3.0-RNASeq
Susan Garcia
2023-04-14
#Kallisto Assignment:
Creating a workflow for RNA Seq
##Downloading reference
# Setting Directory and downloading data
cd ../data
curl --insecure -O https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/rna.fna##Kallisto is installed on the Raven server.
/home/shared/kallisto/kallisto \
index -i \
../data/cgigas_roslin_rna.index \
../data/rna.fna##Downloading sequence reads from the server.
#Setting Directory
cd ../data
#Downloading fastq files using wget
wget --recursive --no-parent --no-directories \
--no-check-certificate \
--accept '*.fastq.gz' \
https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/nopp/#Running Kallisto on fastq for alignment.
#Creating kallisto_01 folder before alignment/run.
mkdir ../output/kallisto_01
find ../data/*fastq.gz \
| xargs basename -s _L001_R1_001.fastq.gz | xargs -I{} /home/shared/kallisto/kallisto \
quant -i ../data/cgigas_roslin_rna.index \
-o ../output/kallisto_01/{} \
-t 40 \
--single -l 100 -s 10 ../data/{}_L001_R1_001.fastq.gz##COmbining output files for visualization
perl /home/shared/trinityrnaseq-v2.12.0/util/abundance_estimates_to_matrix.pl \
--est_method kallisto \
--gene_trans_map none \
--out_prefix ../output/kallisto_01 \
--name_sample_by_basedir \
../output/kallisto_01/D54_S145/abundance.tsv \
../output/kallisto_01/D56_S136/abundance.tsv \
../output/kallisto_01/D58_S144/abundance.tsv \
../output/kallisto_01/M45_S140/abundance.tsv \
../output/kallisto_01/M48_S137/abundance.tsv \
../output/kallisto_01/M89_S138/abundance.tsv \
../output/kallisto_01/D55_S146/abundance.tsv \
../output/kallisto_01/D57_S143/abundance.tsv \
../output/kallisto_01/D59_S142/abundance.tsv \
../output/kallisto_01/M46_S141/abundance.tsv \
../output/kallisto_01/M49_S139/abundance.tsv \
../output/kallisto_01/M90_S147/abundance.tsv \
../output/kallisto_01/N48_S194/abundance.tsv \
../output/kallisto_01/N50_S187/abundance.tsv \
../output/kallisto_01/N52_S184/abundance.tsv \
../output/kallisto_01/N54_S193/abundance.tsv \
../output/kallisto_01/N56_S192/abundance.tsv \
../output/kallisto_01/N58_S195/abundance.tsv \
../output/kallisto_01/N49_S185/abundance.tsv \
../output/kallisto_01/N51_S186/abundance.tsv \
../output/kallisto_01/N53_S188/abundance.tsv \
../output/kallisto_01/N55_S190/abundance.tsv \
../output/kallisto_01/N57_S191/abundance.tsv \
../output/kallisto_01/N59_S189/abundance.tsv#Run DESeq2
countmatrix <- read.delim("../output/kallisto_01.isoform.counts.matrix", header = TRUE, sep = '\t')
rownames(countmatrix) <- countmatrix$X
countmatrix <- countmatrix[,-1]
head(countmatrix)## D54_S145 D56_S136 D58_S144 M45_S140 M48_S137 M89_S138 D55_S146
