs_0" "temp_treatment_9_vs_0"
[4] "temp_treatment_16_vs_0"plotDispEsts(dds_L)
2.2 PCA visualization
# Generate PCAs
# top 500 most variable genes
pca_L_500<- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE)
percentVar_L_500 <- round(100*attr(pca_L_500, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_500 <- subset(pca_L_500, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_500 <- merge(pca_L_500, cod_sample_info, by.x = "name", by.y = "row.names")
# top 1000 most variable genes
pca_L_1000 <- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE, ntop=1000)
percentVar_L_1000 <- round(100*attr(pca_L_1000, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_1000 <- subset(pca_L_1000, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_1000 <- merge(pca_L_1000, cod_sample_info, by.x = "name", by.y = "row.names")
# all genes
pca_L_all <- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE, ntop=nrow(assay(vst(dds_L))))
percentVar_L_all <- round(100*attr(pca_L_all, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_all <- subset(pca_L_all, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_all <- merge(pca_L_all, cod_sample_info, by.x = "name", by.y = "row.names")
# Assign specific colors to each temperature treatment level
temp_colors <- c(
"0" = "darkblue",
"5" = "royalblue1",
"9" = "green",
"16" = "orangered")
# Plot PCAs
p.L.500 <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment)) +
geom_point(size=4, alpha = 5/10) +
ggtitle("Liver, top 500 most variable genes") +
xlab(paste0("PC1: ",percentVar_L_500[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_L_500[2],"% variance")) +
coord_fixed() +
scale_color_manual(values=temp_colors)+
stat_ellipse()
p.L.500.SLdiff <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, top 500 most variable genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
p.L.500.WWTdiff <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, top 500 most variable genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
p.L.1000 <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment)) +
geom_point(size=4, alpha = 5/10) +
ggtitle("Liver, top 1000 most variable genes") +
xlab(paste0("PC1: ",percentVar_L_1000[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_L_1000[2],"% variance")) +
coord_fixed() +
scale_color_manual(values=temp_colors)+
stat_ellipse()
p.L.1000.SLdiff <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, top 1000 most variable genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
p.L.1000.WWTdiff <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, top 1000 most variable genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
p.L.all <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment)) +
geom_point(size=4, alpha = 5/10) +
ggtitle("Liver, all genes") +
xlab(paste0("PC1: ",percentVar_L_all[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_L_all[2],"% variance")) +
coord_fixed() +
scale_color_manual(values=temp_colors)+
stat_ellipse()
p.L.all.SLdiff <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, all genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
p.L.all.WWTdiff <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) +
geom_point(alpha = 0.5) +
ggtitle("Liver, all genes") +
xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) +
coord_fixed() +
scale_color_manual(values = temp_colors) +
scale_size_continuous() + # Add this line to control the size of points
stat_ellipse()
# View PCAs
p.L.500
p.L.500.SLdiff
p.L.500.WWTdiff
p.L.1000
p.L.1000.SLdiff
p.L.1000.WWTdiff
p.L.all
p.L.all.SLdiff
p.L.all.WWTdiff
# Export PCAs as pngs
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_500.png",
plot = p.L.500,
res = 600,
width = 6000,
height = 4000)
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_1000.png",
plot = p.L.1000,
res = 600,
width = 6000,
height = 4000)
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_all.png",
plot = p.L.all,
res = 600,
width = 6000,
height = 4000)3 Liver tissue, 9C v. 16C
The 9*C temperature treatment is effectively our “control,” as it represents the ambient temperature that wild juvenile Pacific cod would experience.
# liver tissue, temperatures 9 vs. 16
# Filter data
infosub_L.9.16 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "16"))
countsub_L.9.16 <- subset(cod_counts_data, select=row.names(infosub_L.9.16))
# Calculate DESeq object
dds_L.9.16 <- DESeqDataSetFromMatrix(countData = countsub_L.9.16,
colData = infosub_L.9.16,
design = ~ temp_treatment)
dds_L.9.16 <- DESeq(dds_L.9.16)
resultsNames(dds_L.9.16) # lists the coefficients[1] "Intercept" "temp_treatment_16_vs_9"plotDispEsts(dds_L.9.16)
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.16) >= 10) >= ncol(countsub_L.9.16)/3
dds_L.9.16<- dds_L.9.16[keep,]
# Generate Contrasts
contrast_list_L.9.16 <- c("temp_treatment", "16", "9") # order is important: factor, treatment group, control
res_table_L.9.16_noshrink <- results(dds_L.9.16, contrast=contrast_list_L.9.16, alpha = 0.05)
res_table_L.9.16_norm <- lfcShrink(dds_L.9.16,
coef=2,
type="normal") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.16_apeglm <- lfcShrink(dds_L.9.16,
coef=2,
type="apeglm") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.16_ashr <- lfcShrink(dds_L.9.16,
coef=2,
type="ashr")# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.16_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.16_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.16_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.16_ashr, xlim=xlim, ylim=ylim, main="ashr")
# Examine results formatting
res_table_L.9.16_norm %>% data.frame() %>% head() baseMean log2FoldChange lfcSE stat pvalue
XM_060040253.1 2327.5126 0.165359455 0.1639051 1.00886280 0.313040436
XM_060042128.1 235.2778 -0.480849032 0.1613357 -2.98063209 0.002876542
XM_060066729.1 1102.9999 0.463245207 0.2728022 1.69785341 0.089535433
XM_060048268.1 87.8240 -0.006211092 0.2620379 -0.02370511 0.981087830
XM_060056224.1 14.5507 0.020946723 0.3001896 0.06977496 0.944372781
XM_060067663.1 249.9426 0.005196508 0.1283411 0.04048782 0.967704216
padj
XM_060040253.1 0.56255857
XM_060042128.1 0.02019223
XM_060066729.1 0.24954575
XM_060048268.1 0.99295424
XM_060056224.1 0.97937829
XM_060067663.1 0.98886396Note that the metric we want to use to identify significantly expressed genes is the padj values, NOT the pvalue. padj are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).
summary(res_table_L.9.16_noshrink)
out of 21414 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 1785, 8.3%
LFC < 0 (down) : 2320, 11%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_norm)
out of 21414 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2315, 11%
LFC < 0 (down) : 2929, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_apeglm)
out of 21414 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2315, 11%
LFC < 0 (down) : 2929, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_ashr)
out of 21414 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2316, 11%
LFC < 0 (down) : 2928, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results3.1 Extracting significantly expressed genes
padj.cutoff <- 0.05
lfc.cutoff <- 0.58
# Convert results table into tibble
res_table_L.9.16_norm_tb <- res_table_L.9.16_norm %>%
data.frame() %>%
rownames_to_column(var="gene") %>%
as_tibble()
# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.16_norm_noLFCcutoff <- res_table_L.9.16_norm_tb %>%
filter(padj < padj.cutoff)
sig_L.9.16_norm <- sig_L.9.16_norm_noLFCcutoff %>%
filter(abs(log2FoldChange) > lfc.cutoff)
head(sig_L.9.16_norm_noLFCcutoff)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060042128.1 235. -0.481 0.161 -2.98 0.00288 0.0202
2 XM_060072015.1 216. 0.765 0.128 5.96 0.00000000245 0.000000192
3 XM_060048303.1 92.0 -1.00 0.317 -3.19 0.00143 0.0119
4 XM_060038104.1 369. 0.634 0.212 2.99 0.00281 0.0199
5 XM_060068449.1 95.9 0.580 0.120 4.85 0.00000123 0.0000384
6 XM_060038440.1 226. 0.971 0.192 5.07 0.000000408 0.0000152 paste("Number of significant DEGs for 9C v 16C:", nrow(sig_L.9.16_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")[1] "Number of significant DEGs for 9C v 16C: 4105 (padj< 0.05 )"head(sig_L.9.16_norm)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060072015.1 216. 0.765 0.128 5.96 0.00000000245 0.000000192
2 XM_060048303.1 92.0 -1.00 0.317 -3.19 0.00143 0.0119
3 XM_060038104.1 369. 0.634 0.212 2.99 0.00281 0.0199
4 XM_060068449.1 95.9 0.580 0.120 4.85 0.00000123 0.0000384
5 XM_060038440.1 226. 0.971 0.192 5.07 0.000000408 0.0000152
6 XM_060056947.1 38.7 -0.584 0.157 -3.72 0.000202 0.00252 paste("Number of significant DEGs for 9C v 16C:", nrow(sig_L.9.16_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")[1] "Number of significant DEGs for 9C v 16C: 2790 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.16_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.16_norm_noLFCcutoff.tab", sep = "\t",
row.names = TRUE, col.names = NA)
write.table(sig_L.9.16_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.16_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)3.2 Heatmap
For DEGs based on adjusted p-value and log fold-change
# Retrieve normalized counts matrix
dds_L.9.16_norm_counts <- counts(dds_L.9.16, normalized=TRUE)
# Extract normalized expression for significant genes
norm_sig_L.9.16 <- dds_L.9.16_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.16_norm_counts) %in% sig_L.9.16_norm$gene)
head(norm_sig_L.9.16) sample_1 sample_10 sample_100 sample_107 sample_108 sample_109
XM_060072015.1 380.28252 277.19091 205.48115 114.72825 182.94008 136.36096
XM_060048303.1 26.41963 16.73331 81.99488 168.55137 128.45949 198.93756
XM_060038104.1 1320.98138 345.57921 321.06429 175.63336 132.47385 346.50627
XM_060068449.1 124.09219 122.95345 81.99488 52.40673 94.05070 71.91640
XM_060038440.1 208.15464 123.68098 193.62646 127.47583 90.03634 239.09867
XM_060056947.1 23.21725 27.64634 37.53982 42.49194 51.61319 50.43487
sample_11 sample_110 sample_12 sample_13 sample_18 sample_19
XM_060072015.1 264.85604 230.05618 253.69608 250.76618 296.20392 202.79083
XM_060048303.1 39.59642 215.56445 24.77167 25.32167 23.61759 29.26878
XM_060038104.1 437.32044 214.65872 289.57229 416.58225 575.67872 277.00809
XM_060068449.1 124.06878 117.74529 124.71255 146.21220 127.92861 70.03601
XM_060038440.1 233.17891 211.03578 232.34119 369.20623 295.21986 249.82994
XM_060056947.1 34.31690 48.00385 35.02202 32.67312 30.50605 30.31409
sample_2 sample_20 sample_21 sample_28 sample_29 sample_3
XM_060072015.1 378.08611 255.72267 258.99186 286.39780 237.48616 224.62210
XM_060048303.1 103.11439 17.41091 28.77687 78.69077 48.44718 14.20535
XM_060038104.1 348.92244 375.42264 259.92015 267.18262 318.23145 213.96809
XM_060068449.1 131.23650 103.37725 122.53378 134.50632 78.84540 133.17516
XM_060038440.1 218.72750 207.84268 241.35442 570.96559 189.03898 614.38139
XM_060056947.1 44.78706 28.29272 25.99201 43.00542 13.29922 29.29853
sample_30 sample_4 sample_5 sample_78 sample_79 sample_80
XM_060072015.1 242.03005 295.29212 304.52826 152.17854 160.56004 144.73819
XM_060048303.1 87.87553 74.05739 77.20435 77.14607 154.42398 114.93915
XM_060038104.1 257.66892 1343.34477 522.41609 283.22118 294.53051 266.06285
XM_060068449.1 109.47205 147.17734 105.51261 68.69170 65.45122 107.48939
XM_060038440.1 192.87933 285.91776 223.03478 136.32661 93.06346 100.03963
XM_060056947.1 19.36240 37.49741 24.87696 36.98784 38.86166 51.08407
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
XM_060072015.1 227.38728 84.49904 88.17124 165.19670 202.95145 135.94380
XM_060048303.1 151.16808 0.00000 0.00000 70.01193 179.07481 172.48784
XM_060038104.1 308.68777 224.52603 490.09470 368.15264 168.33032 339.12863
XM_060068449.1 73.67856 48.28517 74.43027 106.98453 74.01759 43.85284
XM_060038440.1 196.89960 97.77746 182.06789 124.29085 105.05722 238.26710
XM_060056947.1 67.32696 21.72833 53.81881 40.90585 41.78412 46.77636
sample_98 sample_99 sample_RESUB.116 sample_RESUB.36
XM_060072015.1 59.76367 181.06706 213.3480 293.43784
XM_060048303.1 0.00000 243.17727 215.2032 91.16515
XM_060038104.1 106.37933 307.39291 309.8184 280.61774
XM_060068449.1 50.20148 70.53193 95.5428 74.07169
XM_060038440.1 154.19026 154.74917 115.9500 787.72391
XM_060056947.1 111.16042 34.73961 24.1176 35.61139
sample_RESUB.94
XM_060072015.1 179.49963
XM_060048303.1 242.37006
XM_060038104.1 413.66921
XM_060068449.1 78.36025
XM_060038440.1 93.85006
XM_060056947.1 39.18012# Annotate heatmap
annotation <- infosub_L.9.16 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.16 <- pheatmap(norm_sig_L.9.16,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression, Liver, 9*C and 16*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.16_norm_sig.png",
plot = h.L.9.16,
res = 600,
width = 5000,
height = 5000)Note the argument scale="row" was included, so the values plotted in the heat map are Z-scores, rather thn the normalized count value. This vastly improves the color visualization.
For DEGs based on adjusted p-value only
# Extract normalized expression for significant genes
norm_sig_L.9.16_noLFCcutoff <- dds_L.9.16_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.16_norm_counts) %in% sig_L.9.16_norm_noLFCcutoff$gene)
head(norm_sig_L.9.16_noLFCcutoff) sample_1 sample_10 sample_100 sample_107 sample_108 sample_109
XM_060042128.1 208.15464 157.14760 239.06941 526.90009 155.98652 316.6189
XM_060072015.1 380.28252 277.19091 205.48115 114.72825 182.94008 136.3610
XM_060048303.1 26.41963 16.73331 81.99488 168.55137 128.45949 198.9376
XM_060038104.1 1320.98138 345.57921 321.06429 175.63336 132.47385 346.5063
XM_060068449.1 124.09219 122.95345 81.99488 52.40673 94.05070 71.9164
XM_060038440.1 208.15464 123.68098 193.62646 127.47583 90.03634 239.0987
sample_11 sample_110 sample_12 sample_13 sample_18 sample_19
XM_060042128.1 200.62185 267.1912 179.38107 173.98435 180.08411 270.73621
