07.2.1-cod-RNAseq-DESeq2genome-exon
Kathleen Durkin
2024-04-30
Differential gene expression analysis for Pacific cod RNAseq data.
- Raw reads found here
- Reads aligned to genome downloaded from NCBI
- NOTE this analysis is for the featureCounts exons (see
06.2-cod-RNAseq-alignment-genome)
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 alignment using the featureCounts. 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 hisat2 and summarized using featureCounts
cod_counts_data_OG <- read_delim("../output/06.2-cod-RNAseq-alignment-genome/featureCounts-exon/featureCounts_exon_matrix_noheader.txt", delim="\t")
head(cod_counts_data_OG)# A tibble: 6 × 86
Geneid Chr Start End Strand Length ../output/06.2-cod-R…¹
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 LOC132464423 NC_082382.1;NC_… 7826… 8896… +;+ 1618 6
2 rereb NC_082382.1;NC_… 4475… 4490… +;+;+… 5887 236
3 LOC132464476 NC_082382.1;NC_… 6269… 6305… -;- 1468 2
4 LOC132464448 NC_082382.1;NC_… 7316… 7404… -;-;-… 5017 0
5 c1h1orf159 NC_082382.1;NC_… 8431… 8452… -;-;-… 1093 0
6 LOC132464512 NC_082382.1;NC_… 8922… 8959… +;+;+… 1720 0
# ℹ abbreviated name:
# ¹`../output/06.2-cod-RNAseq-alignment-genome/hisat2/100.sorted.bam`
# ℹ 79 more variables:
# `../output/06.2-cod-RNAseq-alignment-genome/hisat2/107.sorted.bam` <dbl>,
# `../output/06.2-cod-RNAseq-alignment-genome/hisat2/108.sorted.bam` <dbl>,
# `../output/06.2-cod-RNAseq-alignment-genome/hisat2/109.sorted.bam` <dbl>,
# `../output/06.2-cod-RNAseq-alignment-genome/hisat2/10.sorted.bam` <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 = "Geneid")
# Additional formatting
# Remove unnecessary columns
cod_counts_data <- subset(cod_counts_data, select = -c(Chr, Start, End, Strand, Length))
# Remove the directory path and file type portions of the column names, to leave just the sample names
colnames(cod_counts_data) <- sub("../output/06.2-cod-RNAseq-alignment-genome/hisat2/", "sample_", colnames(cod_counts_data))
colnames(cod_counts_data) <- sub(".sorted.bam", "", 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)
write.table(cod_counts_data, file = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_genome_exon_counts_formatted.tab", sep = "\t",
row.names = TRUE, col.names = NA)
head(cod_counts_data) sample_1 sample_10 sample_100 sample_107 sample_108 sample_109
LOC132464423 1 5 6 3 20 0
rereb 465 547 236 121 415 168
LOC132464476 1 1 2 0 5 1
LOC132464448 6 3 0 0 4 0
c1h1orf159 1 8 0 3 3 0
LOC132464512 0 7 0 2 0 1
sample_11 sample_110 sample_117 sample_118 sample_119 sample_12
LOC132464423 0 4 3 0 0 3
rereb 342 204 335 267 179 395
LOC132464476 0 0 0 0 0 0
LOC132464448 2 0 2 0 1 2
c1h1orf159 2 10 3 9 8 3
LOC132464512 0 1 0 0 2 0
sample_120 sample_121 sample_127 sample_128 sample_129 sample_13
LOC132464423 1 26 2 2 9 0
rereb 393 224 317 111 17 445
LOC132464476 0 0 0 0 0 3
LOC132464448 2 3 2 0 0 4
c1h1orf159 8 2 5 1 0 6
LOC132464512 1 0 1 0 3 2
sample_131 sample_137 sample_138 sample_139 sample_140 sample_147
LOC132464423 7 0 0 0 2 2
rereb 150 75 45 72 78 94
LOC132464476 1 0 0 0 0 2
LOC132464448 3 0 0 1 0 1
c1h1orf159 1 2 0 2 1 0
LOC132464512 0 0 1 1 0 0
sample_148 sample_149 sample_150 sample_18 sample_19 sample_19-G
LOC132464423 13 37 21 4 20 25
rereb 352 778 261 353 210 663
LOC132464476 2 0 2 2 2 6
LOC132464448 5 25 2 1 5 30
c1h1orf159 5 69 4 3 0 29
LOC132464512 0 1 1 2 2 11
sample_19-S sample_2 sample_20 sample_20-G sample_20-S sample_21
LOC132464423 48 2 1 18 65 2
rereb 984 305 231 722 820 337
LOC132464476 6 0 1 3 4 1
LOC132464448 38 2 2 26 37 3
c1h1orf159 54 9 1 68 14 2
LOC132464512 19 1 0 4 22 7
sample_28 sample_29 sample_3 sample_30 sample_31 sample_37
LOC132464423 0 1 2 2 0 3
rereb 274 353 301 486 241 124
LOC132464476 1 1 4 0 1 2
LOC132464448 5 3 8 6 5 2
c1h1orf159 3 0 10 4 2 0
LOC132464512 0 1 2 4 0 1
sample_38 sample_39 sample_4 sample_40 sample_41 sample_47
LOC132464423 1 1 1 3 24 11
rereb 132 117 418 70 192 127
LOC132464476 0 0 0 4 15 7
LOC132464448 2 4 2 1 1 0
c1h1orf159 0 4 0 2 0 3
LOC132464512 0 0 0 0 2 0
sample_48 sample_49 sample_5 sample_50 sample_57 sample_57-G
LOC132464423 8 6 6 3 1 7
rereb 115 267 270 241 223 836
LOC132464476 0 5 0 2 0 3
LOC132464448 3 1 8 0 1 38
c1h1orf159 3 4 4 8 5 52
LOC132464512 0 3 0 0 0 1
sample_57-S sample_58 sample_58-G sample_58-S sample_59 sample_60
LOC132464423 33 6 9 31 6 10
rereb 62 213 1065 390 149 289
LOC132464476 19 5 6 30 4 0
LOC132464448 0 2 35 4 0 5
c1h1orf159 0 0 42 0 0 0
LOC132464512 0 0 2 3 0 0
sample_67 sample_68 sample_69 sample_70 sample_78 sample_79
LOC132464423 0 1 1 10 13 2
rereb 337 293 210 122 154 156
LOC132464476 1 1 0 2 0 0
LOC132464448 3 1 1 2 5 1
c1h1orf159 7 7 2 0 1 4
LOC132464512 0 0 3 0 0 0