## XM_034466363.1 26 105.0000 187.00000 42.00000 82.00000 115.00000 72
## XM_011444661.3 0 0.0000 8.00000 0.00000 0.00000 0.00000 0
## XM_020069058.2 1 1.0000 12.00000 4.00000 0.00000 14.00000 0
## XM_034469001.1 0 0.0000 0.00000 3.85757 0.00000 1.91961 0
## XM_011418637.3 4 10.1256 5.04397 8.00000 7.44373 4.51914 4
## XM_034449947.1 0 0.0000 0.00000 6.43103 12.84260 0.00000 0
## D57_S143 D59_S142 M46_S141 M49_S139 M90_S147 N48_S194
## XM_034466363.1 85.0000 66.97100 105.0000 93.00000000 52.00000 64.00000
## XM_011444661.3 0.0000 5.00000 0.0000 0.00000000 0.00000 0.00000
## XM_020069058.2 4.0000 4.00000 2.0000 4.00000000 1.00000 3.00000
## XM_034469001.1 0.0000 0.00000 0.0000 0.00858952 0.00000 0.00000
## XM_011418637.3 5.0149 6.99053 2.1126 8.00000000 2.15634 5.11944
## XM_034449947.1 0.0000 0.00000 0.0000 0.00000000 0.00000 0.00000
## N50_S187 N52_S184 N54_S193 N56_S192 N58_S195 N49_S185
## XM_034466363.1 18 110.00000 62.00000 10.00000 21 115.000000
## XM_011444661.3 0 0.00000 0.00000 0.00000 0 0.000000
## XM_020069058.2 4 0.00000 2.00000 2.00000 0 1.000000
## XM_034469001.1 0 0.00000 1.93555 0.00000 0 0.126773
## XM_011418637.3 3 2.08289 3.11800 2.02687 2 6.000000
## XM_034449947.1 0 0.00000 0.00000 0.00000 0 0.000000
## N51_S186 N53_S188 N55_S190 N57_S191 N59_S189
## XM_034466363.1 14 58.00000 13.00000 180.00000 94.00000
## XM_011444661.3 0 0.00000 0.00000 6.00000 0.00000
## XM_020069058.2 2 1.00000 0.00000 1.00000 2.00000
## XM_034469001.1 0 1.87094 0.00000 0.00000 0.00000
## XM_011418637.3 4 8.00000 2.07166 4.74957 4.11638
## XM_034449947.1 0 0.00000 0.00000 0.00000 0.00000Rounding to whole numbers.
countmatrix <- round(countmatrix, 0)
str(countmatrix)## 'data.frame': 73307 obs. of 24 variables:
## $ D54_S145: num 26 0 1 0 4 0 0 0 4 0 ...
## $ D56_S136: num 105 0 1 0 10 0 0 0 3 0 ...
## $ D58_S144: num 187 8 12 0 5 0 0 2 4 0 ...
## $ M45_S140: num 42 0 4 4 8 6 0 2 3 0 ...
## $ M48_S137: num 82 0 0 0 7 13 0 0 2 0 ...
## $ M89_S138: num 115 0 14 2 5 0 0 0 3 0 ...
## $ D55_S146: num 72 0 0 0 4 0 0 3 2 0 ...
## $ D57_S143: num 85 0 4 0 5 0 0 1 0 0 ...
## $ D59_S142: num 67 5 4 0 7 0 0 0 7 0 ...
## $ M46_S141: num 105 0 2 0 2 0 0 0 4 0 ...
## $ M49_S139: num 93 0 4 0 8 0 0 0 16 0 ...
## $ M90_S147: num 52 0 1 0 2 0 0 0 3 0 ...
## $ N48_S194: num 64 0 3 0 5 0 0 0 1 0 ...
## $ N50_S187: num 18 0 4 0 3 0 0 0 3 0 ...
## $ N52_S184: num 110 0 0 0 2 0 0 0 3 0 ...
## $ N54_S193: num 62 0 2 2 3 0 0 0 0 0 ...
## $ N56_S192: num 10 0 2 0 2 0 0 0 0 0 ...
## $ N58_S195: num 21 0 0 0 2 0 0 3 0 0 ...
## $ N49_S185: num 115 0 1 0 6 0 0 0 3 0 ...
## $ N51_S186: num 14 0 2 0 4 0 0 1 0 0 ...
## $ N53_S188: num 58 0 1 2 8 0 0 1 1 0 ...
## $ N55_S190: num 13 0 0 0 2 0 0 4 2 0 ...
## $ N57_S191: num 180 6 1 0 5 0 0 0 3 0 ...
## $ N59_S189: num 94 0 2 0 4 0 0 2 0 0 ...#GET DEGs BASED ON DESICATION
deseq2.colData <- data.frame(condition=factor(c(rep("control", 12), rep("desicated", 12))),
type=factor(rep("single-read", 24)))
rownames(deseq2.colData) <- colnames(data)
#Creates the DESeq dataframe
deseq2.dds <- DESeqDataSetFromMatrix(countData = countmatrix,
colData = deseq2.colData,
design = ~ condition)#Runs the DESeq
deseq2.dds <- DESeq(deseq2.dds)
#Makes a dataframe for the results
deseq2.res <- results(deseq2.dds)
deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ]vsd <- vst(deseq2.dds, blind = FALSE)
#Plots Graph based on condition
plotPCA(vsd, intgroup = "condition")
#Selecting the top 50 differentially expressed genes
res <- results(deseq2.dds)
res_ordered <- res[order(res$padj), ]
top_genes <- row.names(res_ordered)[1:50]
#Extracting the counts and normalizing
counts <- counts(deseq2.dds, normalized = TRUE)
counts_top <- counts[top_genes, ]
#Log-transform counts
log_counts_top <- log2(counts_top + 1)
#Generate a heat map (assure package is installed)
pheatmap(log_counts_top, scale = "row")
head(deseq2.res)## log2 fold change (MLE): condition desicated vs control
## Wald test p-value: condition desicated vs control
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## NM_001305288.1 0.181270 1.0453698 3.002647 0.348149 7.27728e-01
## NM_001305289.1 0.881457 -2.8119577 1.068276 -2.632239 8.48240e-03
## NM_001305290.1 145.913728 0.4580323 0.116185 3.942251 8.07203e-05
## NM_001305291.1 0.261701 0.5618449 1.587076 0.354013 7.23329e-01
## NM_001305292.1 2.902430 -1.2181330 0.763421 -1.595624 1.10573e-01
## NM_001305293.1 234.342117 0.0663449 0.131969 0.502731 6.15154e-01
## padj
## <numeric>
## NM_001305288.1 NA
## NM_001305289.1 NA
## NM_001305290.1 0.00956401
## NM_001305291.1 NA
## NM_001305292.1 0.59541971
## NM_001305293.1 0.95562321# Count number of hits with adjusted p-value less then 0.05
dim(deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ])## [1] 607 6#Visualizing Data
temp <- deseq2.res
# The main plot
plot(temp$baseMean, temp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log="x", col="gray",
main="DEG Dessication (pval <= 0.05)",
xlab="mean of normalized counts",
ylab="Log2 Fold Change")
# Getting the significant points and plotting them again so they're a different color
temp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]
points(temp.sig$baseMean, temp.sig$log2FoldChange, pch=20, cex=0.45, col="green")
# 2 FC lines
abline(h=c(-1,1), col="purple")
#Create a new plot.
# Prepping data
res_df <- as.data.frame(deseq2.res)
res_df$gene <- row.names(res_df)