XM_060072015.1 264.85604 230.0562 253.69608 250.76618 296.20392 202.79083
XM_060048303.1 39.59642 215.5644 24.77167 25.32167 23.61759 29.26878
XM_060038104.1 437.32044 214.6587 289.57229 416.58225 575.67872 277.00809
XM_060068449.1 124.06878 117.7453 124.71255 146.21220 127.92861 70.03601
XM_060038440.1 233.17891 211.0358 232.34119 369.20623 295.21986 249.82994
sample_2 sample_20 sample_21 sample_28 sample_29 sample_3
XM_060042128.1 219.7691 186.07905 170.80467 185.74682 161.49059 181.11821
XM_060072015.1 378.0861 255.72267 258.99186 286.39780 237.48616 224.62210
XM_060048303.1 103.1144 17.41091 28.77687 78.69077 48.44718 14.20535
XM_060038104.1 348.9224 375.42264 259.92015 267.18262 318.23145 213.96809
XM_060068449.1 131.2365 103.37725 122.53378 134.50632 78.84540 133.17516
XM_060038440.1 218.7275 207.84268 241.35442 570.96559 189.03898 614.38139
sample_30 sample_4 sample_5 sample_78 sample_79 sample_80
XM_060042128.1 184.68755 178.11271 194.72652 233.55179 215.78451 233.0711
XM_060072015.1 242.03005 295.29212 304.52826 152.17854 160.56004 144.7382
XM_060048303.1 87.87553 74.05739 77.20435 77.14607 154.42398 114.9392
XM_060038104.1 257.66892 1343.34477 522.41609 283.22118 294.53051 266.0629
XM_060068449.1 109.47205 147.17734 105.51261 68.69170 65.45122 107.4894
XM_060038440.1 192.87933 285.91776 223.03478 136.32661 93.06346 100.0396
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
XM_060042128.1 299.79553 310.23220 400.77838 172.27656 179.07481 564.23987
XM_060072015.1 227.38728 84.49904 88.17124 165.19670 202.95145 135.94380
XM_060048303.1 151.16808 0.00000 0.00000 70.01193 179.07481 172.48784
XM_060038104.1 308.68777 224.52603 490.09470 368.15264 168.33032 339.12863
XM_060068449.1 73.67856 48.28517 74.43027 106.98453 74.01759 43.85284
XM_060038440.1 196.89960 97.77746 182.06789 124.29085 105.05722 238.26710
sample_98 sample_99 sample_RESUB.116 sample_RESUB.36
XM_060042128.1 438.66533 142.11658 115.0224 245.00635
XM_060072015.1 59.76367 181.06706 213.3480 293.43784
XM_060048303.1 0.00000 243.17727 215.2032 91.16515
XM_060038104.1 106.37933 307.39291 309.8184 280.61774
XM_060068449.1 50.20148 70.53193 95.5428 74.07169
XM_060038440.1 154.19026 154.74917 115.9500 787.72391
sample_RESUB.94
XM_060042128.1 146.69767
XM_060072015.1 179.49963
XM_060048303.1 242.37006
XM_060038104.1 413.66921
XM_060068449.1 78.36025
XM_060038440.1 93.85006# Annotate heatmap
annotation <- infosub_L.9.16 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.16 <- pheatmap(norm_sig_L.9.16_noLFCcutoff,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 16*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.16_norm_sig_noLFCcutoff.png",
plot = h.L.9.16,
res = 600,
width = 5000,
height = 5000)Note the argument scale="row" was included, so the values plotted in the heat map are Z-scores, rather thn the normalized count value. This vastly improves the color visualization.
3.3 Volcano plot
# Generate plot
v.L.9.16 <-
ggplot(res_table_L.9.16_norm_tb) +
# Plot all
geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
size=.5) +
# Overlay all significantly upregulated in red
geom_point(data = sig_L.9.16_norm[sig_L.9.16_norm$log2FoldChange > 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="upregulated"),
size=.5) +
# Overlay all significantly downregulated in blue
geom_point(data = sig_L.9.16_norm[sig_L.9.16_norm$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 16*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.16
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.16.png",
plot = v.L.9.16,
res = 600,
width = 6000,
height = 4000)3.4 Plot expression of top DEGs across treatments
top15DEGs_L.9.16 <- sig_L.9.16_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
plot_gene_data <- function(geneName) {
gene_data <- cod_countsdata_plus_sampleinfo %>%
filter(gene == geneName)
ggplot(data = gene_data,
aes(x = temp_treatment, y = count, color = factor(temp_treatment))) +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.1), size = 2) +
labs(title = geneName, x = "Sample", y = "Count", color = "Treatment") +
scale_color_manual(values = temp_colors) +
theme_minimal()
}
L.9.16_DEG1 <- plot_gene_data(top15DEGs_L.9.16[1, ]$gene)
L.9.16_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG1.",top15DEGs_L.9.16[1, ]$gene,".png"), plot = L.9.16_DEG1, res = 600, width = 5000, height = 5000)
L.9.16_DEG2 <- plot_gene_data(top15DEGs_L.9.16[2, ]$gene)
L.9.16_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG2.",top15DEGs_L.9.16[2, ]$gene,".png"), plot = L.9.16_DEG2, res = 600, width = 5000, height = 5000)
L.9.16_DEG3 <- plot_gene_data(top15DEGs_L.9.16[3, ]$gene)
L.9.16_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG3.",top15DEGs_L.9.16[3, ]$gene,".png"), plot = L.9.16_DEG3, res = 600, width = 5000, height = 5000)
L.9.16_DEG4 <- plot_gene_data(top15DEGs_L.9.16[4, ]$gene)
L.9.16_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG4.",top15DEGs_L.9.16[4, ]$gene,".png"), plot = L.9.16_DEG4, res = 600, width = 5000, height = 5000)
L.9.16_DEG5 <- plot_gene_data(top15DEGs_L.9.16[5, ]$gene)
L.9.16_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG5.",top15DEGs_L.9.16[5, ]$gene,".png"), plot = L.9.16_DEG5, res = 600, width = 5000, height = 5000)
L.9.16_DEG6 <- plot_gene_data(top15DEGs_L.9.16[6, ]$gene)
L.9.16_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG6.",top15DEGs_L.9.16[6, ]$gene,".png"), plot = L.9.16_DEG6, res = 600, width = 5000, height = 5000)
L.9.16_DEG7 <- plot_gene_data(top15DEGs_L.9.16[7, ]$gene)
L.9.16_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG7.",top15DEGs_L.9.16[7, ]$gene,".png"), plot = L.9.16_DEG7, res = 600, width = 5000, height = 5000)
L.9.16_DEG8 <- plot_gene_data(top15DEGs_L.9.16[8, ]$gene)
L.9.16_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG8.",top15DEGs_L.9.16[8, ]$gene,".png"), plot = L.9.16_DEG8, res = 600, width = 5000, height = 5000)
L.9.16_DEG9 <- plot_gene_data(top15DEGs_L.9.16[9, ]$gene)
L.9.16_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG9.",top15DEGs_L.9.16[9, ]$gene,".png"), plot = L.9.16_DEG9, res = 600, width = 5000, height = 5000)
L.9.16_DEG10 <- plot_gene_data(top15DEGs_L.9.16[10, ]$gene)
L.9.16_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG10.",top15DEGs_L.9.16[10, ]$gene,".png"), plot = L.9.16_DEG10, res = 600, width = 5000, height = 5000)
L.9.16_DEG11 <- plot_gene_data(top15DEGs_L.9.16[11, ]$gene)
L.9.16_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG11.",top15DEGs_L.9.16[11, ]$gene,".png"), plot = L.9.16_DEG11, res = 600, width = 5000, height = 5000)
L.9.16_DEG12 <- plot_gene_data(top15DEGs_L.9.16[12, ]$gene)
L.9.16_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG12.",top15DEGs_L.9.16[12, ]$gene,".png"), plot = L.9.16_DEG12, res = 600, width = 5000, height = 5000)
L.9.16_DEG13 <- plot_gene_data(top15DEGs_L.9.16[13, ]$gene)
L.9.16_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG13.",top15DEGs_L.9.16[13, ]$gene,".png"), plot = L.9.16_DEG13, res = 600, width = 5000, height = 5000)
L.9.16_DEG14 <- plot_gene_data(top15DEGs_L.9.16[14, ]$gene)
L.9.16_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG14.",top15DEGs_L.9.16[14, ]$gene,".png"), plot = L.9.16_DEG14, res = 600, width = 5000, height = 5000)
L.9.16_DEG15 <- plot_gene_data(top15DEGs_L.9.16[15, ]$gene)
L.9.16_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG15.",top15DEGs_L.9.16[15, ]$gene,".png"), plot = L.9.16_DEG15, res = 600, width = 5000, height = 5000)4 Liver tissue, 9C v. 0C
The 9*C temperature treatment is effectively our “control,” as it represents the ambient temperature that wild juvenile Pacific cod would experience.
# liver tissue, temperatures 9 vs. 0
# Filter data
infosub_L.9.0 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "0"))
countsub_L.9.0 <- subset(cod_counts_data, select=row.names(infosub_L.9.0))
# Calculate DESeq object
dds_L.9.0 <- DESeqDataSetFromMatrix(countData = countsub_L.9.0,
colData = infosub_L.9.0,
design = ~ temp_treatment)
dds_L.9.0 <- DESeq(dds_L.9.0)
resultsNames(dds_L.9.0) # lists the coefficients[1] "Intercept" "temp_treatment_9_vs_0"plotDispEsts(dds_L.9.0)
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.0) >= 10) >= ncol(countsub_L.9.0)/3
dds_L.9.0<- dds_L.9.0[keep,]
# Generate Contrasts
contrast_list_L.9.0 <- c("temp_treatment", "0", "9") # order is important: factor, treatment group, control
res_table_L.9.0_noshrink <- results(dds_L.9.0, contrast=contrast_list_L.9.0, alpha = 0.05)
res_table_L.9.0_norm <- lfcShrink(dds_L.9.0,
coef=2,
type="normal") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.0_apeglm <- lfcShrink(dds_L.9.0,
coef=2,
type="apeglm") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.0_ashr <- lfcShrink(dds_L.9.0,
coef=2,
type="ashr")# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.0_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.0_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.0_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.0_ashr, xlim=xlim, ylim=ylim, main="ashr")
# Examine results formatting
res_table_L.9.0_norm %>% data.frame() %>% head() baseMean log2FoldChange lfcSE stat pvalue
XM_060040253.1 3122.82259 -0.7256493 0.1776386 -4.0848913 4.409746e-05
XM_060042128.1 285.77569 0.0649712 0.2031898 0.3197811 7.491343e-01
XM_060066729.1 702.36692 1.0038553 0.2230834 4.5001294 6.791210e-06
XM_060048268.1 164.24708 -0.4572041 0.4019688 -1.1534333 2.487326e-01
XM_060056224.1 13.64937 0.2793657 0.3190532 0.8748907 3.816334e-01
XM_060067663.1 306.31256 -0.3855980 0.1401494 -2.7512012 5.937717e-03
padj
XM_060040253.1 3.599217e-04
XM_060042128.1 8.706335e-01
XM_060066729.1 6.778697e-05
XM_060048268.1 4.511504e-01
XM_060056224.1 5.962076e-01
XM_060067663.1 2.490951e-02Note that the metric we want to use to identify significantly expressed genes is the padj values, NOT the pvalue. padj are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).
summary(res_table_L.9.0_noshrink)
out of 21670 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 2955, 14%
LFC < 0 (down) : 3025, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_norm)
out of 21670 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3646, 17%
LFC < 0 (down) : 3441, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_apeglm)
out of 21670 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3646, 17%
LFC < 0 (down) : 3441, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_ashr)
out of 21670 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3645, 17%
LFC < 0 (down) : 3442, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results4.1 Extracting significantly expressed genes
padj.cutoff <- 0.05
lfc.cutoff <- 0.58
# Convert results table into tibble
res_table_L.9.0_norm_tb <- res_table_L.9.0_norm %>%
data.frame() %>%
rownames_to_column(var="gene") %>%
as_tibble()
# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.0_norm_noLFCcutoff <- res_table_L.9.0_norm_tb %>%
filter(padj < padj.cutoff)
sig_L.9.0_norm <- sig_L.9.0_norm_noLFCcutoff %>%
filter(abs(log2FoldChange) > lfc.cutoff)
head(sig_L.9.0_norm_noLFCcutoff)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060040253.1 3123. -0.726 0.178 -4.08 4.41e- 5 3.60e- 4
2 XM_060066729.1 702. 1.00 0.223 4.50 6.79e- 6 6.78e- 5
3 XM_060067663.1 306. -0.386 0.140 -2.75 5.94e- 3 2.49e- 2
4 XM_060068449.1 174. -1.70 0.123 -13.8 2.94e-43 1.68e-40
5 XM_060038440.1 289. -1.42 0.136 -10.4 3.03e-25 3.98e-23
6 XM_060041661.1 23.6 -1.14 0.419 -2.84 4.55e- 3 2.00e- 2paste("Number of significant DEGs for 9C v 0C:", nrow(sig_L.9.0_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")[1] "Number of significant DEGs for 9C v 0C: 5980 (padj< 0.05 )"head(sig_L.9.0_norm)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060040253.1 3123. -0.726 0.178 -4.08 4.41e- 5 3.60e- 4
2 XM_060066729.1 702. 1.00 0.223 4.50 6.79e- 6 6.78e- 5
3 XM_060068449.1 174. -1.70 0.123 -13.8 2.94e-43 1.68e-40
4 XM_060038440.1 289. -1.42 0.136 -10.4 3.03e-25 3.98e-23
5 XM_060041661.1 23.6 -1.14 0.419 -2.84 4.55e- 3 2.00e- 2
6 XM_060071337.1 770. 1.06 0.365 2.90 3.70e- 3 1.69e- 2paste("Number of significant DEGs for 9C v 0C:", nrow(sig_L.9.0_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")[1] "Number of significant DEGs for 9C v 0C: 4703 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.0_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.0_norm_noLFCcutoff.tab", sep = "\t",
row.names = TRUE, col.names = NA)
write.table(sig_L.9.0_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.0_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)4.2 Heatmap
For DEGs based on adjusted p-value and LFC cutoff
# Retrieve normalized counts matrix
dds_L.9.0_norm_counts <- counts(dds_L.9.0, normalized=TRUE)
# Extract normalized expression for significant genes
norm_sig_L.9.0 <- dds_L.9.0_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.0_norm_counts) %in% sig_L.9.0_norm$gene)
head(norm_sig_L.9.0) sample_100 sample_107 sample_108 sample_109 sample_110
XM_060040253.1 3154.38405 2218.39508 1975.71558 2884.64070 2727.0654
XM_060066729.1 1396.49987 460.47822 649.27391 955.58074 814.6801
XM_060068449.1 88.48053 55.49738 97.33174 76.56578 127.7544
XM_060038440.1 208.94196 134.99362 93.17733 254.55637 228.9752
XM_060041661.1 0.00000 0.00000 36.20266 24.85902 0.0000
XM_060071337.1 1300.55713 580.47255 794.67803 1603.90398 1375.8168
sample_37 sample_38 sample_39 sample_40 sample_47 sample_48
XM_060040253.1 2925.553844 3678.59851 3415.75582 4110.9561 4871.97351 1484.6904
XM_060066729.1 368.343275 282.50675 350.43721 321.2764 416.83430 370.5844
XM_060068449.1 360.269997 204.36658 307.13606 278.6699 255.62274 295.2910
XM_060038440.1 443.021089 435.35234 537.73986 365.0345 481.85332 530.5827
XM_060041661.1 40.366386 19.74971 62.43422 0.0000 103.31790 0.0000
XM_060071337.1 6.054958 1233.92765 118.82641 673.6441 73.92574 119.9987
sample_49 sample_50 sample_57 sample_58 sample_59
XM_060040253.1 2887.89685 4224.67414 3721.09905 3249.34617 3302.17448
XM_060066729.1 703.35219 307.57799 447.33678 515.26592 492.03549