sample_80 sample_83 sample_88 sample_90 sample_91 sample_92
LOC132464423 1 0 22 3 1 0
rereb 224 158 174 155 396 215
LOC132464476 0 1 0 1 0 0
LOC132464448 1 2 4 0 2 4
c1h1orf159 0 0 1 1 9 5
LOC132464512 1 0 0 0 1 1
sample_97 sample_98 sample_99 sample_RESUB-116 sample_RESUB-156
LOC132464423 2 52 5 3 2
rereb 137 81 193 380 249
LOC132464476 1 13 3 1 1
LOC132464448 0 6 0 4 5
c1h1orf159 0 0 3 5 4
LOC132464512 2 0 0 1 0
sample_RESUB-36 sample_RESUB-76 sample_RESUB-94
LOC132464423 6 1 0
rereb 114 165 463
LOC132464476 0 1 0
LOC132464448 2 1 1
c1h1orf159 4 0 3
LOC132464512 1 0 1cod_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 maing 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/missing 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-PCA_L_500.png", plot = p.L.500, res = 600, width = 6000, height = 4000)
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-PCA_L_1000.png", plot = p.L.1000, res = 600, width = 6000, height = 4000)
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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
rereb 262.72405 0.44093999 0.1274573 3.4590881 0.0005420075
p4htmb 73.90676 0.61162744 0.2143905 2.8511799 0.0043557321
LOC132464555 521.80884 0.05089259 0.0933051 0.5454549 0.5854406870
abhd14a 89.12868 0.12150643 0.1194928 1.0168713 0.3092146703
LOC132464547 65.32804 -0.48357310 0.1942092 -2.4915335 0.0127192973
tusc2b 139.10778 -0.37679681 0.1693066 -2.2261841 0.0260018556
padj
rereb 0.003178632
p4htmb 0.016670447
LOC132464555 0.699748497
abhd14a 0.445645789
LOC132464547 0.039057208
tusc2b 0.067900354Note 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 14245 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 2174, 15%
LFC < 0 (down) : 2841, 20%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_norm)
out of 14245 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2701, 19%
LFC < 0 (down) : 3425, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_apeglm)
out of 14245 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2701, 19%
LFC < 0 (down) : 3425, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.16_ashr)
out of 14245 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2701, 19%
LFC < 0 (down) : 3425, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results4 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 rereb 263. 0.441 0.127 3.46 0.000542 0.00318
2 p4htmb 73.9 0.612 0.214 2.85 0.00436 0.0167
3 LOC132464547 65.3 -0.484 0.194 -2.49 0.0127 0.0391
4 zbtb40 22.4 0.584 0.193 3.03 0.00244 0.0107
5 zc3h11a 826. 0.660 0.165 4.01 0.0000608 0.000531
6 LOC132465594 308. 0.454 0.165 2.75 0.00591 0.0214 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: 5015 (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 p4htmb 73.9 0.612 0.214 2.85 0.00436 0.0167
2 zbtb40 22.4 0.584 0.193 3.03 0.00244 0.0107
3 zc3h11a 826. 0.660 0.165 4.01 0.0000608 0.000531
4 LOC132465678 9.49 0.943 0.257 3.65 0.000257 0.00175
5 LOC132465686 144. 0.676 0.161 4.21 0.0000260 0.000267
6 ppil1 137. -0.589 0.157 -3.75 0.000179 0.00129 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: 2616 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.16_norm_noLFCcutoff, file = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.16_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)4.1 Heatmap
# 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
p4htmb 31.52731 150.321085 58.888643 85.951815 54.578301 48.530523
zbtb40 37.83277 23.621885 3.925910 10.197673 29.563247 17.128420
zc3h11a 1049.07126 1371.500943 724.330310 413.734161 973.881566 383.486288
LOC132465678 22.06912 5.726518 7.851819 8.740863 1.137048 2.854737
LOC132465686 302.66218 178.237857 83.425578 135.483370 50.598634 100.867361
ppil1 130.05016 89.476836 92.258874 217.064753 98.354647 217.911563
sample_11 sample_110 sample_12 sample_13 sample_18 sample_19
p4htmb 86.63664 53.470058 117.325051 91.98737 66.64435 65.93788
zbtb40 38.50517 23.170358 36.557806 31.74785 33.32218 17.58343
zc3h11a 1065.89321 405.481273 1120.539255 1038.72459 1081.01057 668.17049
LOC132465678 13.12676 2.673503 2.550545 21.16523 10.78070 15.38550
LOC132465686 181.14934 90.007931 124.126503 144.90045 216.59414 124.18300
ppil1 108.51458 182.689365 84.167971 91.17332 125.44819 139.56851
sample_2 sample_20 sample_21 sample_28 sample_29 sample_3
p4htmb 94.52159 79.88478 111.555274 79.65069 154.16603 67.30977
zbtb40 14.38372 16.41468 25.814443 31.67925 20.23429 22.14137
zc3h11a 734.59715 1228.91245 1087.894404 1057.18193 1276.68745 1048.61542
LOC132465678 14.38372 19.69762 7.375555 29.86901 26.97906 1.77131
LOC132465686 200.34468 128.03451 192.686382 113.14019 184.03570 104.50728
ppil1 120.20681 99.58240 115.243051 94.13264 90.57254 114.24948