#Volcano Plot
volcano_plot <- ggplot(res_df, aes(x = log2FoldChange, y = -log10(padj), color = padj < 0.05)) +
geom_point(alpha = 0.6, size = 1.5) +
scale_color_manual(values = c("yellow", "red")) +
labs(title = "Volcano Plot",
x = "Log2 Fold Change",
y = "-Log10 Adjusted P-value",
color = "Significantly\nDifferentially Expressed") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "top")
#See your plot
print(volcano_plot)
#Create a table for output
write.table(temp.sig, "../output/DEGlist.tab", sep = '\t', row.names = T)deglist <- read.csv("../output/DEGlist.tab", sep = '\t', header = TRUE)
deglist$RowName <- rownames(deglist)
deglist2 <- deglist[, c("RowName", "pvalue")]
#See your plot
print(deglist)## baseMean log2FoldChange lfcSE stat pvalue
## NM_001305290.1 145.913728 0.4580323 0.11618547 3.942251 8.072030e-05
## NM_001305310.1 59.842398 -1.5708758 0.26443342 -5.940534 2.840948e-09
## NM_001305333.1 595.728664 1.6748216 0.15635275 10.711815 8.958816e-27
## NM_001305339.1 20.326216 -1.0966532 0.26705439 -4.106479 4.017360e-05
## NM_001305362.1 319.626991 1.1898192 0.21105288 5.637540 1.724961e-08
## NM_001305373.1 213.693709 1.0596455 0.30640237 3.458346 5.435026e-04
## NM_001308854.1 1.771126 -3.6162970 1.07795990 -3.354760 7.943380e-04
## NM_001308861.1 14.305166 -0.8231046 0.24414173 -3.371421 7.478144e-04
## NM_001308879.1 70.719397 -0.8069036 0.21673317 -3.723028 1.968479e-04
## NM_001308913.1 1513.818727 8.0449715 0.79519222 10.117015 4.643630e-24
## NM_001308924.1 247.810341 5.5094321 0.68889451 7.997497 1.269734e-15
## XM_011413623.3 27.280260 -0.6730374 0.18761781 -3.587279 3.341473e-04
## XM_011413703.3 321.347449 -1.0911373 0.20660673 -5.281228 1.283206e-07
## XM_011413735.3 6.300732 4.0173765 1.19704471 3.356079 7.905600e-04
## XM_011413737.3 34.200523 -1.3610671 0.26454807 -5.144876 2.676974e-07
## XM_011413957.2 175.921820 0.4765915 0.13249896 3.596945 3.219768e-04
## XM_011413989.3 11.426496 -2.7173716 0.63040631 -4.310508 1.628797e-05
## XM_011414589.3 16.043654 0.8729150 0.24423841 3.574028 3.515308e-04
## XM_011414951.3 29.320422 -0.6679064 0.18021585 -3.706147 2.104364e-04
## XM_011415190.3 15.772808 -0.9253064 0.27722455 -3.337751 8.445948e-04
## XM_011416606.3 159.294647 1.0524600 0.22887660 4.598373 4.258033e-06
## XM_011416726.3 31.448917 0.8892852 0.15436502 5.760925 8.365440e-09
## XM_011416892.3 17.594430 0.9518297 0.26647142 3.571977 3.542968e-04
## XM_011416960.3 6.392437 -1.3092062 0.38933591 -3.362665 7.719401e-04
## XM_011417063.3 6.200573 6.2866548 1.45710078 4.314496 1.599678e-05
## XM_011417064.3 7.672047 -4.4817425 1.06250459 -4.218092 2.463779e-05
## XM_011417316.3 27.121817 -3.2710802 0.97843115 -3.343189 8.282149e-04
## XM_011417317.3 24.231104 3.2499954 0.68052618 4.775710 1.790747e-06
## XM_011417325.3 20.200242 -1.4056750 0.31531004 -4.458072 8.269997e-06
## XM_011417799.3 124.379143 -0.6478374 0.16511780 -3.923486 8.727681e-05
## XM_011417830.3 31.302133 0.7131600 0.20157900 3.537869 4.033709e-04
## XM_011417902.3 10.593744 1.3008443 0.27620668 4.709677 2.481092e-06
## XM_011417904.3 28.142449 0.6200997 0.15482389 4.005194 6.196655e-05
## XM_011417908.2 20.498601 -0.6612499 0.17439909 -3.791590 1.496858e-04
## XM_011418768.3 167.671865 -0.8020069 0.20680406 -3.878100 1.052753e-04
## XM_011418872.3 63.894510 0.7539219 0.18814636 4.007103 6.146797e-05
## XM_011418954.3 16.363651 -1.1258910 0.25919681 -4.343769 1.400589e-05
## XM_011419041.3 50.152446 -0.8000891 0.17570446 -4.553607 5.273378e-06
## XM_011419098.3 15.849417 0.8852472 0.19538894 4.530692 5.879070e-06
## XM_011419179.3 53.158210 0.9029822 0.13951381 6.472350 