XM_060068449.1 363.21186 260.83820 246.88656 250.49851 189.08370
XM_060038440.1 543.16983 317.37827 340.53319 514.47320 351.00621
XM_060041661.1 41.52876 39.95498 50.30604 42.01399 48.05442
XM_060071337.1 107.44743 1076.52296 129.24782 89.57700 688.43182
sample_60 sample_67 sample_68 sample_69 sample_70
XM_060040253.1 4580.17338 4591.08604 5428.83855 3998.64605 3333.77677
XM_060066729.1 436.55399 343.63446 527.80375 341.95318 697.03470
XM_060068449.1 269.61680 267.12716 249.35089 261.58630 171.71846
XM_060038440.1 313.33844 393.55871 472.24546 293.89064 443.01331
XM_060041661.1 71.54451 35.01181 98.54982 14.18239 46.73994
XM_060071337.1 490.21237 334.55732 868.42897 647.66247 227.60317
sample_78 sample_79 sample_80 sample_83 sample_88 sample_90
XM_060040253.1 2074.17667 2948.81439 2879.1082 1578.5019 2220.99585 2883.29832
XM_060066729.1 551.99863 1000.15695 848.5911 524.3700 435.48938 184.14342
XM_060068449.1 72.48467 70.34071 114.1248 78.1837 51.23405 78.74554
XM_060038440.1 143.85419 100.01569 106.2151 208.9392 103.74894 192.62371
XM_060041661.1 16.72723 0.00000 0.0000 0.0000 0.00000 0.00000
XM_060071337.1 4413.75873 242.89526 446.3296 373.3946 190.84682 38.76704
sample_91 sample_92 sample_97 sample_98 sample_99
XM_060040253.1 2021.36914 2841.31441 647.84286 862.39177 1288.42338
XM_060066729.1 1235.18772 795.05260 779.86771 297.55038 2481.52972
XM_060068449.1 114.35366 79.89183 46.05518 52.95388 73.40507
XM_060038440.1 132.85205 113.39486 250.23314 162.64406 161.05292
XM_060041661.1 21.02089 0.00000 0.00000 0.00000 0.00000
XM_060071337.1 2751.21457 403.32490 1097.64844 148.77519 2643.67824
sample_RESUB.116 sample_RESUB.76 sample_RESUB.94
XM_060040253.1 3133.42480 7466.3389 3687.34971
XM_060066729.1 1924.22389 647.2300 1678.40717
XM_060068449.1 102.00466 399.8247 85.10791
XM_060038440.1 123.79207 518.0049 101.93157
XM_060041661.1 14.85505 0.0000 0.00000
XM_060071337.1 875.45750 175.6136 613.56866# Annotate heatmap
annotation <- infosub_L.9.0 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.0 <- pheatmap(norm_sig_L.9.0,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression, Liver, 9*C and 0*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.0_norm_sig.png",
plot = h.L.9.0,
res = 600,
width = 5000,
height = 5000)Note the argument scale="row" was included, so the values plotted in the heat map are Z-scores, rather thn the normalized count value. This vastly improves the color visualization.
For DEGs based on adjusted p-value only
# Extract normalized expression for significant genes
norm_sig_L.9.0_noLFCcutoff <- dds_L.9.0_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.0_norm_counts) %in% sig_L.9.0_norm_noLFCcutoff$gene)
head(norm_sig_L.9.0_noLFCcutoff) sample_100 sample_107 sample_108 sample_109 sample_110
XM_060040253.1 3154.38405 2218.39508 1975.71558 2884.64070 2727.0654
XM_060066729.1 1396.49987 460.47822 649.27391 955.58074 814.6801
XM_060067663.1 356.05416 113.99461 283.09292 256.54509 294.8179
XM_060068449.1 88.48053 55.49738 97.33174 76.56578 127.7544
XM_060038440.1 208.94196 134.99362 93.17733 254.55637 228.9752
XM_060041661.1 0.00000 0.00000 36.20266 24.85902 0.0000
sample_37 sample_38 sample_39 sample_40 sample_47 sample_48
XM_060040253.1 2925.55384 3678.59851 3415.75582 4110.9561 4871.9735 1484.6904
XM_060066729.1 368.34327 282.50675 350.43721 321.2764 416.8343 370.5844
XM_060067663.1 250.27159 249.87679 391.72436 392.6712 401.6929 142.3515
XM_060068449.1 360.27000 204.36658 307.13606 278.6699 255.6227 295.2910
XM_060038440.1 443.02109 435.35234 537.73986 365.0345 481.8533 530.5827
XM_060041661.1 40.36639 19.74971 62.43422 0.0000 103.3179 0.0000
sample_49 sample_50 sample_57 sample_58 sample_59
XM_060040253.1 2887.89685 4224.67414 3721.09905 3249.34617 3302.17448
XM_060066729.1 703.35219 307.57799 447.33678 515.26592 492.03549
XM_060067663.1 341.45870 430.45841 321.95865 493.86257 267.43330
XM_060068449.1 363.21186 260.83820 246.88656 250.49851 189.08370
XM_060038440.1 543.16983 317.37827 340.53319 514.47320 351.00621
XM_060041661.1 41.52876 39.95498 50.30604 42.01399 48.05442
sample_60 sample_67 sample_68 sample_69 sample_70
XM_060040253.1 4580.17338 4591.08604 5428.83855 3998.64605 3333.77677
XM_060066729.1 436.55399 343.63446 527.80375 341.95318 697.03470
XM_060067663.1 431.91684 359.84363 423.30125 364.01467 338.35650
XM_060068449.1 269.61680 267.12716 249.35089 261.58630 171.71846
XM_060038440.1 313.33844 393.55871 472.24546 293.89064 443.01331
XM_060041661.1 71.54451 35.01181 98.54982 14.18239 46.73994
sample_78 sample_79 sample_80 sample_83 sample_88 sample_90
XM_060040253.1 2074.17667 2948.81439 2879.1082 1578.5019 2220.99585 2883.29832
XM_060066729.1 551.99863 1000.15695 848.5911 524.3700 435.48938 184.14342
XM_060067663.1 307.78105 268.17395 270.0576 233.2031 192.12767 212.00723
XM_060068449.1 72.48467 70.34071 114.1248 78.1837 51.23405 78.74554
XM_060038440.1 143.85419 100.01569 106.2151 208.9392 103.74894 192.62371
XM_060041661.1 16.72723 0.00000 0.0000 0.0000 0.00000 0.00000
sample_91 sample_92 sample_97 sample_98 sample_99
XM_060040253.1 2021.36914 2841.31441 647.84286 862.39177 1288.42338
XM_060066729.1 1235.18772 795.05260 779.86771 297.55038 2481.52972
XM_060067663.1 369.12689 282.19857 216.45934 127.34148 341.82661
XM_060068449.1 114.35366 79.89183 46.05518 52.95388 73.40507
XM_060038440.1 132.85205 113.39486 250.23314 162.64406 161.05292
XM_060041661.1 21.02089 0.00000 0.00000 0.00000 0.00000
sample_RESUB.116 sample_RESUB.76 sample_RESUB.94
XM_060040253.1 3133.42480 7466.3389 3687.34971
XM_060066729.1 1924.22389 647.2300 1678.40717
XM_060067663.1 374.34721 343.4958 277.09552
XM_060068449.1 102.00466 399.8247 85.10791
XM_060038440.1 123.79207 518.0049 101.93157
XM_060041661.1 14.85505 0.0000 0.00000# Annotate heatmap
annotation <- infosub_L.9.0 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.0 <- pheatmap(norm_sig_L.9.0_noLFCcutoff,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 0*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.0_norm_sig_noLFCcutoff.png",
plot = h.L.9.0,
res = 600,
width = 5000,
height = 5000)4.3 Volcano plot
# Generate plot
v.L.9.0 <-
ggplot(res_table_L.9.0_norm_tb) +
# Plot all
geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
size=.5) +
# Overlay all significantly upregulated in red
geom_point(data = sig_L.9.0_norm[sig_L.9.0_norm$log2FoldChange > 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="upregulated"),
size=.5) +
# Overlay all significantly downregulated in blue
geom_point(data = sig_L.9.0_norm[sig_L.9.0_norm$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 0*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.0
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.0.png",
plot = v.L.9.0,
res = 600,
width = 6000,
height = 4000)4.4 Plot expression of top DEGs across treatments
top15DEGs_L.9.0 <- sig_L.9.0_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
L.9.0_DEG1 <- plot_gene_data(top15DEGs_L.9.0[1, ]$gene)
L.9.0_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG1.",top15DEGs_L.9.0[1, ]$gene,".png"), plot = L.9.0_DEG1, res = 600, width = 5000, height = 5000)
L.9.0_DEG2 <- plot_gene_data(top15DEGs_L.9.0[2, ]$gene)
L.9.0_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG2.",top15DEGs_L.9.0[2, ]$gene,".png"), plot = L.9.0_DEG2, res = 600, width = 5000, height = 5000)
L.9.0_DEG3 <- plot_gene_data(top15DEGs_L.9.0[3, ]$gene)
L.9.0_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG3.",top15DEGs_L.9.0[3, ]$gene,".png"), plot = L.9.0_DEG3, res = 600, width = 5000, height = 5000)
L.9.0_DEG4 <- plot_gene_data(top15DEGs_L.9.0[4, ]$gene)
L.9.0_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG4.",top15DEGs_L.9.0[4, ]$gene,".png"), plot = L.9.0_DEG4, res = 600, width = 5000, height = 5000)
L.9.0_DEG5 <- plot_gene_data(top15DEGs_L.9.0[5, ]$gene)
L.9.0_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG5.",top15DEGs_L.9.0[5, ]$gene,".png"), plot = L.9.0_DEG5, res = 600, width = 5000, height = 5000)
L.9.0_DEG6 <- plot_gene_data(top15DEGs_L.9.0[6, ]$gene)
L.9.0_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG6.",top15DEGs_L.9.0[6, ]$gene,".png"), plot = L.9.0_DEG6, res = 600, width = 5000, height = 5000)
L.9.0_DEG7 <- plot_gene_data(top15DEGs_L.9.0[7, ]$gene)
L.9.0_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG7.",top15DEGs_L.9.0[7, ]$gene,".png"), plot = L.9.0_DEG7, res = 600, width = 5000, height = 5000)
L.9.0_DEG8 <- plot_gene_data(top15DEGs_L.9.0[8, ]$gene)
L.9.0_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG8.",top15DEGs_L.9.0[8, ]$gene,".png"), plot = L.9.0_DEG8, res = 600, width = 5000, height = 5000)
L.9.0_DEG9 <- plot_gene_data(top15DEGs_L.9.0[9, ]$gene)
L.9.0_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG9.",top15DEGs_L.9.0[9, ]$gene,".png"), plot = L.9.0_DEG9, res = 600, width = 5000, height = 5000)
L.9.0_DEG10 <- plot_gene_data(top15DEGs_L.9.0[10, ]$gene)
L.9.0_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG10.",top15DEGs_L.9.0[10, ]$gene,".png"), plot = L.9.0_DEG10, res = 600, width = 5000, height = 5000)
L.9.0_DEG11 <- plot_gene_data(top15DEGs_L.9.0[11, ]$gene)
L.9.0_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG11.",top15DEGs_L.9.0[11, ]$gene,".png"), plot = L.9.0_DEG11, res = 600, width = 5000, height = 5000)
L.9.0_DEG12 <- plot_gene_data(top15DEGs_L.9.0[12, ]$gene)
L.9.0_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG12.",top15DEGs_L.9.0[12, ]$gene,".png"), plot = L.9.0_DEG12, res = 600, width = 5000, height = 5000)
L.9.0_DEG13 <- plot_gene_data(top15DEGs_L.9.0[13, ]$gene)
L.9.0_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG13.",top15DEGs_L.9.0[13, ]$gene,".png"), plot = L.9.0_DEG13, res = 600, width = 5000, height = 5000)
L.9.0_DEG14 <- plot_gene_data(top15DEGs_L.9.0[14, ]$gene)
L.9.0_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG14.",top15DEGs_L.9.0[14, ]$gene,".png"), plot = L.9.0_DEG14, res = 600, width = 5000, height = 5000)
L.9.0_DEG15 <- plot_gene_data(top15DEGs_L.9.0[15, ]$gene)
L.9.0_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG15.",top15DEGs_L.9.0[15, ]$gene,".png"), plot = L.9.0_DEG15, res = 600, width = 5000, height = 5000)5 Liver tissue, 9C v. 5C
The 9*C temperature treatment is effectively our “control,” as it represents the ambient temperature that wild juvenile Pacific cod would experience.
# liver tissue, temperatures 9 vs. 5
# Filter data
infosub_L.9.5 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "5"))
countsub_L.9.5 <- subset(cod_counts_data, select=row.names(infosub_L.9.5))
# Calculate DESeq object
dds_L.9.5 <- DESeqDataSetFromMatrix(countData = countsub_L.9.5,
colData = infosub_L.9.5,
design = ~ temp_treatment)
dds_L.9.5 <- DESeq(dds_L.9.5)
resultsNames(dds_L.9.5) # lists the coefficients[1] "Intercept" "temp_treatment_9_vs_5"plotDispEsts(dds_L.9.5)
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.5) >= 10) >= ncol(countsub_L.9.5)/3
dds_L.9.5<- dds_L.9.5[keep,]
# Generate Contrasts
contrast_list_L.9.5 <- c("temp_treatment", "5", "9") # order is important: factor, treatment group, control
res_table_L.9.5_noshrink <- results(dds_L.9.5, contrast=contrast_list_L.9.5, alpha = 0.05)
res_table_L.9.5_norm <- lfcShrink(dds_L.9.5,
coef=2,
type="normal") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.5_apeglm <- lfcShrink(dds_L.9.5,
coef=2,
type="apeglm") # lfcThreshold = 0.585) # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.5_ashr <- lfcShrink(dds_L.9.5,
coef=2,
type="ashr")# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.5_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.5_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.5_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.5_ashr, xlim=xlim, ylim=ylim, main="ashr")
# Examine results formatting
res_table_L.9.5_norm %>% data.frame() %>% head() baseMean log2FoldChange lfcSE stat pvalue
XM_060040253.1 2306.40103 -0.35416518 0.1643030 -2.1554052 0.03113014
XM_060042128.1 236.38023 0.19703304 0.1788232 1.1022592 0.27034900
XM_060066729.1 762.06956 0.16619727 0.2334513 0.7120654 0.47642427
XM_060048268.1 92.04034 -0.05113841 0.1973887 -0.2584982 0.79602241
XM_060056224.1 11.59796 0.30174249 0.2626236 1.1522189 0.24923112
XM_060067663.1 250.22583 -0.24689780 0.1284776 -1.9214078 0.05468032
padj
XM_060040253.1 0.2135309
XM_060042128.1 0.6701601
XM_060066729.1 0.8338922
XM_060048268.1 0.9584280
XM_060056224.1 0.6468082
XM_060067663.1 0.2991018Note that the metric we want to use to identify significantly expressed genes is the padj values, NOT the pvalue. padj are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).
summary(res_table_L.9.5_noshrink)
out of 21090 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 730, 3.5%
LFC < 0 (down) : 697, 3.3%
outliers [1] : 0, 0%
low counts [2] : 409, 1.9%
(mean count < 8)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.5_norm)
out of 21090 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1015, 4.8%
LFC < 0 (down) : 976, 4.6%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 4)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.5_apeglm)
out of 21090 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1015, 4.8%
LFC < 0 (down) : 976, 4.6%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 4)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.5_ashr)
out of 21090 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1017, 4.8%
LFC < 0 (down) : 974, 4.6%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 4)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results5.1 Extracting significantly expressed genes
padj.cutoff <- 0.05
lfc.cutoff <- 0.58
# Convert results table into tibble
res_table_L.9.5_norm_tb <- res_table_L.9.5_norm %>%
data.frame() %>%
rownames_to_column(var="gene") %>%
as_tibble()
# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.5_norm_noLFCcutoff <- res_table_L.9.5_norm_tb %>%
filter(padj < padj.cutoff)
sig_L.9.5_norm <- sig_L.9.5_norm_noLFCcutoff %>%