sample_30 sample_4 sample_5 sample_78 sample_79 sample_80
p4htmb 144.13336 37.876705 91.085365 33.471387 68.808378 55.243808
zbtb40 25.60745 41.571994 32.096748 14.036388 8.215926 11.915331
zc3h11a 1045.51562 1308.132067 883.094297 521.505810 611.059472 912.064437
LOC132465678 16.09611 8.314399 6.939837 4.318889 6.161944 3.249636
LOC132465686 127.30561 258.670183 379.088613 89.616941 121.184904 125.652583
ppil1 59.99460 134.878024 70.265853 227.821379 143.778699 152.732881
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
p4htmb 24.49252 29.32661 36.21465 64.62824 85.06382 7.628027
zbtb40 10.31264 26.77647 7.24293 10.12250 12.64462 42.716953
zc3h11a 537.54634 446.27456 514.24803 967.08770 664.41737 234.943242
LOC132465678 6.44540 2.55014 2.41431 12.45846 17.24267 3.051211
LOC132465686 69.61032 160.65884 115.88688 72.41478 88.51235 176.970234
ppil1 166.29132 234.61291 208.83782 122.24861 68.97066 254.776113
sample_98 sample_99 sample_RESUB.116 sample_RESUB.36
p4htmb 3.939351 87.29378 70.58924 114.45319
zbtb40 14.444288 16.16551 27.35333 22.01023
zc3h11a 157.574048 806.12033 922.95431 570.79860
LOC132465678 3.939351 0.00000 11.47075 11.73879
LOC132465686 147.069112 71.12826 103.23676 115.92054
ppil1 286.259521 155.18894 79.41290 136.46342
sample_RESUB.94
p4htmb 133.601103
zbtb40 27.247593
zc3h11a 1074.961508
LOC132465678 1.757909
LOC132465686 153.817060
ppil1 78.226962# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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
rereb 366.50499 391.55063 231.62866 176.27406 235.93745 159.86525
p4htmb 31.52731 150.32108 58.88864 85.95182 54.57830 48.53052
LOC132464547 54.38461 36.50655 46.12944 100.51992 42.07077 78.02947
zbtb40 37.83277 23.62188 3.92591 10.19767 29.56325 17.12842
zc3h11a 1049.07126 1371.50094 724.33031 413.73416 973.88157 383.48629
LOC132465594 554.88067 549.74568 227.70275 205.41027 468.46375 206.49262
sample_11 sample_110 sample_12 sample_13 sample_18 sample_19
rereb 299.29021 181.79820 335.82170 362.25113 345.96259 230.78257
p4htmb 86.63664 53.47006 117.32505 91.98737 66.64435 65.93788
LOC132464547 39.38029 66.83757 34.85744 44.77261 53.90352 67.03684
zbtb40 38.50517 23.17036 36.55781 31.74785 33.32218 17.58343
zc3h11a 1065.89321 405.48127 1120.53925 1038.72459 1081.01057 668.17049
LOC132465594 500.56725 251.30927 334.97152 380.97422 471.41078 304.41320
sample_2 sample_20 sample_21 sample_28 sample_29 sample_3
rereb 313.35963 252.78609 310.69527 248.00329 340.12881 266.58213
p4htmb 94.52159 79.88478 111.55527 79.65069 154.16603 67.30977
LOC132464547 72.94601 65.65872 33.19000 42.54071 40.46858 43.39709
zbtb40 14.38372 16.41468 25.81444 31.67925 20.23429 22.14137
zc3h11a 734.59715 1228.91245 1087.89440 1057.18193 1276.68745 1048.61542
LOC132465594 248.63288 537.30722 405.65554 262.48524 247.62919 204.58629
sample_30 sample_4 sample_5 sample_78 sample_79 sample_80
rereb 355.57774 386.15763 234.21951 166.27721 160.210551 242.63947
p4htmb 144.13336 37.87671 91.08536 33.47139 68.808378 55.24381
LOC132464547 43.89849 44.34346 68.53089 82.05889 71.889350 38.99563
zbtb40 25.60745 41.57199 32.09675 14.03639 8.215926 11.91533
zc3h11a 1045.51562 1308.13207 883.09430 521.50581 611.059472 912.06444
LOC132465594 377.52698 316.87097 324.43739 252.65499 182.804346 269.71977
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
rereb 203.67463 221.86221 187.10903 308.34680 247.14487 209.007949
p4htmb 24.49252 29.32661 36.21465 64.62824 85.06382 7.628027
LOC132464547 110.86088 127.50702 91.74378 49.83383 41.38240 201.379921
zbtb40 10.31264 26.77647 7.24293 10.12250 12.64462 42.716953
zc3h11a 537.54634 446.27456 514.24803 967.08770 664.41737 234.943242
LOC132465594 171.44763 131.33223 137.61567 376.86832 209.21101 172.393418
sample_98 sample_99 sample_RESUB.116 sample_RESUB.36
rereb 106.362483 207.99629 335.29889 167.27774
p4htmb 3.939351 87.29378 70.58924 114.45319
LOC132464547 148.382229 26.94252 35.29462 105.64910
zbtb40 14.444288 16.16551 27.35333 22.01023
zc3h11a 157.574048 806.12033 922.95431 570.79860
LOC132465594 261.310297 536.69508 336.18126 154.07160
sample_RESUB.94
rereb 406.95599
p4htmb 133.60110
LOC132464547 35.15819
zbtb40 27.24759
zc3h11a 1074.96151
LOC132465594 220.61761# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-heatmap_L.9.16_norm_sig.png",
plot = h.L.9.16,
res = 600,
width = 5000,
height = 5000)4.2 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$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 16*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.16
# Save plot
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-volcano_L.9.16.png",
plot = v.L.9.16,
res = 600,
width = 6000,
height = 4000)4.3 Plot expression of top DEGs across treatments