9.649033e-11
## XM_011419181.3 139.100482 0.5014458 0.13167252 3.808280 1.399368e-04
## XM_011419576.3 2916.663760 0.5326587 0.08935488 5.961159 2.504552e-09
## XM_011419645.3 27.729964 2.7838255 0.73809537 3.771634 1.621821e-04
## XM_011419848.3 2818.979900 -1.1076316 0.32197924 -3.440072 5.815604e-04
## XM_011419981.3 7.960624 -3.1668378 0.94544068 -3.349589 8.093151e-04
## XM_011420357.3 27.585191 1.1542525 0.17963483 6.425550 1.313937e-10
## XM_011420371.3 796.279059 4.3873193 0.56253854 7.799144 6.232830e-15
## XM_011420372.3 22.194919 2.8944654 0.76655624 3.775933 1.594096e-04
## XM_011420610.3 11.194669 -3.1982130 0.83782354 -3.817287 1.349271e-04
## XM_011420631.3 19.552538 1.6221623 0.47434115 3.419822 6.266213e-04
## XM_011420662.3 12.831164 -0.8371635 0.25083077 -3.337563 8.451658e-04
## XM_011420824.3 10.700144 2.7335903 0.59231789 4.615073 3.929574e-06
## XM_011420913.3 109.850427 0.6672415 0.19457779 3.429176 6.054161e-04
## XM_011420959.3 27.615900 1.3042797 0.21082187 6.186644 6.145874e-10
## XM_011420983.3 31.348067 -1.0301736 0.24721757 -4.167073 3.085362e-05
## XM_011421012.3 29.560399 -0.7124849 0.17531994 -4.063912 4.825696e-05
## XM_011421699.3 5.199215 2.0469087 0.57247376 3.575550 3.494920e-04
## XM_011422329.3 4.077318 3.0671139 0.81789443 3.750012 1.768261e-04
## XM_011422342.3 75.353366 -1.1586354 0.16580605 -6.987896 2.790392e-12
## XM_011422632.3 22.364262 -1.5622839 0.38169378 -4.093029 4.257736e-05
## XM_011422990.3 339.214376 0.6312277 0.17952720 3.516056 4.380082e-04
## XM_011423572.3 32.406055 0.7334437 0.18758379 3.909952 9.231433e-05
## XM_011424137.3 18.293748 0.7277872 0.17168554 4.239071 2.244469e-05
## XM_011424142.3 43.331312 -1.0131522 0.23754571 -4.265083 1.998279e-05
## XM_011424173.3 110.309485 0.7811440 0.19630691 3.979198 6.914821e-05
## XM_011424596.3 20.363069 -0.6216313 0.17124748 -3.630017 2.834022e-04
## XM_011424712.3 148.959750 -0.7445053 0.13656375 -5.451705 4.988908e-08
## XM_011425176.3 25.642814 -1.0454763 0.20267291 -5.158441 2.490140e-07
## XM_011425181.3 105.697687 -0.5126864 0.15073140 -3.401325 6.706015e-04
## XM_011425228.3 189.011741 -0.6383047 0.16631187 -3.837998 1.240413e-04
## XM_011425478.3 58.024319 -0.7046968 0.12953964 -5.440008 5.327806e-08
## XM_011425483.3 292.452300 -1.1358752 0.23106078 -4.915915 8.836866e-07
## XM_011425751.3 136.811339 -0.3844994 0.10563743 -3.639802 2.728475e-04
## XM_011425772.3 27.872181 -0.6246231 0.16277037 -3.837450 1.243186e-04
## XM_011425989.3 32.144223 1.1998495 0.23897871 5.020738 5.147332e-07
## XM_011426213.3 6.725486 1.5257928 0.37360673 4.083954 4.427574e-05
## XM_011426240.3 70.113585 0.6566019 0.16086471 4.081703 4.470697e-05
## XM_011426321.3 4.377013 -2.0150090 0.54664080 -3.686166 2.276575e-04
## XM_011426616.3 6.915439 -1.1303234 0.32670013 -3.459819 5.405379e-04
## XM_011426647.3 31.783387 -0.4422579 0.12982316 -3.406618 6.577317e-04
## XM_011426829.3 12.880886 -6.7744077 1.40285559 -4.829013 1.372115e-06
## XM_011426844.3 154.745368 1.0970831 0.33185878 3.305873 9.468090e-04
## XM_011426889.3 27.313880 -0.7232110 0.21540649 -3.357425 7.867222e-04
## XM_011427084.3 122.040470 0.6792293 0.14237919 4.770566 1.837089e-06
## XM_011427263.3 3.508810 3.4576041 0.79414446 4.353873 1.337532e-05
## XM_011427409.3 40.295984 -0.6011743 0.18119076 -3.317908 9.069434e-04
## XM_011427593.3 30.645924 -0.7777858 0.22311860 -3.485975 4.903471e-04
## XM_011428050.3 7.876202 -3.9809814 0.92305177 -4.312847 1.611654e-05
## XM_011428302.3 5.533072 2.3431260 0.59524590 3.936400 8.271304e-05
## XM_011428362.3 43.391764 -2.0368336 0.58912580 -3.457383 5.454487e-04
## XM_011428813.3 