filter(abs(log2FoldChange) > lfc.cutoff)
head(sig_L.9.5_norm)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060038104.1 342. -0.692 0.148 -4.66 3.15e- 6 0.000283
2 XM_060064921.1 84.2 -0.804 0.206 -3.91 9.23e- 5 0.00375
3 XM_060038440.1 214. -1.02 0.177 -5.75 8.73e- 9 0.00000242
4 XM_060039114.1 115. -0.939 0.182 -5.17 2.35e- 7 0.0000346
5 XM_060057878.1 81.9 0.752 0.234 3.22 1.28e- 3 0.0254
6 XM_060051825.1 118. 0.965 0.151 6.37 1.85e-10 0.000000106paste("Number of significant DEGs for 9C v 5C:", nrow(sig_L.9.5_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")[1] "Number of significant DEGs for 9C v 5C: 1422 (padj< 0.05 )"head(sig_L.9.5_norm)# A tibble: 6 × 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XM_060038104.1 342. -0.692 0.148 -4.66 3.15e- 6 0.000283
2 XM_060064921.1 84.2 -0.804 0.206 -3.91 9.23e- 5 0.00375
3 XM_060038440.1 214. -1.02 0.177 -5.75 8.73e- 9 0.00000242
4 XM_060039114.1 115. -0.939 0.182 -5.17 2.35e- 7 0.0000346
5 XM_060057878.1 81.9 0.752 0.234 3.22 1.28e- 3 0.0254
6 XM_060051825.1 118. 0.965 0.151 6.37 1.85e-10 0.000000106paste("Number of significant DEGs for 9C v 5C:", nrow(sig_L.9.5_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")[1] "Number of significant DEGs for 9C v 5C: 989 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.5_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.5_norm_noLFCcutoff.tab", sep = "\t",
row.names = TRUE, col.names = NA)
write.table(sig_L.9.5_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.5_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)5.2 Heatmap
For DEGs based on adjusted p-value and LFC cutoff
# Retrieve normalized counts matrix
dds_L.9.5_norm_counts <- counts(dds_L.9.5, normalized=TRUE)
# Extract normalized expression for significant genes
norm_sig_L.9.5 <- dds_L.9.5_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.5_norm_counts) %in% sig_L.9.5_norm$gene)
head(norm_sig_L.9.5) sample_100 sample_107 sample_108 sample_109 sample_110
XM_060038104.1 295.71554 159.60607 121.97567 313.89296 194.35918
XM_060064921.1 62.78268 46.33725 41.71462 91.37585 113.17117
XM_060038440.1 178.33922 115.84312 82.90121 216.59461 191.07886
XM_060039114.1 76.43109 97.82308 83.95728 63.45545 79.54785
XM_060057878.1 36.39576 203.36903 110.88697 118.45017 65.60648
XM_060051825.1 104.63781 126.14028 84.48531 117.60410 127.93263
sample_117 sample_118 sample_119 sample_120 sample_121
XM_060038104.1 536.85118 505.8675 435.66389 473.00778 480.92812
XM_060064921.1 63.41247 120.1435 239.49696 66.00108 335.92919
XM_060038440.1 379.24349 411.0174 402.57549 411.28454 86.45899
XM_060039114.1 96.04218 119.4409 274.16100 199.83662 106.27250
XM_060057878.1 32.01406 67.4490 106.35556 32.38942 71.14854
XM_060051825.1 81.88212 79.3931 73.26716 72.72342 37.82581
sample_127 sample_128 sample_131 sample_137 sample_138
XM_060038104.1 278.35595 395.18488 326.85770 409.26949 792.68997
XM_060064921.1 86.34934 70.89194 60.24436 132.79489 90.51477
XM_060038440.1 315.89915 461.55180 174.32411 304.77515 521.14565
XM_060039114.1 80.44970 312.22622 129.46129 124.08703 57.60031
XM_060057878.1 23.59858 40.72516 44.86282 43.53931 126.17211
XM_060051825.1 75.08638 66.36693 80.75308 54.42413 90.51477
sample_139 sample_140 sample_147 sample_148 sample_150 sample_78
XM_060038104.1 359.46526 406.55133 377.09249 368.93588 389.59040 258.64896
XM_060064921.1 173.53495 128.30327 82.65041 42.60554 37.54433 39.56943
XM_060038440.1 382.48520 148.39896 189.40719 191.49083 237.78073 124.49894
XM_060039114.1 148.74424 205.59440 167.02270 66.95156 247.03078 97.47591
XM_060057878.1 37.18606 54.10379 53.37839 77.25180 25.02955 94.58059
XM_060051825.1 115.09971 75.74530 63.70969 83.80650 77.80926 155.38240
sample_79 sample_80 sample_83 sample_88 sample_90 sample_91
XM_060038104.1 270.38098 245.58561 280.54998 208.73175 453.67964 339.49663
XM_060064921.1 73.22818 60.90523 47.33559 41.52191 47.69996 60.20987
XM_060038440.1 85.43288 92.34019 178.95163 90.89931 168.53987 114.61638
XM_060039114.1 66.65642 105.11064 66.96255 84.16603 65.71995 82.69790
XM_060057878.1 124.86344 102.16361 58.88086 221.07611 91.15993 62.38613
XM_060051825.1 138.94578 160.12182 177.79710 359.10840 277.71978 123.32143
sample_92 sample_97 sample_98 sample_99 sample_RESUB.116
XM_060038104.1 154.77261 311.91541 100.45049 283.30844 282.496273
XM_060064921.1 81.22818 59.15637 32.73106 32.01773 62.588995
XM_060038440.1 96.59567 219.14746 145.59678 142.62445 105.724653
XM_060039114.1 98.79103 37.64496 20.31583 73.73781 85.425520
XM_060057878.1 185.50759 79.32331 215.57353 118.36860 8.457972
XM_060051825.1 154.77261 141.16861 223.47413 110.60672 118.411612
sample_RESUB.156 sample_RESUB.94
XM_060038104.1 440.38384 382.77616
XM_060064921.1 87.73931 52.27340
XM_060038440.1 214.28639 86.84129
XM_060039114.1 206.69356 75.03762
XM_060057878.1 30.37130 21.07798
XM_060051825.1 76.77190 111.29175# Annotate heatmap
annotation <- infosub_L.9.5 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.5 <- pheatmap(norm_sig_L.9.5,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression, Liver, 9*C and 5*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.5_norm_sig.png",
plot = h.L.9.5,
res = 600,
width = 5000,
height = 5000)Note the argument scale="row" was included, so the values plotted in the heat map are Z-scores, rather thn the normalized count value. This vastly improves the color visualization.
For DEGs based on adjusted p-value only
# Extract normalized expression for significant genes
norm_sig_L.9.5_noLFCcutoff <- dds_L.9.5_norm_counts %>%
data.frame() %>%
filter(row.names(dds_L.9.5_norm_counts) %in% sig_L.9.5_norm_noLFCcutoff$gene)
head(norm_sig_L.9.5_noLFCcutoff) sample_100 sample_107 sample_108 sample_109 sample_110
XM_060038104.1 295.71554 159.60607 121.97567 313.89296 194.35918
XM_060064921.1 62.78268 46.33725 41.71462 91.37585 113.17117
XM_060068449.1 75.52120 47.62439 86.59744 65.14760 106.61052
XM_060038440.1 178.33922 115.84312 82.90121 216.59461 191.07886
XM_060072995.1 223.83392 200.79473 214.90951 197.98101 270.62671
XM_060039114.1 76.43109 97.82308 83.95728 63.45545 79.54785
sample_117 sample_118 sample_119 sample_120 sample_121
XM_060038104.1 536.85118 505.8675 435.6639 473.00778 480.92812
XM_060064921.1 63.41247 120.1435 239.4970 66.00108 335.92919
XM_060068449.1 110.20225 135.6006 215.0746 137.50226 124.28479
XM_060038440.1 379.24349 411.0174 402.5755 411.28454 86.45899
XM_060072995.1 226.56105 346.3787 567.2297 526.17532 228.75607
XM_060039114.1 96.04218 119.4409 274.1610 199.83662 106.27250
sample_127 sample_128 sample_131 sample_137 sample_138
XM_060038104.1 278.35595 395.18488 326.85770 409.26949 792.68997
XM_060064921.1 86.34934 70.89194 60.24436 132.79489 90.51477
XM_060068449.1 169.48070 122.17548 88.44385 87.07862 35.65733
XM_060038440.1 315.89915 461.55180 174.32411 304.77515 521.14565
XM_060072995.1 264.94767 404.23491 329.42129 283.00550 181.02954
XM_060039114.1 80.44970 312.22622 129.46129 124.08703 57.60031
sample_139 sample_140 sample_147 sample_148 sample_150 sample_78
XM_060038104.1 359.46526 406.55133 377.09249 368.93588 389.59040 258.64896
XM_060064921.1 173.53495 128.30327 82.65041 42.60554 37.54433 39.56943
XM_060068449.1 51.35218 80.38277 63.70969 85.21108 84.33870 62.73202
XM_060038440.1 382.48520 148.39896 189.40719 191.49083 237.78073 124.49894
XM_060072995.1 304.57155 471.47588 223.84486 169.48577 347.14899 200.74248
XM_060039114.1 148.74424 205.59440 167.02270 66.95156 247.03078 97.47591
sample_79 sample_80 sample_83 sample_88 sample_90 sample_91
XM_060038104.1 270.38098 245.58561 280.54998 208.73175 453.67964 339.49663
XM_060064921.1 73.22818 60.90523 47.33559 41.52191 47.69996 60.20987
XM_060068449.1 60.08466 99.21659 66.96255 44.88855 68.89995 98.65714
XM_060038440.1 85.43288 92.34019 178.95163 90.89931 168.53987 114.61638
XM_060072995.1 225.31748 272.10885 162.78826 234.54267 213.05983 195.13802
XM_060039114.1 66.65642 105.11064 66.96255 84.16603 65.71995 82.69790
sample_92 sample_97 sample_98 sample_99 sample_RESUB.116
XM_060038104.1 154.77261 311.91541 100.45049 283.30844 282.49627
XM_060064921.1 81.22818 59.15637 32.73106 32.01773 62.58899
XM_060068449.1 68.05604 40.33389 47.40360 65.00570 87.11711
XM_060038440.1 96.59567 219.14746 145.59678 142.62445 105.72465
XM_060072995.1 295.27540 317.29326 117.38035 186.28500 223.29047
XM_060039114.1 98.79103 37.64496 20.31583 73.73781 85.42552
sample_RESUB.156 sample_RESUB.94
XM_060038104.1 440.38384 382.77616
XM_060064921.1 87.73931 52.27340
XM_060068449.1 128.23437 72.50826
XM_060038440.1 214.28639 86.84129
XM_060072995.1 396.51419 225.95597
XM_060039114.1 206.69356 75.03762# Annotate heatmap
annotation <- infosub_L.9.5 %>%
dplyr::select(temp_treatment)
# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))
# Run pheatmap
h.L.9.5 <- pheatmap(norm_sig_L.9.5_noLFCcutoff,
color = heat_colors,
cluster_rows = T,
show_rownames = F,
annotation = annotation,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10,
height = 30,
main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 5*C")
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.5_norm_sig_noLFCcutoff.png",
plot = h.L.9.5,
res = 600,
width = 5000,
height = 5000)5.3 Volcano plot
# Generate plot
v.L.9.5 <-
ggplot(res_table_L.9.5_norm_tb) +
# Plot all
geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
size=.5) +
# Overlay all significantly upregulated in red
geom_point(data = sig_L.9.5_norm[sig_L.9.5_norm$log2FoldChange > 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="upregulated"),
size=.5) +
# Overlay all significantly downregulated in blue
geom_point(data = sig_L.9.5_norm[sig_L.9.5_norm$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 5*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.5
# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.5.png",
plot = v.L.9.0,
res = 600,
width = 6000,
height = 4000)5.4 Plot expression of top DEGs across treatments
top15DEGs_L.9.5 <- sig_L.9.5_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
L.9.5_DEG1 <- plot_gene_data(top15DEGs_L.9.5[1, ]$gene)
L.9.5_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG1.",top15DEGs_L.9.5[1, ]$gene,".png"), plot = L.9.5_DEG1, res = 600, width = 5000, height = 5000)
L.9.5_DEG2 <- plot_gene_data(top15DEGs_L.9.5[2, ]$gene)
L.9.5_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG2.",top15DEGs_L.9.5[2, ]$gene,".png"), plot = L.9.5_DEG2, res = 600, width = 5000, height = 5000)
L.9.5_DEG3 <- plot_gene_data(top15DEGs_L.9.5[3, ]$gene)
L.9.5_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG3.",top15DEGs_L.9.5[3, ]$gene,".png"), plot = L.9.5_DEG3, res = 600, width = 5000, height = 5000)
L.9.5_DEG4 <- plot_gene_data(top15DEGs_L.9.5[4, ]$gene)
L.9.5_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG4.",top15DEGs_L.9.5[4, ]$gene,".png"), plot = L.9.5_DEG4, res = 600, width = 5000, height = 5000)
L.9.5_DEG5 <- plot_gene_data(top15DEGs_L.9.5[5, ]$gene)
L.9.5_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG5.",top15DEGs_L.9.5[5, ]$gene,".png"), plot = L.9.5_DEG5, res = 600, width = 5000, height = 5000)
L.9.5_DEG6 <- plot_gene_data(top15DEGs_L.9.5[6, ]$gene)
L.9.5_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG6.",top15DEGs_L.9.5[6, ]$gene,".png"), plot = L.9.5_DEG6, res = 600, width = 5000, height = 5000)
L.9.5_DEG7 <- plot_gene_data(top15DEGs_L.9.5[7, ]$gene)
L.9.5_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG7.",top15DEGs_L.9.5[7, ]$gene,".png"), plot = L.9.5_DEG7, res = 600, width = 5000, height = 5000)
L.9.5_DEG8 <- plot_gene_data(top15DEGs_L.9.5[8, ]$gene)
L.9.5_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG8.",top15DEGs_L.9.5[8, ]$gene,".png"), plot = L.9.5_DEG8, res = 600, width = 5000, height = 5000)
L.9.5_DEG9 <- plot_gene_data(top15DEGs_L.9.5[9, ]$gene)
L.9.5_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG9.",top15DEGs_L.9.5[9, ]$gene,".png"), plot = L.9.5_DEG9, res = 600, width = 5000, height = 5000)
L.9.5_DEG10 <- plot_gene_data(top15DEGs_L.9.5[10, ]$gene)
L.9.5_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG10.",top15DEGs_L.9.5[10, ]$gene,".png"), plot = L.9.5_DEG10, res = 600, width = 5000, height = 5000)
L.9.5_DEG11 <- plot_gene_data(top15DEGs_L.9.5[11, ]$gene)
L.9.5_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG11.",top15DEGs_L.9.5[11, ]$gene,".png"), plot = L.9.5_DEG11, res = 600, width = 5000, height = 5000)
L.9.5_DEG12 <- plot_gene_data(top15DEGs_L.9.5[12, ]$gene)
L.9.5_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG12.",top15DEGs_L.9.5[12, ]$gene,".png"), plot = L.9.5_DEG12, res = 600, width = 5000, height = 5000)
L.9.5_DEG13 <- plot_gene_data(top15DEGs_L.9.5[13, ]$gene)
L.9.5_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG13.",top15DEGs_L.9.5[13, ]$gene,".png"), plot = L.9.5_DEG13, res = 600, width = 5000, height = 5000)
L.9.5_DEG14 <- plot_gene_data(top15DEGs_L.9.5[14, ]$gene)
L.9.5_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG14.",top15DEGs_L.9.5[14, ]$gene,".png"), plot = L.9.5_DEG14, res = 600, width = 5000, height = 5000)
L.9.5_DEG15 <- plot_gene_data(top15DEGs_L.9.5[15, ]$gene)
L.9.5_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG15.",top15DEGs_L.9.5[15, ]$gene,".png"), plot = L.9.5_DEG15, res = 600, width = 5000, height = 5000)---
title: "07-cod-RNAseq-DESeq2"
author: "Kathleen Durkin"
date: "2024-03-19"
always_allow_html: true
output: 
  bookdown::html_document2:
    theme: cosmo
    toc: true
    toc_float: true
    number_sections: true
    code_folding: show
    code_download: true
  github_document:
    toc: true
    toc_depth: 3
    number_sections: true
    html_preview: true 
---

```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(
  echo = TRUE,         # Display code chunks
  eval = TRUE,         # Evaluate code chunks
  warning = FALSE,     # Hide warnings
  message = FALSE,     # Hide messages
  comment = ""         # Prevents appending '##' to beginning of lines in code output
)
```