top15DEGs_L.9.16 <- sig_L.9.16_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
plot_gene_data <- function(geneName) {
gene_data <- cod_countsdata_plus_sampleinfo %>%
filter(gene == geneName)
ggplot(data = gene_data,
aes(x = temp_treatment, y = count, color = factor(temp_treatment))) +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.1), size = 2) +
labs(title = geneName, x = "Sample", y = "Count", color = "Treatment") +
scale_color_manual(values = temp_colors) +
theme_minimal()
}
L.9.16_DEG1 <- plot_gene_data(top15DEGs_L.9.16[1, ]$gene)
L.9.16_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG15.",top15DEGs_L.9.16[15, ]$gene,".png"), plot = L.9.16_DEG15, res = 600, width = 5000, height = 5000)5 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
rereb 195.72261 0.5997946 0.1356807 4.4202373 9.859257e-06
p4htmb 40.33790 1.3560309 0.2433347 5.5661856 2.603759e-08
LOC132464555 568.80555 -0.1183840 0.1147838 -1.0313817 3.023619e-01
abhd14a 85.91394 0.1683689 0.1344005 1.2528336 2.102663e-01
LOC132464547 77.66220 0.1626846 0.2298079 0.7082412 4.787955e-01
tusc2b 160.57348 0.1349880 0.2000022 0.6750591 4.996382e-01
padj
rereb 5.571974e-05
p4htmb 2.448409e-07
LOC132464555 4.154587e-01
abhd14a 3.132713e-01
LOC132464547 5.909948e-01
tusc2b 6.092403e-01Note 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 14547 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 3114, 21%
LFC < 0 (down) : 3352, 23%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_norm)
out of 14547 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3894, 27%
LFC < 0 (down) : 3528, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_apeglm)
out of 14547 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3894, 27%
LFC < 0 (down) : 3528, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultssummary(res_table_L.9.0_ashr)
out of 14547 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3891, 27%
LFC < 0 (down) : 3531, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[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.9.0_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 rereb 196. 0.600 0.136 4.42 0.00000986 0.0000557
2 p4htmb 40.3 1.36 0.243 5.57 0.0000000260 0.000000245
3 LOC132464874 14.3 -1.20 0.291 -4.13 0.0000366 0.000182
4 usp48 267. 0.710 0.117 6.07 0.00000000129 0.0000000154
5 zbtb40 23.9 -0.614 0.230 -2.67 0.00755 0.0199
6 atxn7l2a 36.4 -0.948 0.249 -3.80 0.000143 0.000615 paste("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: 6466 (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 rereb 196. 0.600 0.136 4.42 0.00000986 0.0000557
2 p4htmb 40.3 1.36 0.243 5.57 0.0000000260 0.000000245
3 LOC132464874 14.3 -1.20 0.291 -4.13 0.0000366 0.000182
4 usp48 267. 0.710 0.117 6.07 0.00000000129 0.0000000154
5 zbtb40 23.9 -0.614 0.230 -2.67 0.00755 0.0199
6 atxn7l2a 36.4 -0.948 0.249 -3.80 0.000143 0.000615 paste("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: 4405 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.0_norm_noLFCcutoff, file = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.0_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)5.2 Heatmap
# 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 sample_37
rereb 247.999034 185.564799 244.818042 169.749800 195.275681 125.08035
p4htmb 63.050602 90.482010 56.632607 51.531189 57.434024 11.09584
LOC132464874 8.406747 13.802340 4.129461 10.104155 3.828935 17.14811
usp48 270.066745 440.141301 168.128053 315.249628 269.939912 174.50727
zbtb40 4.203373 10.735154 30.675996 18.187479 24.888077 16.13940
atxn7l2a 29.423614 6.134374 39.524841 8.083324 22.016376 28.24395
sample_38 sample_39 sample_40 sample_47 sample_48 sample_49
rereb 114.34618 120.39379 83.94794 114.62611 140.38311 177.26531
p4htmb 13.86014 9.26106 19.18810 10.83081 18.31084 21.90920
LOC132464874 30.31906 32.92821 41.97397 16.24622 53.71180 26.55660
usp48 218.29725 230.49750 223.06166 163.36477 207.52286 204.48582
zbtb40 18.19144 14.40609 17.98884 30.68730 14.64867 47.80188
atxn7l2a 24.25525 27.78318 21.58661 43.32325 17.09012 80.99763
sample_50 sample_57 sample_58 sample_59 sample_60 sample_67
rereb 175.924054 166.979073 172.660638 157.54476 189.221992 206.775700
p4htmb 13.139556 17.222057 12.969813 19.03225 33.392116 20.248066
LOC132464874 7.299753 8.985421 8.916747 48.63798 4.583232 3.681466
usp48 152.564844 205.915897 179.956158 271.73828 165.651086 219.047255
zbtb40 35.768791 48.671030 30.803306 10.57347 43.213327 18.407332
atxn7l2a 69.347656 59.902806 68.091519 17.97491 77.260190 60.130619
sample_68 sample_69 sample_70 sample_78 sample_79 sample_80
rereb 192.508523 162.176007 127.04274 175.828245 171.240293 256.683441