5.800234 2.2276910 0.62070059 3.588995 3.319558e-04
## XM_011429405.3 71.720707 -0.5296972 0.15840562 -3.343929 8.260076e-04
## XM_011429412.3 39.224747 -2.7213838 0.73860296 -3.684502 2.291506e-04
## XM_011429469.3 4.892169 -1.3302176 0.39895033 -3.334294 8.551627e-04
## XM_011429964.3 21.661903 -1.0798986 0.30202537 -3.575523 3.495286e-04
## XM_011430087.3 73.691905 0.6800030 0.16388157 4.149356 3.334114e-05
## XM_011430130.2 8.347677 1.5951761 0.48197998 3.309631 9.341891e-04
## XM_011430224.3 82.731689 -3.6000674 0.72566936 -4.961030 7.012039e-07
## XM_011430225.3 10.334116 22.2392897 2.94686658 7.546758 4.462257e-14
## XM_011430243.2 251.707414 -1.8984548 0.42754515 -4.440361 8.980812e-06
## XM_011430257.3 25.150217 -0.6604107 0.18807794 -3.511367 4.458083e-04
## XM_011430301.3 61.608206 -2.7947858 0.59560233 -4.692369 2.700597e-06
## XM_011430339.3 20.196492 1.6861974 0.42538698 3.963914 7.373078e-05
## XM_011430436.3 29.196322 -0.7562698 0.21520558 -3.514174 4.411238e-04
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## XM_034471632.1 28.441825 -0.6634102 0.15249948 -4.350246 1.359851e-05
## XM_034471694.1 16.000560 5.7778063 1.09487297 5.277148 1.312102e-07
## XM_034471697.1 51.144637 -2.3223696 0.43762340 -5.306776 1.115809e-07
## XM_034471759.1 45.347870 -2.3202606 0.69206747 -3.352651 8.004160e-04
## XM_034472096.1 4.032534 -1.4462135 0.42380184 -3.412476 6.437565e-04
## XM_034472105.1 5.032483 -2.6764831 0.75271005 -3.555796 3.768369e-04
## XM_034472119.1 9.614431 -1.7903815 0.53019912 -3.376810 7.333177e-04
## XM_034472167.1 81.097331 0.8902867 0.22205154 4.009370 6.088103e-05
## XM_034472465.1 13.507657 -1.4222639 0.41801052 -3.402459 6.678226e-04
## XM_034472524.1 50.276907 -1.0592198 0.26817145 -3.949786 7.822116e-05
## XM_034472602.1 190.880955 -0.8538101 0.23017914 -3.709329 2.078094e-04
## XM_034472902.1 10.572452 -2.8835879 0.71303446 -4.044107 5.252285e-05
## XM_034473158.1 11.013390 2.5458518 0.47834268 5.322234 1.025005e-07
## XM_034473208.1 18.489718 -0.8742062 0.20463688 -4.271987 1.937385e-05
## XM_034473522.1 29.486240 -0.6064607 0.15063415 -4.026051 5.672145e-05
## XM_034473839.1 5.276401 4.3854346 1.32610941 3.306993 9.430323e-04
## XM_034474036.1 3.205007 3.4148681 0.95540286 3.574270 3.512059e-04
## XM_034474325.1 16.884039 -2.2731532 0.66639581 -3.411116 6.469764e-04
## XM_034474636.1 16.284126 3.0814040 0.52937850 5.820796 5.856818e-09
## XM_034474649.1 7.361976 -1.5426527 0.45397690 -3.398086 6.785902e-04
## XM_034474866.1 2.908560 -3.1951639 0.85889970 -3.720066 1.991705e-04
## XM_034474911.1 7.064297 -2.3178348 0.60696510 -3.818728 1.341414e-04
## XM_034475156.1 15.607388 -6.5594224 1.51465368 -4.330642 1.486754e-05
## XM_034475157.1 10.591771 -4.5849124 1.09996829 -4.168222 3.069844e-05
## XM_034475818.1 35.466963 0.8525892 0.23956035 3.558975 3.723054e-04
## XM_034476264.1 10.803191 3.5787492 0.96861172 3.694720 2.201291e-04
## XM_034476388.1 12.539656 -1.0737727 0.31104304 -3.452167 5.561025e-04
## XM_034476392.1 55.019470 -10.4050746 1.88483174 -5.520426 3.381783e-08
## XM_034476517.1 2.839368 -2.3601968 0.67797982 -3.481220 4.991357e-04
## XM_034476550.1 6.926835 -1.6457134 0.42576984 -3.865265 1.109685e-04
## XM_034476829.1 7.504928 -6.2386749 1.80086564 -3.464264 5.316835e-04
## XM_034476955.1 19.843682 7.9544615 1.94852564 4.082298 4.459265e-05
## XM_034477015.1 16.412997 1.1805521 0.30594243 3.858739 1.139735e-04
## XM_034477591.1 21.478567 -1.0240577 0.29288414 -3.496460 4.714748e-04
## XM_034478134.1 3.586978 -2.6072312 0.65734494 -3.966306 7.299513e-05
## XM_034478177.1 266.617797 -0.7072268 0.17488300 -4.044000 5.254694e-05