Differential gene expression analysis for [Pacific cod RNAseq data](https://shedurkin.github.io/Roberts-LabNotebook/posts/projects/pacific_cod/2023_12_13_pacific_cod.html).

-   trimmed reads generated in `05-cod-RNAseq-trimming`

-   Reads aligned to transcriptome downloaded from [NCBI](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_031168955.1/), stored [here](https://owl.fish.washington.edu/halfshell/genomic-databank/GCF_031168955.1_ASM3116895v1_rna.fna) as a part of lab [genomic resources](https://robertslab.github.io/resources/Genomic-Resources/#gadus-macrocephalus-pacific-cod).

### Install and load packages

```{r load_libraries, inlcude = TRUE}

## clear
rm(list=ls())

## Install Rtools directly from (https://cran.r-project.org/bin/windows/Rtools/), then install these on first run:
# install.packages("BiocManager")
# BiocManager::install("DESeq2")
# BiocManager::install("vsn")
# BiocManager::install("tidybulk")
# BiocManager::install("goseq")
# BiocManager::install("affycoretools")
# BiocManager::install("EnhancedVolcano")
# BiocManager::install("pcaExplorer")
# BiocManager::install("apeglm")
# BiocManager::install("PCAtools")


# List of packages we want to install (run every time)
load.lib<-c("DESeq2","edgeR","goseq","dplyr","GenomicFeatures","data.table","calibrate","affycoretools","data.table","vsn","tidybulk","ggplot2","cowplot","pheatmap","gplots","RColorBrewer","EnhancedVolcano","pcaExplorer","readxl","apeglm","ashr","tibble","plotly","sqldf","PCAtools","ggpubr","beepr","genefilter","ComplexHeatmap","circlize","scales", "tidyverse", "gridextra'")

# Select only the packages that aren't currently installed (run every time)
# install.lib <- load.lib[!load.lib %in% installed.packages()]

# And finally we install the missing packages, including their dependency.
# for(lib in install.lib) install.packages(lib,dependencies=TRUE)
# After the installation process completes, we load all packages.
sapply(load.lib,require,character=TRUE)
                        
```

I found the [DESeq2 vignette](https://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) and the [HBC DGE training workshop](https://github.com/hbctraining/DGE_workshop) super helpful in figuring out how to use the DESeq2 package!