p4htmb 24.309950 49.425069 28.11602 35.393997 73.545510 58.441319
LOC132464874 7.227282 7.722667 26.03335 5.708709 6.586165 4.583633
usp48 199.078780 193.838942 249.92014 375.633069 353.457528 272.726156
zbtb40 55.190157 33.979735 28.11602 14.842644 8.781553 12.604990
atxn7l2a 72.929850 64.098136 47.90136 21.693095 19.758495 32.085430
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
rereb 217.774925 237.916095 198.234831 329.456976 263.355034 221.642448
p4htmb 26.188124 31.448679 38.368032 69.052851 90.643128 8.089140
LOC132464874 5.513289 19.142674 12.789344 2.495886 4.899629 6.471312
usp48 250.854661 393.792158 377.285645 301.170266 327.050205 564.622003
zbtb40 11.026578 28.714012 7.673606 10.815507 13.473978 45.299186
atxn7l2a 28.944769 9.571337 8.952541 43.262027 24.498143 1.617828
sample_98 sample_99 sample_RESUB.116 sample_RESUB.76
rereb 113.373933 217.551880 358.02267 178.76269
p4htmb 4.199035 91.304157 75.37319 23.83503
LOC132464874 20.995173 3.381635 3.76866 18.41797
usp48 401.707639 240.096117 279.82299 126.75900
zbtb40 15.396460 16.908177 29.20711 37.91936
atxn7l2a 9.797747 28.180295 27.32278 70.42166
sample_RESUB.94
rereb 440.164201
p4htmb 144.503150
LOC132464874 1.901357
usp48 347.948375
zbtb40 29.471037
atxn7l2a 60.843432# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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 sample_37
rereb 247.999034 185.564799 244.818042 169.749800 195.275681 125.08035
p4htmb 63.050602 90.482010 56.632607 51.531189 57.434024 11.09584
LOC132464874 8.406747 13.802340 4.129461 10.104155 3.828935 17.14811
usp48 270.066745 440.141301 168.128053 315.249628 269.939912 174.50727
zbtb40 4.203373 10.735154 30.675996 18.187479 24.888077 16.13940
atxn7l2a 29.423614 6.134374 39.524841 8.083324 22.016376 28.24395
sample_38 sample_39 sample_40 sample_47 sample_48 sample_49
rereb 114.34618 120.39379 83.94794 114.62611 140.38311 177.26531
p4htmb 13.86014 9.26106 19.18810 10.83081 18.31084 21.90920
LOC132464874 30.31906 32.92821 41.97397 16.24622 53.71180 26.55660
usp48 218.29725 230.49750 223.06166 163.36477 207.52286 204.48582
zbtb40 18.19144 14.40609 17.98884 30.68730 14.64867 47.80188
atxn7l2a 24.25525 27.78318 21.58661 43.32325 17.09012 80.99763
sample_50 sample_57 sample_58 sample_59 sample_60 sample_67
rereb 175.924054 166.979073 172.660638 157.54476 189.221992 206.775700
p4htmb 13.139556 17.222057 12.969813 19.03225 33.392116 20.248066
LOC132464874 7.299753 8.985421 8.916747 48.63798 4.583232 3.681466
usp48 152.564844 205.915897 179.956158 271.73828 165.651086 219.047255
zbtb40 35.768791 48.671030 30.803306 10.57347 43.213327 18.407332
atxn7l2a 69.347656 59.902806 68.091519 17.97491 77.260190 60.130619
sample_68 sample_69 sample_70 sample_78 sample_79 sample_80
rereb 192.508523 162.176007 127.04274 175.828245 171.240293 256.683441
p4htmb 24.309950 49.425069 28.11602 35.393997 73.545510 58.441319
LOC132464874 7.227282 7.722667 26.03335 5.708709 6.586165 4.583633
usp48 199.078780 193.838942 249.92014 375.633069 353.457528 272.726156
zbtb40 55.190157 33.979735 28.11602 14.842644 8.781553 12.604990
atxn7l2a 72.929850 64.098136 47.90136 21.693095 19.758495 32.085430
sample_83 sample_88 sample_90 sample_91 sample_92 sample_97
rereb 217.774925 237.916095 198.234831 329.456976 263.355034 221.642448
p4htmb 26.188124 31.448679 38.368032 69.052851 90.643128 8.089140
LOC132464874 5.513289 19.142674 12.789344 2.495886 4.899629 6.471312
usp48 250.854661 393.792158 377.285645 301.170266 327.050205 564.622003
zbtb40 11.026578 28.714012 7.673606 10.815507 13.473978 45.299186
atxn7l2a 28.944769 9.571337 8.952541 43.262027 24.498143 1.617828
sample_98 sample_99 sample_RESUB.116 sample_RESUB.76
rereb 113.373933 217.551880 358.02267 178.76269
p4htmb 4.199035 91.304157 75.37319 23.83503
LOC132464874 20.995173 3.381635 3.76866 18.41797
usp48 401.707639 240.096117 279.82299 126.75900
zbtb40 15.396460 16.908177 29.20711 37.91936
atxn7l2a 9.797747 28.180295 27.32278 70.42166
sample_RESUB.94
rereb 440.164201
p4htmb 144.503150
LOC132464874 1.901357
usp48 347.948375
zbtb40 29.471037
atxn7l2a 60.843432# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/heatmap_L.9.0_norm_sig_noLFCcutoff.png",
plot = h.L.9.0,
res = 600,
width = 5000,
height = 5000)5.3 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$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 0*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.0
# Save plot
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-volcano_L.9.0.png",
plot = v.L.9.0,
res = 600,
width = 6000,
height = 4000)5.4 Plot expression of top DEGs across treatments
top15DEGs_L.9.0 <- sig_L.9.0_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
L.9.0_DEG1 <- plot_gene_data(top15DEGs_L.9.0[1, ]$gene)