## XM_034478323.1 9.080252 1.6366194 0.35245784 4.643447 3.426433e-06
## XM_034478793.1 4.329981 -3.0890857 0.86344200 -3.577641 3.467088e-04
## XM_034478830.1 2.106178 4.5053683 0.87777458 5.132717 2.855895e-07
## XM_034479290.1 16.209547 -3.1099001 0.75976981 -4.093214 4.254355e-05
## XM_034479293.1 14.426553 7.5116046 0.88485327 8.489096 2.082503e-17
## XM_034479294.1 39.106501 -3.6912685 0.79656999 -4.633954 3.587471e-06
## XM_034479721.1 35.413364 2.9299453 0.65677232 4.461128 8.152942e-06
## XM_034480188.1 25.475388 -1.0068068 0.30195950 -3.334245 8.553143e-04
## XM_034480222.1 145.314425 -0.9488953 0.25986961 -3.651428 2.607857e-04
## XM_034480331.1 41.207041 -4.8449731 1.33425748 -3.631213 2.820922e-04
## XM_034480902.1 15.539784 7.3423118 1.88277842 3.899722 9.630334e-05
## XM_034481530.1 11.233125 -2.6906443 0.79584175 -3.380879 7.225446e-04
## XM_034481710.1 11.207176 4.4467365 0.58035619 7.662082 1.829434e-14
## XM_034481737.1 3.524566 -1.9833119 0.48589033 -4.081810 4.468638e-05
## XM_034481740.1 126.507381 0.6182789 0.12606517 4.904439 9.369491e-07
## XM_034481838.1 3.252253 -2.4332885 0.54492958 -4.465327 7.994673e-06
## XM_034481868.1 7.732183 -1.3817673 0.37033415 -3.731137 1.906178e-04
## XM_034481890.1 4.409944 -3.0712123 0.90973492 -3.375942 7.356342e-04
## XM_034482247.1 10.359507 -1.5061610 0.36780962 -4.094947 4.222645e-05
## XM_034482265.1 7.635552 -1.2482890 0.29979794 -4.163768 3.130388e-05
## XM_034482414.1 14.401957 2.0850762 0.56059510 3.719398 1.996983e-04
## XM_034482422.1 16.719522 1.9129964 0.54655615 3.500091 4.650988e-04
## XM_034482608.1 3.467940 -1.5176011 0.45900578 -3.306279 9.454392e-04
## XM_034482887.1 50.429391 -2.4500713 0.24484003 -10.006825 1.422445e-23
## XM_034483005.1 9.534922 -5.5956109 1.66264837 -3.365481 7.641044e-04
## XM_034483031.1 29.578218 -3.2659249 0.56464392 -5.784043 7.292612e-09
## XM_034483390.1 25.551340 0.5178920 0.15401650 3.362574 7.721930e-04
## XM_034483436.1 5.313725 -5.7406331 1.28565541 -4.465141 8.001592e-06
## XM_034483547.1 4.204207 -2.9107469 0.80418626 -3.619493 2.951803e-04
## XM_034483593.1 6.955696 -2.8401392 0.80712056 -3.518854 4.334156e-04
## XR_004596035.1 3.356606 5.2900801 1.13854455 4.646353 3.378543e-06
## XR_004596351.1 39.430651 0.7003527 0.20542686 3.409256 6.514039e-04
## XR_004596743.1 74.012389 -0.6728147 0.17154622 -3.922061 8.779485e-05
## XR_004596906.1 23.759367 -0.8364580 0.21774516 -3.841454 1.223076e-04
## XR_004596944.1 3.630443 2.1990119 0.65484518 3.358064 7.849050e-04
## XR_004597797.1 483.757206 4.0416066 0.34198820 11.817971 3.152069e-32
## XR_004598294.1 50.808817 0.7583984 0.22230245 3.411561 6.459200e-04
## XR_004598549.1 18.648034 -1.6070622 0.26348149 -6.099336 1.065098e-09
## XR_004598733.1 2.595977 3.8141598 0.70120862 5.439408 5.345791e-08
## XR_004598744.1 2.595977 3.8141598 0.70120862 5.439408 5.345791e-08
## XR_004598752.1 2.595977 3.8141598 0.70120862 5.439408 5.345791e-08
## XR_004599693.1 2.804525 3.8806126 0.76860620 5.048896 4.443712e-07
## XR_004599841.1 59.938095 0.7817128 0.18904417 4.135080 3.548311e-05
## XR_004600141.1 22.797457 -0.6542898 0.19693222 -3.322411 8.924312e-04
## XR_004600166.1 15.837131 -1.9353381 0.51420759 -3.763729 1.673982e-04
## XR_004600498.1 106.619115 1.0268982 0.30518671 3.364820 7.659371e-04
## XR_004600500.1 102.706598 1.1417681 0.29997140 3.806257 1.410860e-04
## XR_004600655.1 3.149001 -1.7794190 0.53418541 -3.331089 8.650703e-04
## XR_004601051.1 81.487975 -4.4779826 0.73965868 -6.054120 1.411872e-09
## XR_004601053.1 8.632261 -5.1519422 0.87776817 -5.869366 4.374660e-09
## XR_004601648.1 278.076323 0.6996756 