# Load data

## Load count data

Load in the count matrix we generated after kallisto pseudoalignment using the Trinity abundance_estimates_to_matrix.pl script. We also need to slightly reformat the count matrix to make all of the estimated counts integers, as required for DESeq2.

```{r}
# Read in counts data. This is a gene-level counts matrix generated from kallisto transcript abundances using Trinity
cod_counts_data_OG <- read_delim("../output/06-cod-RNAseq-alignment/kallisto/kallisto.isoform.counts.matrix") 
head(cod_counts_data_OG)
```

## Count data munging

```{r}
# # We need to modify this data frame so that the row names are actually row names, instead of comprising the first column
cod_counts_data <- cod_counts_data_OG %>% 
  column_to_rownames(var = "...1")

# Additional formatting
# Round all estimated counts to integers
cod_counts_data <- round(cod_counts_data, digits = 0)

# Remove the "kallisto_quant_" portion of the column names, to leave just the sample names
colnames(cod_counts_data) <- sub("kallisto_quant_", "sample_", colnames(cod_counts_data))

# Reorder the coumns into alphabetical order (to make it easier to create an associated metadata spreadsheet)
cod_counts_data <- cod_counts_data[, order(colnames(cod_counts_data))]

cod_sample_names <- names(cod_counts_data)

head(cod_counts_data)
cod_sample_names
```

## Import sample metadata sheets

```{r}
# Read in the csv file as a data frame
cod_sample_info_OG <- read.csv("~/project-cod-temperature/data/DESeq2_Sample_Information.csv")
cod_experiment_alldata_OG <- read.csv("~/project-cod-temperature/data/temp-experiment.csv")
head(cod_sample_info_OG)
head(cod_experiment_alldata_OG)

# Rename the "GeneticSamplingCount" column of the experimental data to "sample_number"
cod_experiment_alldata <- cod_experiment_alldata_OG
names(cod_experiment_alldata)[names(cod_experiment_alldata) == "GeneticSamplingCount"] <- "sample_number"

# Calculate length difference and weight difference (end-beginning)
cod_experiment_alldata$SL_diff_mm <- cod_experiment_alldata$SL_mm - cod_experiment_alldata$SL_11212022
cod_experiment_alldata$WWT_diff_g <- cod_experiment_alldata$WholeBodyWW_g - cod_experiment_alldata$WWT_11212022

# Merge the two data frames to get experimental data for all of our RNAseq'd samples.
# This should include all rows from cod_sample_info_OG and matching rows from cod_experiment_alldata based on the shared sample_number column. Sample number duplicates (e.g. from different tissue types) should be retained.
cod_sample_info <- merge(cod_sample_info_OG, cod_experiment_alldata, by = "sample_number", all.x = TRUE)

# Reorder the data frame into alphabetical order by the sample names, so that the rows are in the same order as our count matrix columns
cod_sample_info <- cod_sample_info[order(cod_sample_info$sample_name), ]

# Again, we need to reformat so that the data in the first column becomes the row names
rownames(cod_sample_info) <- cod_sample_info$sample_name

# Remove duplicate columns (artifact of merging data frames with multiple shared columns and of making sample_name the rownames instead of a variable)
cod_sample_info <- subset(cod_sample_info, select=-Temperature)
cod_sample_info <- subset(cod_sample_info, select=-Tank)
#cod_sample_info <- subset(cod_sample_info, select=-sample_name)


head(cod_sample_info)
```

## Sample metadata munging

```{r}
# Factor variables
cod_sample_info$temp_treatment <- factor(cod_sample_info$temp_treatment)
cod_sample_info$tank <- factor(cod_sample_info$tank)
cod_sample_info$tissue_type <- factor(cod_sample_info$tissue_type)

# Remove bad samples
# MuliQC report: 149, 129
# PCA outliers: 31, 41 (per Laura)
cod_sample_info <- cod_sample_info[!(row.names(cod_sample_info) %in% c("sample_149", "sample_129", "sample_31", "sample_41")),]
cod_counts_data <- as.matrix(subset(cod_counts_data, select=-c(sample_149, sample_129, sample_31, sample_41)))

# Check that the column names of our count data match the row names of our sample info sheet
ncol(cod_counts_data)
nrow(cod_sample_info)

all(colnames(cod_counts_data) %in% rownames(cod_sample_info))
all(colnames(cod_counts_data) == rownames(cod_sample_info))
```

Combine counts data and sample metadata for later analyses
```{r}
# Melt cod_counts_data to long format
cod_counts_data_long <- as.data.frame(cod_counts_data)
cod_counts_data_long$gene <- rownames(cod_counts_data_long)
cod_counts_data_long <- cod_counts_data_long %>% 
  pivot_longer(cols = starts_with("sample"), 
               names_to = "sample_name", 
               values_to = "count")

colnames(cod_counts_data_long)
colnames(cod_sample_info)

# Merge cod_counts_data_long with df_C to get treatment info
cod_countsdata_plus_sampleinfo <- left_join(cod_counts_data_long, cod_sample_info, by = "sample_name")
```

# Preliminary PCA visualization (liver tissue)

## DESeq object

```{r, cache=TRUE}
# Filter data
infosub_L <- cod_sample_info %>% filter(tissue_type == "Liver")
countsub_L <- subset(cod_counts_data, select=row.names(infosub_L))

# Calculate DESeq object
dds_L <- DESeqDataSetFromMatrix(countData = countsub_L,
                              colData = infosub_L,
                              design = ~ temp_treatment) 

# Run differential expression analysis 
# (Note that this DESeq() function runs all necessary steps, including data normalization, 
# estimating size factors, estimating dispersions, gene-wise dispersion estimates, mean-dispersion 
# relationship, final dispersion estimates, fitting model, and testing)
dds_L <- DESeq(dds_L)
resultsNames(dds_L) # lists the coefficients
```

```{r}
plotDispEsts(dds_L)
```

## PCA visualization

```{r}
# Generate PCAs
# top 500 most variable genes
pca_L_500<- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE)
percentVar_L_500 <- round(100*attr(pca_L_500, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_500 <- subset(pca_L_500, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_500 <- merge(pca_L_500, cod_sample_info, by.x = "name", by.y = "row.names")

# top 1000 most variable genes
pca_L_1000 <- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE, ntop=1000)
percentVar_L_1000 <- round(100*attr(pca_L_1000, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_1000 <- subset(pca_L_1000, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_1000 <- merge(pca_L_1000, cod_sample_info, by.x = "name", by.y = "row.names")

# all genes
pca_L_all <- plotPCA(vst(dds_L), intgroup = c("temp_treatment"), returnData=TRUE, ntop=nrow(assay(vst(dds_L))))
percentVar_L_all <- round(100*attr(pca_L_all, "percentVar"))
# merge with metadata sheet so we can plot using other features
pca_L_all <- subset(pca_L_all, select=-temp_treatment) #remove the temp_treatment column, which will be a duplicate post-merge
pca_L_all <- merge(pca_L_all, cod_sample_info, by.x = "name", by.y = "row.names")

# Assign specific colors to each temperature treatment level
temp_colors <- c(
  "0" = "darkblue",
  "5" = "royalblue1",
  "9" = "green",
  "16" = "orangered") 

# Plot PCAs
p.L.500 <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment)) + 
  geom_point(size=4, alpha = 5/10) +
  ggtitle("Liver, top 500 most variable genes") +
  xlab(paste0("PC1: ",percentVar_L_500[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar_L_500[2],"% variance")) + 
  coord_fixed() +
  scale_color_manual(values=temp_colors)+
  stat_ellipse()

p.L.500.SLdiff <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, top 500 most variable genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

p.L.500.WWTdiff <- ggplot(pca_L_500, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, top 500 most variable genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

p.L.1000 <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment)) + 
  geom_point(size=4, alpha = 5/10) +
  ggtitle("Liver, top 1000 most variable genes") +
  xlab(paste0("PC1: ",percentVar_L_1000[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar_L_1000[2],"% variance")) + 
  coord_fixed() +
  scale_color_manual(values=temp_colors)+
  stat_ellipse()

p.L.1000.SLdiff <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, top 1000 most variable genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

p.L.1000.WWTdiff <- ggplot(pca_L_1000, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, top 1000 most variable genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

p.L.all <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment)) + 
  geom_point(size=4, alpha = 5/10) +
  ggtitle("Liver, all genes") +
  xlab(paste0("PC1: ",percentVar_L_all[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar_L_all[2],"% variance")) + 
  coord_fixed() +
  scale_color_manual(values=temp_colors)+
  stat_ellipse()

p.L.all.SLdiff <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment, size=SL_diff_mm)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, all genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

p.L.all.WWTdiff <- ggplot(pca_L_all, aes(PC1, PC2, color=temp_treatment, size=WWT_diff_g)) + 
  geom_point(alpha = 0.5) +
  ggtitle("Liver, all genes") +
  xlab(paste0("PC1: ", percentVar_L_500[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_L_500[2], "% variance")) + 
  coord_fixed() +
  scale_color_manual(values = temp_colors) +
  scale_size_continuous() +  # Add this line to control the size of points
  stat_ellipse()

# View PCAs
p.L.500
p.L.500.SLdiff
p.L.500.WWTdiff
p.L.1000
p.L.1000.SLdiff
p.L.1000.WWTdiff
p.L.all
p.L.all.SLdiff
p.L.all.WWTdiff

# Export PCAs as pngs
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_500.png",
         plot   = p.L.500,
         res    = 600,
         width  = 6000,
         height = 4000)

ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_1000.png",
         plot   = p.L.1000,
         res    = 600,
         width  = 6000,
         height = 4000)

ggexport(filename = "../output/07-cod-RNAseq-DESeq2/PCA_L_all.png",
         plot   = p.L.all,
         res    = 600,
         width  = 6000,
         height = 4000)
```

# Liver tissue, 9*C v. 16*C

The 9\*C temperature treatment is effectively our "control," as it represents the ambient temperature that wild juvenile Pacific cod would experience.

```{r, cache=TRUE}
# liver tissue, temperatures 9 vs. 16 

# Filter data
infosub_L.9.16 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "16"))
countsub_L.9.16 <- subset(cod_counts_data, select=row.names(infosub_L.9.16))

# Calculate DESeq object
dds_L.9.16 <- DESeqDataSetFromMatrix(countData = countsub_L.9.16,
                              colData = infosub_L.9.16,
                              design = ~ temp_treatment)

dds_L.9.16 <- DESeq(dds_L.9.16)
resultsNames(dds_L.9.16) # lists the coefficients
```

```{r}
plotDispEsts(dds_L.9.16)
```

```{r, cache=TRUE}
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.16) >= 10) >= ncol(countsub_L.9.16)/3
dds_L.9.16<- dds_L.9.16[keep,]

# Generate Contrasts
contrast_list_L.9.16        <- c("temp_treatment", "16", "9") # order is important: factor, treatment group, control
res_table_L.9.16_noshrink <- results(dds_L.9.16, contrast=contrast_list_L.9.16, alpha = 0.05)

res_table_L.9.16_norm     <- lfcShrink(dds_L.9.16,
                                       coef=2,
                                       type="normal") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.16_apeglm   <- lfcShrink(dds_L.9.16,
                                       coef=2, 
                                       type="apeglm") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.16_ashr     <- lfcShrink(dds_L.9.16,
                                       coef=2, 
                                       type="ashr")
```

```{r}
# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.16_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.16_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.16_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.16_ashr, xlim=xlim, ylim=ylim, main="ashr")
```

```{r}
# Examine results formatting
res_table_L.9.16_norm %>% data.frame() %>% head()
```

Note that the metric we want to use to identify significantly expressed genes is the `padj` values, **NOT** the `pvalue`. `padj` are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).

```{r}
summary(res_table_L.9.16_noshrink)
summary(res_table_L.9.16_norm)
summary(res_table_L.9.16_apeglm)
summary(res_table_L.9.16_ashr)
```

## Extracting significantly expressed genes

```{r}
padj.cutoff <- 0.05
lfc.cutoff <- 0.58

# Convert results table into tibble
res_table_L.9.16_norm_tb <- res_table_L.9.16_norm %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>%
  as_tibble()

# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.16_norm_noLFCcutoff <- res_table_L.9.16_norm_tb %>%
  filter(padj < padj.cutoff)

sig_L.9.16_norm <- sig_L.9.16_norm_noLFCcutoff %>% 
  filter(abs(log2FoldChange) > lfc.cutoff)

head(sig_L.9.16_norm_noLFCcutoff)
paste("Number of significant DEGs for 9C v 16C:", nrow(sig_L.9.16_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")

head(sig_L.9.16_norm)
paste("Number of significant DEGs for 9C v 16C:", nrow(sig_L.9.16_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")

write.table(sig_L.9.16_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.16_norm_noLFCcutoff.tab", sep = "\t",
            row.names = TRUE, col.names = NA)

write.table(sig_L.9.16_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.16_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

For DEGs based on adjusted p-value *and* log fold-change
```{r}
# Retrieve normalized counts matrix
dds_L.9.16_norm_counts <- counts(dds_L.9.16, normalized=TRUE)

# Extract normalized expression for significant genes
norm_sig_L.9.16 <- dds_L.9.16_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.16_norm_counts) %in% sig_L.9.16_norm$gene)

head(norm_sig_L.9.16)

# Annotate heatmap
annotation <- infosub_L.9.16 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.16 <- pheatmap(norm_sig_L.9.16, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression, Liver, 9*C and 16*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.16_norm_sig.png",
         plot   = h.L.9.16,
         res    = 600,
         width  = 5000,
         height = 5000)
```

Note the argument `scale="row"` was included, so the values plotted in the heat map are *Z-scores*, rather thn the normalized count value. This vastly improves the color visualization.

For DEGs based on adjusted p-value *only*
```{r}
# Extract normalized expression for significant genes
norm_sig_L.9.16_noLFCcutoff <- dds_L.9.16_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.16_norm_counts) %in% sig_L.9.16_norm_noLFCcutoff$gene)

head(norm_sig_L.9.16_noLFCcutoff)

# Annotate heatmap
annotation <- infosub_L.9.16 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.16 <- pheatmap(norm_sig_L.9.16_noLFCcutoff, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 16*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.16_norm_sig_noLFCcutoff.png",
         plot   = h.L.9.16,
         res    = 600,
         width  = 5000,
         height = 5000)
```

Note the argument `scale="row"` was included, so the values plotted in the heat map are *Z-scores*, rather thn the normalized count value. This vastly improves the color visualization.

## Volcano plot

```{r}
# Generate plot
v.L.9.16 <- 
  ggplot(res_table_L.9.16_norm_tb) +
  # Plot all
  geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
             size=.5) +
  # Overlay all significantly upregulated in red
  geom_point(data = sig_L.9.16_norm[sig_L.9.16_norm$log2FoldChange > 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="upregulated"), 
             size=.5) +
  # Overlay all significantly downregulated in blue
  geom_point(data = sig_L.9.16_norm[sig_L.9.16_norm$log2FoldChange < 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="downregulated"), 
             size=.5) +
  ggtitle("Liver, 9*C and 16*C") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-4,4)) +
  scale_y_continuous(limits = c(0,30)) +
  scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
                     labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
                     name = NULL) +
  theme(legend.position = "top",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

v.L.9.16

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.16.png",
         plot   = v.L.9.16,
         res    = 600,
         width  = 6000,
         height = 4000)
```

## Plot expression of top DEGs across treatments