L.9.0_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG15.",top15DEGs_L.9.0[15, ]$gene,".png"), plot = L.9.0_DEG15, res = 600, width = 5000, height = 5000)6 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
rereb 187.79714 0.2045636 0.1285921 1.5904247 0.111739109
p4htmb 39.63125 0.5986769 0.2003332 2.9873841 0.002813759
LOC132464555 491.53128 -0.1406288 0.1029234 -1.3664738 0.171790296
abhd14a 82.85574 -0.1635148 0.1236476 -1.3229814 0.185841567
LOC132464547 61.72158 0.3457015 0.1867980 1.8553627 0.063544453
tusc2b 155.64889 -0.1649801 0.1783700 -0.9258003 0.354549763
padj
rereb 0.32066032
p4htmb 0.02992357
LOC132464555 0.41125645
abhd14a 0.42787081
LOC132464547 0.23023829
tusc2b 0.61074721Note 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 14208 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 876, 6.2%
LFC < 0 (down) : 824, 5.8%
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.9.5_norm)
out of 14208 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1220, 8.6%
LFC < 0 (down) : 1172, 8.2%
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.9.5_apeglm)
out of 14208 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1220, 8.6%
LFC < 0 (down) : 1172, 8.2%
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.9.5_ashr)
out of 14208 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1223, 8.6%
LFC < 0 (down) : 1169, 8.2%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results6.1 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 p4htmb 39.6 0.599 0.200 2.99 0.00281 0.0299
2 fkbp5 401. -0.916 0.191 -4.80 0.00000156 0.000102
3 fbxo2 12.2 -0.680 0.214 -3.17 0.00152 0.0193
4 LOC132466537 354. 0.682 0.146 4.66 0.00000322 0.000184
5 LOC132466614 136. 0.723 0.141 5.12 0.000000304 0.0000261
6 ccdc3b 41.2 0.838 0.196 4.29 0.0000181 0.000736 paste("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: 1700 (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 p4htmb 39.6 0.599 0.200 2.99 0.00281 0.0299
2 fkbp5 401. -0.916 0.191 -4.80 0.00000156 0.000102
3 fbxo2 12.2 -0.680 0.214 -3.17 0.00152 0.0193
4 LOC132466537 354. 0.682 0.146 4.66 0.00000322 0.000184
5 LOC132466614 136. 0.723 0.141 5.12 0.000000304 0.0000261
6 ccdc3b 41.2 0.838 0.196 4.29 0.0000181 0.000736 paste("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: 840 (padj< 0.05 , log-fold change > 0.58 )"write.table(sig_L.9.5_norm_noLFCcutoff, file = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.5_norm.tab", sep = "\t",
row.names = TRUE, col.names = NA)6.2 Heatmap
# 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 sample_117
p4htmb 53.727408 78.23193 49.33584 44.069179 48.34665 86.072979
fkbp5 188.941383 163.09369 145.95187 258.366361 182.10573 360.209521
fbxo2 3.581827 2.65193 10.27830 5.184609 12.89244 9.432655
LOC132466537 368.928199 425.63475 419.86858 406.127724 290.07993 226.973266
LOC132466614 214.909630 145.85614 112.54739 273.056087 124.89553 93.147470
ccdc3b 35.818272 114.03298 76.05942 60.487108 27.39644 15.917606
sample_118 sample_119 sample_120 sample_121 sample_127 sample_128
p4htmb 15.12887 16.06242 42.41875 13.12144 15.02789 30.123885
fkbp5 759.73235 331.19189 1065.11742 814.84117 674.70047 466.920222
fbxo2 11.83998 10.70828 15.68913 101.03506 11.91867 4.518583
LOC132466537 176.94199 165.21351 216.16131 253.24371 210.90867 331.362738
LOC132466614 87.48433 117.02623 91.22937 61.67075 137.84203 140.076066
ccdc3b 13.15554 22.94632 23.24315 30.17930 19.17352 24.099108
sample_131 sample_137 sample_138 sample_139 sample_140 sample_147
p4htmb 29.18508 17.799905 25.14052 21.71330 25.066986 19.23673
fkbp5 1096.87252 453.897575 1064.28185 313.03347 198.969202 251.82632
fbxo2 7.29627 8.899952 13.96695 28.95107 7.833433 17.48794
LOC132466537 196.99928 195.798954 332.41349 258.75021 206.802635 416.21294
LOC132466614 87.55523 86.774536 41.90086 57.90215 111.234751 89.18849
ccdc3b 21.88881 31.149834 25.14052 12.66609 31.333733 26.23191
sample_148 sample_150 sample_78 sample_79 sample_80 sample_83
p4htmb 28.61782 20.728754 30.208488 62.79026 50.095212 22.174947
fkbp5 504.21869 505.127001 416.097565 118.08318 166.001782 772.621851
fbxo2 8.63077 8.727896 1.948935 14.05752 5.893554 1.167102
LOC132466537 317.52150 404.756193 500.876226 359.87254 513.721492 461.005485
LOC132466614 123.55629 116.735614 194.893473 131.20353 186.629223 215.913961
ccdc3b 44.06235 43.093988 36.055293 30.92655 50.095212 44.349895
sample_88 sample_90 sample_91 sample_92 sample_97 sample_98
p4htmb 26.914531 32.999136 59.06621 76.93003 6.986541 3.654154
fkbp5 218.826835 406.989341 272.55853 122.67221 262.693928 77.955289
fbxo2 9.361576 4.399885 10.67462 0.00000 9.781157 0.000000