```{r}
top15DEGs_L.9.16 <- sig_L.9.16_norm_noLFCcutoff %>%
  arrange(padj) %>%
  slice_head(n=15)

plot_gene_data <- function(geneName) {
  gene_data <- cod_countsdata_plus_sampleinfo %>%
    filter(gene == geneName)
  
  ggplot(data = gene_data, 
         aes(x = temp_treatment, y = count, color = factor(temp_treatment))) +
    geom_boxplot() +
    geom_point(position = position_jitter(width = 0.1), size = 2) +
    labs(title = geneName, x = "Sample", y = "Count", color = "Treatment") +
  scale_color_manual(values = temp_colors) +
    theme_minimal()
}


L.9.16_DEG1 <- plot_gene_data(top15DEGs_L.9.16[1, ]$gene)
L.9.16_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG1.",top15DEGs_L.9.16[1, ]$gene,".png"), plot = L.9.16_DEG1, res = 600, width = 5000, height = 5000)

L.9.16_DEG2 <- plot_gene_data(top15DEGs_L.9.16[2, ]$gene)
L.9.16_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG2.",top15DEGs_L.9.16[2, ]$gene,".png"), plot = L.9.16_DEG2, res = 600, width = 5000, height = 5000)

L.9.16_DEG3 <- plot_gene_data(top15DEGs_L.9.16[3, ]$gene)
L.9.16_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG3.",top15DEGs_L.9.16[3, ]$gene,".png"), plot = L.9.16_DEG3, res = 600, width = 5000, height = 5000)

L.9.16_DEG4 <- plot_gene_data(top15DEGs_L.9.16[4, ]$gene)
L.9.16_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG4.",top15DEGs_L.9.16[4, ]$gene,".png"), plot = L.9.16_DEG4, res = 600, width = 5000, height = 5000)

L.9.16_DEG5 <- plot_gene_data(top15DEGs_L.9.16[5, ]$gene)
L.9.16_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG5.",top15DEGs_L.9.16[5, ]$gene,".png"), plot = L.9.16_DEG5, res = 600, width = 5000, height = 5000)

L.9.16_DEG6 <- plot_gene_data(top15DEGs_L.9.16[6, ]$gene)
L.9.16_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG6.",top15DEGs_L.9.16[6, ]$gene,".png"), plot = L.9.16_DEG6, res = 600, width = 5000, height = 5000)

L.9.16_DEG7 <- plot_gene_data(top15DEGs_L.9.16[7, ]$gene)
L.9.16_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG7.",top15DEGs_L.9.16[7, ]$gene,".png"), plot = L.9.16_DEG7, res = 600, width = 5000, height = 5000)

L.9.16_DEG8 <- plot_gene_data(top15DEGs_L.9.16[8, ]$gene)
L.9.16_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG8.",top15DEGs_L.9.16[8, ]$gene,".png"), plot = L.9.16_DEG8, res = 600, width = 5000, height = 5000)

L.9.16_DEG9 <- plot_gene_data(top15DEGs_L.9.16[9, ]$gene)
L.9.16_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG9.",top15DEGs_L.9.16[9, ]$gene,".png"), plot = L.9.16_DEG9, res = 600, width = 5000, height = 5000)

L.9.16_DEG10 <- plot_gene_data(top15DEGs_L.9.16[10, ]$gene)
L.9.16_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG10.",top15DEGs_L.9.16[10, ]$gene,".png"), plot = L.9.16_DEG10, res = 600, width = 5000, height = 5000)

L.9.16_DEG11 <- plot_gene_data(top15DEGs_L.9.16[11, ]$gene)
L.9.16_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG11.",top15DEGs_L.9.16[11, ]$gene,".png"), plot = L.9.16_DEG11, res = 600, width = 5000, height = 5000)

L.9.16_DEG12 <- plot_gene_data(top15DEGs_L.9.16[12, ]$gene)
L.9.16_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG12.",top15DEGs_L.9.16[12, ]$gene,".png"), plot = L.9.16_DEG12, res = 600, width = 5000, height = 5000)

L.9.16_DEG13 <- plot_gene_data(top15DEGs_L.9.16[13, ]$gene)
L.9.16_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG13.",top15DEGs_L.9.16[13, ]$gene,".png"), plot = L.9.16_DEG13, res = 600, width = 5000, height = 5000)

L.9.16_DEG14 <- plot_gene_data(top15DEGs_L.9.16[14, ]$gene)
L.9.16_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG14.",top15DEGs_L.9.16[14, ]$gene,".png"), plot = L.9.16_DEG14, res = 600, width = 5000, height = 5000)

L.9.16_DEG15 <- plot_gene_data(top15DEGs_L.9.16[15, ]$gene)
L.9.16_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.16_DEG15.",top15DEGs_L.9.16[15, ]$gene,".png"), plot = L.9.16_DEG15, res = 600, width = 5000, height = 5000)

```
# Liver tissue, 9*C v. 0*C

The 9\*C temperature treatment is effectively our "control," as it represents the ambient temperature that wild juvenile Pacific cod would experience.

```{r, cache=TRUE}
# liver tissue, temperatures 9 vs. 0 

# Filter data
infosub_L.9.0 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "0"))
countsub_L.9.0 <- subset(cod_counts_data, select=row.names(infosub_L.9.0))

# Calculate DESeq object
dds_L.9.0 <- DESeqDataSetFromMatrix(countData = countsub_L.9.0,
                              colData = infosub_L.9.0,
                              design = ~ temp_treatment)

dds_L.9.0 <- DESeq(dds_L.9.0)
resultsNames(dds_L.9.0) # lists the coefficients
```

```{r}
plotDispEsts(dds_L.9.0)
```

```{r, cache=TRUE}
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.0) >= 10) >= ncol(countsub_L.9.0)/3
dds_L.9.0<- dds_L.9.0[keep,]

# Generate Contrasts
contrast_list_L.9.0        <- c("temp_treatment", "0", "9") # order is important: factor, treatment group, control
res_table_L.9.0_noshrink <- results(dds_L.9.0, contrast=contrast_list_L.9.0, alpha = 0.05)

res_table_L.9.0_norm     <- lfcShrink(dds_L.9.0,
                                       coef=2,
                                       type="normal") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.0_apeglm   <- lfcShrink(dds_L.9.0,
                                       coef=2, 
                                       type="apeglm") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.0_ashr     <- lfcShrink(dds_L.9.0,
                                       coef=2, 
                                       type="ashr")
```

```{r}
# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.0_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.0_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.0_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.0_ashr, xlim=xlim, ylim=ylim, main="ashr")
```

```{r}
# Examine results formatting
res_table_L.9.0_norm %>% data.frame() %>% head()
```

Note that the metric we want to use to identify significantly expressed genes is the `padj` values, **NOT** the `pvalue`. `padj` are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).

```{r}
summary(res_table_L.9.0_noshrink)
summary(res_table_L.9.0_norm)
summary(res_table_L.9.0_apeglm)
summary(res_table_L.9.0_ashr)
```

## Extracting significantly expressed genes

```{r}
padj.cutoff <- 0.05
lfc.cutoff <- 0.58

# Convert results table into tibble
res_table_L.9.0_norm_tb <- res_table_L.9.0_norm %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>%
  as_tibble()

# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.0_norm_noLFCcutoff <- res_table_L.9.0_norm_tb %>%
  filter(padj < padj.cutoff)

sig_L.9.0_norm <- sig_L.9.0_norm_noLFCcutoff %>% 
  filter(abs(log2FoldChange) > lfc.cutoff)

head(sig_L.9.0_norm_noLFCcutoff)
paste("Number of significant DEGs for 9C v 0C:", nrow(sig_L.9.0_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")

head(sig_L.9.0_norm)
paste("Number of significant DEGs for 9C v 0C:", nrow(sig_L.9.0_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")

write.table(sig_L.9.0_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.0_norm_noLFCcutoff.tab", sep = "\t",
            row.names = TRUE, col.names = NA)

write.table(sig_L.9.0_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.0_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

For DEGs based on adjusted p-value *and* LFC cutoff
```{r}
# Retrieve normalized counts matrix
dds_L.9.0_norm_counts <- counts(dds_L.9.0, normalized=TRUE)

# Extract normalized expression for significant genes
norm_sig_L.9.0 <- dds_L.9.0_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.0_norm_counts) %in% sig_L.9.0_norm$gene)

head(norm_sig_L.9.0)

# Annotate heatmap
annotation <- infosub_L.9.0 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.0 <- pheatmap(norm_sig_L.9.0, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression, Liver, 9*C and 0*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.0_norm_sig.png",
         plot   = h.L.9.0,
         res    = 600,
         width  = 5000,
         height = 5000)
```

Note the argument `scale="row"` was included, so the values plotted in the heat map are *Z-scores*, rather thn the normalized count value. This vastly improves the color visualization.

For DEGs based on adjusted p-value *only*
```{r}
# Extract normalized expression for significant genes
norm_sig_L.9.0_noLFCcutoff <- dds_L.9.0_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.0_norm_counts) %in% sig_L.9.0_norm_noLFCcutoff$gene)

head(norm_sig_L.9.0_noLFCcutoff)

# Annotate heatmap
annotation <- infosub_L.9.0 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.0 <- pheatmap(norm_sig_L.9.0_noLFCcutoff, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 0*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.0_norm_sig_noLFCcutoff.png",
         plot   = h.L.9.0,
         res    = 600,
         width  = 5000,
         height = 5000)
```

## Volcano plot

```{r}
# Generate plot
v.L.9.0 <- 
  ggplot(res_table_L.9.0_norm_tb) +
  # Plot all
  geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
             size=.5) +
  # Overlay all significantly upregulated in red
  geom_point(data = sig_L.9.0_norm[sig_L.9.0_norm$log2FoldChange > 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="upregulated"), 
             size=.5) +
  # Overlay all significantly downregulated in blue
  geom_point(data = sig_L.9.0_norm[sig_L.9.0_norm$log2FoldChange < 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="downregulated"), 
             size=.5) +
  ggtitle("Liver, 9*C and 0*C") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-4,4)) +
  scale_y_continuous(limits = c(0,30)) +
  scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
                     labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
                     name = NULL) +
  theme(legend.position = "top",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

v.L.9.0

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.0.png",
         plot   = v.L.9.0,
         res    = 600,
         width  = 6000,
         height = 4000)
```
## Plot expression of top DEGs across treatments

```{r}
top15DEGs_L.9.0 <- sig_L.9.0_norm_noLFCcutoff %>%
  arrange(padj) %>%
  slice_head(n=15)

L.9.0_DEG1 <- plot_gene_data(top15DEGs_L.9.0[1, ]$gene)
L.9.0_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG1.",top15DEGs_L.9.0[1, ]$gene,".png"), plot = L.9.0_DEG1, res = 600, width = 5000, height = 5000)

L.9.0_DEG2 <- plot_gene_data(top15DEGs_L.9.0[2, ]$gene)
L.9.0_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG2.",top15DEGs_L.9.0[2, ]$gene,".png"), plot = L.9.0_DEG2, res = 600, width = 5000, height = 5000)

L.9.0_DEG3 <- plot_gene_data(top15DEGs_L.9.0[3, ]$gene)
L.9.0_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG3.",top15DEGs_L.9.0[3, ]$gene,".png"), plot = L.9.0_DEG3, res = 600, width = 5000, height = 5000)

L.9.0_DEG4 <- plot_gene_data(top15DEGs_L.9.0[4, ]$gene)
L.9.0_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG4.",top15DEGs_L.9.0[4, ]$gene,".png"), plot = L.9.0_DEG4, res = 600, width = 5000, height = 5000)

L.9.0_DEG5 <- plot_gene_data(top15DEGs_L.9.0[5, ]$gene)
L.9.0_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG5.",top15DEGs_L.9.0[5, ]$gene,".png"), plot = L.9.0_DEG5, res = 600, width = 5000, height = 5000)

L.9.0_DEG6 <- plot_gene_data(top15DEGs_L.9.0[6, ]$gene)
L.9.0_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG6.",top15DEGs_L.9.0[6, ]$gene,".png"), plot = L.9.0_DEG6, res = 600, width = 5000, height = 5000)

L.9.0_DEG7 <- plot_gene_data(top15DEGs_L.9.0[7, ]$gene)
L.9.0_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG7.",top15DEGs_L.9.0[7, ]$gene,".png"), plot = L.9.0_DEG7, res = 600, width = 5000, height = 5000)

L.9.0_DEG8 <- plot_gene_data(top15DEGs_L.9.0[8, ]$gene)
L.9.0_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG8.",top15DEGs_L.9.0[8, ]$gene,".png"), plot = L.9.0_DEG8, res = 600, width = 5000, height = 5000)

L.9.0_DEG9 <- plot_gene_data(top15DEGs_L.9.0[9, ]$gene)
L.9.0_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG9.",top15DEGs_L.9.0[9, ]$gene,".png"), plot = L.9.0_DEG9, res = 600, width = 5000, height = 5000)

L.9.0_DEG10 <- plot_gene_data(top15DEGs_L.9.0[10, ]$gene)
L.9.0_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG10.",top15DEGs_L.9.0[10, ]$gene,".png"), plot = L.9.0_DEG10, res = 600, width = 5000, height = 5000)

L.9.0_DEG11 <- plot_gene_data(top15DEGs_L.9.0[11, ]$gene)
L.9.0_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG11.",top15DEGs_L.9.0[11, ]$gene,".png"), plot = L.9.0_DEG11, res = 600, width = 5000, height = 5000)

L.9.0_DEG12 <- plot_gene_data(top15DEGs_L.9.0[12, ]$gene)
L.9.0_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG12.",top15DEGs_L.9.0[12, ]$gene,".png"), plot = L.9.0_DEG12, res = 600, width = 5000, height = 5000)

L.9.0_DEG13 <- plot_gene_data(top15DEGs_L.9.0[13, ]$gene)
L.9.0_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG13.",top15DEGs_L.9.0[13, ]$gene,".png"), plot = L.9.0_DEG13, res = 600, width = 5000, height = 5000)

L.9.0_DEG14 <- plot_gene_data(top15DEGs_L.9.0[14, ]$gene)
L.9.0_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG14.",top15DEGs_L.9.0[14, ]$gene,".png"), plot = L.9.0_DEG14, res = 600, width = 5000, height = 5000)

L.9.0_DEG15 <- plot_gene_data(top15DEGs_L.9.0[15, ]$gene)
L.9.0_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.0_DEG15.",top15DEGs_L.9.0[15, ]$gene,".png"), plot = L.9.0_DEG15, res = 600, width = 5000, height = 5000)