LOC132466537 571.056126 831.578222 426.27300 265.09674 807.644097 460.423426
LOC132466614 156.806395 158.395852 180.04519 169.45399 183.047365 52.376210
ccdc3b 79.573395 65.998272 19.92595 33.26704 96.414261 168.091092
sample_99 sample_RESUB.116 sample_RESUB.156 sample_RESUB.94
p4htmb 79.39684 64.16723 30.733755 122.18868
fkbp5 158.79368 120.31356 390.082280 315.11817
fbxo2 11.76250 13.63554 18.913080 12.05809
LOC132466537 379.34047 193.30379 184.402532 267.68967
LOC132466614 267.59676 171.64735 94.565401 154.34359
ccdc3b 28.42603 24.06271 5.516315 19.29295# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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
p4htmb 53.727408 78.23193 49.33584 44.069179 48.34665
patz1 55.518321 70.27614 108.43607 50.981991 38.67732
fkbp5 188.941383 163.09369 145.95187 258.366361 182.10573
si:ch211-117k10.3 44.772840 37.12702 34.94622 24.194843 40.28888
snrpe 468.323903 533.03791 362.82401 632.522328 614.80830
fbxo2 3.581827 2.65193 10.27830 5.184609 12.89244
sample_117 sample_118 sample_119 sample_120 sample_121
p4htmb 86.072979 15.12887 16.06242 42.41875 13.12144
patz1 69.565832 43.41328 21.41657 48.22954 59.04646
fkbp5 360.209521 759.73235 331.19189 1065.11742 814.84117
si:ch211-117k10.3 74.282160 73.01324 48.95215 67.98622 44.61288
snrpe 500.520267 569.63482 987.45666 471.83598 947.36767
fbxo2 9.432655 11.83998 10.70828 15.68913 101.03506
sample_127 sample_128 sample_131 sample_137 sample_138
p4htmb 15.02789 30.123885 29.18508 17.799905 25.14052
patz1 66.84820 84.346879 59.58620 60.074679 50.28103
fkbp5 674.70047 466.920222 1096.87252 453.897575 1064.28185
si:ch211-117k10.3 71.51203 55.729188 82.69105 60.074679 61.45459
snrpe 561.73219 652.182116 417.10341 1063.544318 1173.22409
fbxo2 11.91867 4.518583 7.29627 8.899952 13.96695
sample_139 sample_140 sample_147 sample_148 sample_150
p4htmb 21.71330 25.066986 19.23673 28.61782 20.728754
patz1 54.28326 57.967405 34.97588 60.86964 52.912872
fkbp5 313.03347 198.969202 251.82632 504.21869 505.127001
si:ch211-117k10.3 85.04378 65.800838 103.17884 51.78462 40.912014
snrpe 741.87123 777.076568 734.49343 463.33610 505.127001
fbxo2 28.95107 7.833433 17.48794 8.63077 8.727896
sample_78 sample_79 sample_80 sample_83 sample_88
p4htmb 30.208488 62.79026 50.095212 22.174947 26.914531
patz1 71.136118 42.17256 125.729160 51.352510 107.658122
fkbp5 416.097565 118.08318 166.001782 772.621851 218.826835
si:ch211-117k10.3 24.361684 44.04690 58.935544 44.349895 25.744334
snrpe 540.829388 428.28581 511.756973 548.538172 492.652929
fbxo2 1.948935 14.05752 5.893554 1.167102 9.361576
sample_90 sample_91 sample_92 sample_97 sample_98
p4htmb 32.999136 59.06621 76.93003 6.986541 3.654154
patz1 218.894267 43.41011 44.70259 88.030412 227.775610
fkbp5 406.989341 272.55853 122.67221 262.693928 77.955289
si:ch211-117k10.3 13.199654 49.10323 28.06907 22.356930 36.541542
snrpe 444.388362 411.32854 339.94758 496.044385 356.889058
fbxo2 4.399885 10.67462 0.00000 9.781157 0.000000
sample_99 sample_RESUB.116 sample_RESUB.156 sample_RESUB.94
p4htmb 79.39684 64.16723 30.73376 122.18868
patz1 82.33747 70.58396 62.25556 138.26613
fkbp5 158.79368 120.31356 390.08228 315.11817
si:ch211-117k10.3 84.29788 108.28221 57.52729 57.87885
snrpe 331.31028 297.57555 446.82152 295.82522
fbxo2 11.76250 13.63554 18.91308 12.05809# 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/heatmap_L.9.5_norm_sig_noLFCcutoff.png",
plot = h.L.9.5,
res = 600,
width = 5000,
height = 5000)6.3 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$log2FoldChange < 0, ],
aes(x=log2FoldChange, y=-log10(padj), color="downregulated"),
size=.5) +
ggtitle("Liver, 9*C and 0*C") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-4,4)) +
scale_y_continuous(limits = c(0,30)) +
scale_color_manual(values = c("unchanged" = "darkgrey", "upregulated" = "red", "downregulated" = "blue"),
labels = c("unchanged" = "Unchanged", "upregulated" = "Upregulated", "downregulated" = "Downregulated"),
name = NULL) +
theme(legend.position = "top",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
v.L.9.5
# Save plot
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-volcano_L.9.5.png",
plot = v.L.9.0,
res = 600,
width = 6000,
height = 4000)6.4 Plot expression of top DEGs across treatments
top15DEGs_L.9.5 <- sig_L.9.5_norm_noLFCcutoff %>%
arrange(padj) %>%
slice_head(n=15)
L.9.5_DEG1 <- plot_gene_data(top15DEGs_L.9.5[1, ]$gene)
L.9.5_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG15.",top15DEGs_L.9.5[15, ]$gene,".png"), plot = L.9.5_DEG15, res = 600, width = 5000, height = 5000)---
title: "07.2.1-cod-RNAseq-DESeq2genome-exon"
author: "Kathleen Durkin"
date: "2024-04-30"
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).

-   Raw reads found [here](https://owl.fish.washington.edu/nightingales/G_macrocephalus/30-943133806/)
-   Reads aligned to genome downloaded from [NCBI](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_031168955.1/)
-   NOTE this analysis is for the featureCounts *exons* (see `06.2-cod-RNAseq-alignment-genome`)

### 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 alignment using the featureCounts. 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 hisat2 and summarized using featureCounts
cod_counts_data_OG <- read_delim("../output/06.2-cod-RNAseq-alignment-genome/featureCounts-exon/featureCounts_exon_matrix_noheader.txt", delim="\t") 
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 = "Geneid")

# Additional formatting
# Remove unnecessary columns 
cod_counts_data <- subset(cod_counts_data, select = -c(Chr, Start, End, Strand, Length))

# Remove the directory path and file type portions of the column names, to leave just the sample names
colnames(cod_counts_data) <- sub("../output/06.2-cod-RNAseq-alignment-genome/hisat2/", "sample_", colnames(cod_counts_data))
colnames(cod_counts_data) <- sub(".sorted.bam", "", 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)

write.table(cod_counts_data, file = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_genome_exon_counts_formatted.tab", sep = "\t",
            row.names = TRUE, col.names = NA)

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 maing 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/missing 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-PCA_L_500.png", plot = p.L.500, res = 600, width = 6000, height = 4000)

ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-PCA_L_1000.png", plot = p.L.1000, res = 600, width = 6000, height = 4000)

ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.16_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

```{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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-heatmap_L.9.16_norm_sig.png",
         plot   = h.L.9.16,
         res    = 600,
         width  = 5000,
         height = 5000)
```

## 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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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, ]$gene)
L.9.16_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG1.",top15DEGs_L.9.16[1, ]$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, ]$gene)
L.9.16_DEG2
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG2.",top15DEGs_L.9.16[2, ]$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, ]$gene)
L.9.16_DEG3
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG3.",top15DEGs_L.9.16[3, ]$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, ]$gene)
L.9.16_DEG4
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG4.",top15DEGs_L.9.16[4, ]$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, ]$gene)
L.9.16_DEG5
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG5.",top15DEGs_L.9.16[5, ]$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, ]$gene)
L.9.16_DEG6
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG6.",top15DEGs_L.9.16[6, ]$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, ]$gene)
L.9.16_DEG7
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG7.",top15DEGs_L.9.16[7, ]$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, ]$gene)
L.9.16_DEG8
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG8.",top15DEGs_L.9.16[8, ]$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, ]$gene)
L.9.16_DEG9
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG9.",top15DEGs_L.9.16[9, ]$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, ]$gene)
L.9.16_DEG10
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG10.",top15DEGs_L.9.16[10, ]$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, ]$gene)
L.9.16_DEG11
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG11.",top15DEGs_L.9.16[11, ]$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, ]$gene)
L.9.16_DEG12
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG12.",top15DEGs_L.9.16[12, ]$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, ]$gene)
L.9.16_DEG13
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG13.",top15DEGs_L.9.16[13, ]$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, ]$gene)
L.9.16_DEG14
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG14.",top15DEGs_L.9.16[14, ]$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, ]$gene)
L.9.16_DEG15
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.16_DEG15.",top15DEGs_L.9.16[15, ]$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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.0_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

```{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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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, ]$gene)
L.9.0_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG1.",top15DEGs_L.9.0[1, ]$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, ]$gene)
L.9.0_DEG2
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG2.",top15DEGs_L.9.0[2, ]$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, ]$gene)
L.9.0_DEG3
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG3.",top15DEGs_L.9.0[3, ]$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, ]$gene)
L.9.0_DEG4
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG4.",top15DEGs_L.9.0[4, ]$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, ]$gene)
L.9.0_DEG5
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG5.",top15DEGs_L.9.0[5, ]$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, ]$gene)
L.9.0_DEG6
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG6.",top15DEGs_L.9.0[6, ]$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, ]$gene)
L.9.0_DEG7
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG7.",top15DEGs_L.9.0[7, ]$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, ]$gene)
L.9.0_DEG8
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG8.",top15DEGs_L.9.0[8, ]$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, ]$gene)
L.9.0_DEG9
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG9.",top15DEGs_L.9.0[9, ]$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, ]$gene)
L.9.0_DEG10
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG10.",top15DEGs_L.9.0[10, ]$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, ]$gene)
L.9.0_DEG11
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG11.",top15DEGs_L.9.0[11, ]$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, ]$gene)
L.9.0_DEG12
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG12.",top15DEGs_L.9.0[12, ]$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, ]$gene)
L.9.0_DEG13
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG13.",top15DEGs_L.9.0[13, ]$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, ]$gene)
L.9.0_DEG14
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG14.",top15DEGs_L.9.0[14, ]$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, ]$gene)
L.9.0_DEG15
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.0_DEG15.",top15DEGs_L.9.0[15, ]$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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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/07.2.1-cod-RNAseq-DESeq2-genome-exon/Gmac_DEGs_sig_L.9.5_norm.tab", sep = "\t",
            row.names = TRUE, col.names = NA)
```

## Heatmap

```{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/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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/07.2.1-cod-RNAseq-DESeq2-genome-exon/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 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.9.5

# Save plot
ggexport(filename = "../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/exon-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, ]$gene)
L.9.5_DEG1
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG1.",top15DEGs_L.9.5[1, ]$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, ]$gene)
L.9.5_DEG2
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG2.",top15DEGs_L.9.5[2, ]$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, ]$gene)
L.9.5_DEG3
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG3.",top15DEGs_L.9.5[3, ]$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, ]$gene)
L.9.5_DEG4
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG4.",top15DEGs_L.9.5[4, ]$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, ]$gene)
L.9.5_DEG5
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG5.",top15DEGs_L.9.5[5, ]$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, ]$gene)
L.9.5_DEG6
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG6.",top15DEGs_L.9.5[6, ]$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, ]$gene)
L.9.5_DEG7
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG7.",top15DEGs_L.9.5[7, ]$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, ]$gene)
L.9.5_DEG8
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG8.",top15DEGs_L.9.5[8, ]$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, ]$gene)
L.9.5_DEG9
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG9.",top15DEGs_L.9.5[9, ]$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, ]$gene)
L.9.5_DEG10
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG10.",top15DEGs_L.9.5[10, ]$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, ]$gene)
L.9.5_DEG11
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG11.",top15DEGs_L.9.5[11, ]$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, ]$gene)
L.9.5_DEG12
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG12.",top15DEGs_L.9.5[12, ]$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, ]$gene)
L.9.5_DEG13
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG13.",top15DEGs_L.9.5[13, ]$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, ]$gene)
L.9.5_DEG14
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG14.",top15DEGs_L.9.5[14, ]$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, ]$gene)
L.9.5_DEG15
ggexport(filename = paste("../output/07.2.1-cod-RNAseq-DESeq2-genome-exon/L.9.5_DEG15.",top15DEGs_L.9.5[15, ]$gene,".png"), plot = L.9.5_DEG15, res = 600, width = 5000, height = 5000)

```