```


# Liver tissue, 9*C v. 5*C

The 9\*C temperature treatment is effectively our "control," as it represents the ambient temperature that wild juvenile Pacific cod would experience.

```{r, cache=TRUE}
# liver tissue, temperatures 9 vs. 5 

# Filter data
infosub_L.9.5 <- cod_sample_info %>% filter(tissue_type == "Liver" & (temp_treatment == "9" | temp_treatment == "5"))
countsub_L.9.5 <- subset(cod_counts_data, select=row.names(infosub_L.9.5))

# Calculate DESeq object
dds_L.9.5 <- DESeqDataSetFromMatrix(countData = countsub_L.9.5,
                              colData = infosub_L.9.5,
                              design = ~ temp_treatment)

dds_L.9.5 <- DESeq(dds_L.9.5)
resultsNames(dds_L.9.5) # lists the coefficients
```

```{r}
plotDispEsts(dds_L.9.5)
```

```{r, cache=TRUE}
# Filtering: keep genes that have at least 10 counts across 1/3 of the samples - https://support.bioconductor.org/p/110307/
keep <- rowSums(DESeq2::counts(dds_L.9.5) >= 10) >= ncol(countsub_L.9.5)/3
dds_L.9.5<- dds_L.9.5[keep,]

# Generate Contrasts
contrast_list_L.9.5        <- c("temp_treatment", "5", "9") # order is important: factor, treatment group, control
res_table_L.9.5_noshrink <- results(dds_L.9.5, contrast=contrast_list_L.9.5, alpha = 0.05)

res_table_L.9.5_norm     <- lfcShrink(dds_L.9.5,
                                       coef=2,
                                       type="normal") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.5_apeglm   <- lfcShrink(dds_L.9.5,
                                       coef=2, 
                                       type="apeglm") # lfcThreshold = 0.585)  # a lfc threshold of 1 = 2-fold change, 0.585 = 1.5-fold change
res_table_L.9.5_ashr     <- lfcShrink(dds_L.9.5,
                                       coef=2, 
                                       type="ashr")
```

```{r}
# Generate MA plots
par(mfrow=c(2,2), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-4,4)
DESeq2::plotMA(res_table_L.9.5_noshrink, xlim=xlim, ylim=ylim, main="no shrink")
DESeq2::plotMA(res_table_L.9.5_norm, xlim=xlim, ylim=ylim, main="normal")
DESeq2::plotMA(res_table_L.9.5_apeglm, xlim=xlim, ylim=ylim, main="apeglm")
DESeq2::plotMA(res_table_L.9.5_ashr, xlim=xlim, ylim=ylim, main="ashr")
```

```{r}
# Examine results formatting
res_table_L.9.5_norm %>% data.frame() %>% head()
```

Note that the metric we want to use to identify significantly expressed genes is the `padj` values, **NOT** the `pvalue`. `padj` are p-values corrected for multiple testing (default method is the Benjamini and Hochberg method).

```{r}
summary(res_table_L.9.5_noshrink)
summary(res_table_L.9.5_norm)
summary(res_table_L.9.5_apeglm)
summary(res_table_L.9.5_ashr)
```

## Extracting significantly expressed genes

```{r}
padj.cutoff <- 0.05
lfc.cutoff <- 0.58

# Convert results table into tibble
res_table_L.9.5_norm_tb <- res_table_L.9.5_norm %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>%
  as_tibble()

# subset that table to only keep the significant genes using our pre-defined thresholds:
sig_L.9.5_norm_noLFCcutoff <- res_table_L.9.5_norm_tb %>%
  filter(padj < padj.cutoff)

sig_L.9.5_norm <- sig_L.9.5_norm_noLFCcutoff %>% 
  filter(abs(log2FoldChange) > lfc.cutoff)

head(sig_L.9.5_norm)
paste("Number of significant DEGs for 9C v 5C:", nrow(sig_L.9.5_norm_noLFCcutoff), "(padj<", padj.cutoff, ")")

head(sig_L.9.5_norm)
paste("Number of significant DEGs for 9C v 5C:", nrow(sig_L.9.5_norm), "(padj<", padj.cutoff, ", log-fold change >", lfc.cutoff, ")")

write.table(sig_L.9.5_norm_noLFCcutoff, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.5_norm_noLFCcutoff.tab", sep = "\t",
            row.names = TRUE, col.names = NA)

write.table(sig_L.9.5_norm, file = "../output/07-cod-RNAseq-DESeq2/Gmac_DEGs_sig_L.9.5_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

For DEGs based on adjusted p-value *and* LFC cutoff
```{r}
# Retrieve normalized counts matrix
dds_L.9.5_norm_counts <- counts(dds_L.9.5, normalized=TRUE)

# Extract normalized expression for significant genes
norm_sig_L.9.5 <- dds_L.9.5_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.5_norm_counts) %in% sig_L.9.5_norm$gene)

head(norm_sig_L.9.5)

# Annotate heatmap
annotation <- infosub_L.9.5 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.5 <- pheatmap(norm_sig_L.9.5, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression, Liver, 9*C and 5*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.5_norm_sig.png",
         plot   = h.L.9.5,
         res    = 600,
         width  = 5000,
         height = 5000)
```

Note the argument `scale="row"` was included, so the values plotted in the heat map are *Z-scores*, rather thn the normalized count value. This vastly improves the color visualization.

For DEGs based on adjusted p-value *only*
```{r}
# Extract normalized expression for significant genes
norm_sig_L.9.5_noLFCcutoff <- dds_L.9.5_norm_counts %>% 
  data.frame() %>%
  filter(row.names(dds_L.9.5_norm_counts) %in% sig_L.9.5_norm_noLFCcutoff$gene)

head(norm_sig_L.9.5_noLFCcutoff)

# Annotate heatmap
annotation <- infosub_L.9.5 %>% 
	dplyr::select(temp_treatment)

# Set a color palette
heat_colors <- rev(brewer.pal(12, "RdYlBu"))

# Run pheatmap
h.L.9.5 <- pheatmap(norm_sig_L.9.5_noLFCcutoff, 
                     color = heat_colors, 
                     cluster_rows = T, 
                     show_rownames = F,
                     annotation = annotation, 
                     border_color = NA, 
                     fontsize = 10,
                     scale = "row", 
                     fontsize_row = 10, 
                     height = 30,
                     main = "Normalized Significant Expression (no LFC cutoff), Liver, 9*C and 5*C")

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/heatmap_L.9.5_norm_sig_noLFCcutoff.png",
         plot   = h.L.9.5,
         res    = 600,
         width  = 5000,
         height = 5000)
```

## Volcano plot

```{r}
# Generate plot
v.L.9.5 <- 
  ggplot(res_table_L.9.5_norm_tb) +
  # Plot all
  geom_point(aes(x=log2FoldChange, y=-log10(padj),color="unchanged"),
             size=.5) +
  # Overlay all significantly upregulated in red
  geom_point(data = sig_L.9.5_norm[sig_L.9.5_norm$log2FoldChange > 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="upregulated"), 
             size=.5) +
  # Overlay all significantly downregulated in blue
  geom_point(data = sig_L.9.5_norm[sig_L.9.5_norm$log2FoldChange < 0, ], 
             aes(x=log2FoldChange, y=-log10(padj), color="downregulated"), 
             size=.5) +
  ggtitle("Liver, 9*C and 5*C") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-4,4)) +
  scale_y_continuous(limits = c(0,30)) +
  scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
                     labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
                     name = NULL) +
  theme(legend.position = "top",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

v.L.9.5

# Save plot
ggexport(filename = "../output/07-cod-RNAseq-DESeq2/volcano_L.9.5.png",
         plot   = v.L.9.0,
         res    = 600,
         width  = 6000,
         height = 4000)
```

## Plot expression of top DEGs across treatments

```{r}
top15DEGs_L.9.5 <- sig_L.9.5_norm_noLFCcutoff %>%
  arrange(padj) %>%
  slice_head(n=15)

L.9.5_DEG1 <- plot_gene_data(top15DEGs_L.9.5[1, ]$gene)
L.9.5_DEG1
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG1.",top15DEGs_L.9.5[1, ]$gene,".png"), plot = L.9.5_DEG1, res = 600, width = 5000, height = 5000)

L.9.5_DEG2 <- plot_gene_data(top15DEGs_L.9.5[2, ]$gene)
L.9.5_DEG2
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG2.",top15DEGs_L.9.5[2, ]$gene,".png"), plot = L.9.5_DEG2, res = 600, width = 5000, height = 5000)

L.9.5_DEG3 <- plot_gene_data(top15DEGs_L.9.5[3, ]$gene)
L.9.5_DEG3
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG3.",top15DEGs_L.9.5[3, ]$gene,".png"), plot = L.9.5_DEG3, res = 600, width = 5000, height = 5000)

L.9.5_DEG4 <- plot_gene_data(top15DEGs_L.9.5[4, ]$gene)
L.9.5_DEG4
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG4.",top15DEGs_L.9.5[4, ]$gene,".png"), plot = L.9.5_DEG4, res = 600, width = 5000, height = 5000)

L.9.5_DEG5 <- plot_gene_data(top15DEGs_L.9.5[5, ]$gene)
L.9.5_DEG5
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG5.",top15DEGs_L.9.5[5, ]$gene,".png"), plot = L.9.5_DEG5, res = 600, width = 5000, height = 5000)

L.9.5_DEG6 <- plot_gene_data(top15DEGs_L.9.5[6, ]$gene)
L.9.5_DEG6
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG6.",top15DEGs_L.9.5[6, ]$gene,".png"), plot = L.9.5_DEG6, res = 600, width = 5000, height = 5000)

L.9.5_DEG7 <- plot_gene_data(top15DEGs_L.9.5[7, ]$gene)
L.9.5_DEG7
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG7.",top15DEGs_L.9.5[7, ]$gene,".png"), plot = L.9.5_DEG7, res = 600, width = 5000, height = 5000)

L.9.5_DEG8 <- plot_gene_data(top15DEGs_L.9.5[8, ]$gene)
L.9.5_DEG8
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG8.",top15DEGs_L.9.5[8, ]$gene,".png"), plot = L.9.5_DEG8, res = 600, width = 5000, height = 5000)

L.9.5_DEG9 <- plot_gene_data(top15DEGs_L.9.5[9, ]$gene)
L.9.5_DEG9
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG9.",top15DEGs_L.9.5[9, ]$gene,".png"), plot = L.9.5_DEG9, res = 600, width = 5000, height = 5000)

L.9.5_DEG10 <- plot_gene_data(top15DEGs_L.9.5[10, ]$gene)
L.9.5_DEG10
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG10.",top15DEGs_L.9.5[10, ]$gene,".png"), plot = L.9.5_DEG10, res = 600, width = 5000, height = 5000)

L.9.5_DEG11 <- plot_gene_data(top15DEGs_L.9.5[11, ]$gene)
L.9.5_DEG11
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG11.",top15DEGs_L.9.5[11, ]$gene,".png"), plot = L.9.5_DEG11, res = 600, width = 5000, height = 5000)

L.9.5_DEG12 <- plot_gene_data(top15DEGs_L.9.5[12, ]$gene)
L.9.5_DEG12
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG12.",top15DEGs_L.9.5[12, ]$gene,".png"), plot = L.9.5_DEG12, res = 600, width = 5000, height = 5000)

L.9.5_DEG13 <- plot_gene_data(top15DEGs_L.9.5[13, ]$gene)
L.9.5_DEG13
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG13.",top15DEGs_L.9.5[13, ]$gene,".png"), plot = L.9.5_DEG13, res = 600, width = 5000, height = 5000)

L.9.5_DEG14 <- plot_gene_data(top15DEGs_L.9.5[14, ]$gene)
L.9.5_DEG14
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG14.",top15DEGs_L.9.5[14, ]$gene,".png"), plot = L.9.5_DEG14, res = 600, width = 5000, height = 5000)

L.9.5_DEG15 <- plot_gene_data(top15DEGs_L.9.5[15, ]$gene)
L.9.5_DEG15
ggexport(filename = paste("../output/07-cod-RNAseq-DESeq2/L.9.5_DEG15.",top15DEGs_L.9.5[15, ]$gene,".png"), plot = L.9.5_DEG15, res = 600, width = 5000, height = 5000)

```