0.08208851 8.523429 1.548974e-17
## XR_004602054.1 16.802664 0.9810279 0.27100192 3.620004 2.945989e-04
## XR_004602139.1 11.368945 -1.1498214 0.34700915 -3.313519 9.212988e-04
## XR_004602422.1 6.333529 -2.6446981 0.64872496 -4.076763 4.566694e-05
## XR_004602535.1 43.270341 5.4069183 0.88730738 6.093625 1.103822e-09
## XR_004602542.1 35.146570 3.9367561 0.50254632 7.833618 4.740261e-15
## XR_004602869.1 108.537616 -0.6406468 0.12444992 -5.147828 2.635202e-07
## XR_004603302.1 50.320769 -0.9607862 0.28024896 -3.428331 6.073043e-04
## XR_004604213.1 196.184903 0.5488039 0.15679308 3.500179 4.649457e-04
## XR_898264.3 192.098884 0.6212871 0.16157790 3.845124 1.204913e-04
## XR_901070.3 6.517049 -2.2870580 0.56094162 -4.077177 4.558587e-05
## XR_902175.3 61.709830 2.2453423 0.42838473 5.241415 1.593495e-07
## XR_903526.3 17.623858 -1.9788198 0.52102338 -3.797948 1.458988e-04
## padj RowName
## NM_001305290.1 9.564006e-03 NM_001305290.1
## NM_001305310.1 1.646304e-06 NM_001305310.1
## NM_001305333.1 5.710707e-23 NM_001305333.1
## NM_001305339.1 5.965199e-03 NM_001305339.1
## NM_001305362.1 8.205664e-06 NM_001305362.1
## NM_001305373.1 3.471446e-02 NM_001305373.1
## NM_001308854.1 4.491469e-02 NM_001308854.1
## NM_001308861.1 4.325652e-02 NM_001308861.1
## NM_001308879.1 1.728357e-02 NM_001308879.1
## NM_001308913.1 2.114311e-20 NM_001308913.1
## NM_001308924.1 2.129945e-12 NM_001308924.1
## XM_011413623.3 2.516122e-02 XM_011413623.3
## XM_011413703.3 4.595321e-05 XM_011413703.3
## XM_011413735.3 4.483403e-02 XM_011413735.3
## XM_011413737.3 8.795928e-05 XM_011413737.3
## XM_011413957.2 2.455034e-02 XM_011413957.2
## XM_011413989.3 2.983507e-03 XM_011413989.3
## XM_011414589.3 2.546987e-02 XM_011414589.3
## XM_011414951.3 1.815431e-02 XM_011414951.3
## XM_011415190.3 4.660402e-02 XM_011415190.3
## XM_011416606.3 1.005274e-03 XM_011416606.3
## XM_011416726.3 4.232116e-06 XM_011416726.3
## XM_011416892.3 2.560578e-02 XM_011416892.3
## XM_011416960.3 4.402744e-02 XM_011416960.3
## XM_011417063.3 2.964240e-03 XM_011417063.3
## XM_011417064.3 4.089873e-03 XM_011417064.3
## XM_011417316.3 4.590759e-02 XM_011417316.3
## XM_011417317.3 4.836838e-04 XM_011417317.3
## XM_011417325.3 1.757209e-03 XM_011417325.3
## XM_011417799.3 1.007857e-02 XM_011417799.3
## XM_011417830.3 2.838021e-02 XM_011417830.3
## XM_011417902.3 6.377208e-04 XM_011417902.3
## XM_011417904.3 7.740042e-03 XM_011417904.3
## XM_011417908.2 1.473497e-02 XM_011417908.2
## XM_011418768.3 1.161016e-02 XM_011418768.3
## XM_011418872.3 7.713020e-03 XM_011418872.3
## XM_011418954.3 2.657118e-03 XM_011418954.3
## XM_011419041.3 1.226811e-03 XM_011419041.3
## XM_011419098.3 1.338412e-03 XM_011419098.3
## XM_011419179.3 7.151953e-08 XM_011419179.3
## XM_011419181.3 1.420403e-02 XM_011419181.3
## XM_011419576.3 1.478242e-06 XM_011419576.3
## XM_011419645.3 1.529310e-02 XM_011419645.3
## XM_011419848.3 3.613156e-02 XM_011419848.3
## XM_011419981.3 4.531115e-02 XM_011419981.3
## XM_011420357.3 9.517681e-08 XM_011420357.3
## XM_011420371.3 9.029671e-12 XM_011420371.3
## XM_011420372.3 1.519177e-02 XM_011420372.3
## XM_011420610.3 1.373929e-02 XM_011420610.3
## XM_011420631.3 3.818676e-02 XM_011420631.3
## XM_011420662.3 4.660402e-02 XM_011420662.3
## XM_011420824.3 9.416795e-04 XM_011420824.3
## XM_011420913.3 3.717884e-02 XM_011420913.3
## XM_011420959.3 3.997577e-07 XM_011420959.3
## XM_011420983.3 4.868152e-03 XM_011420983.3
## XM_011421012.3 6.478862e-03 XM_011421012.3
## XM_011421699.3 2.546987e-02 XM_011421699.3
## XM_011422329.3 1.614843e-02 XM_011422329.3
## XM_011422342.3 2.615746e-09 XM_011422342.3
## XM_011422632.3 6.112729e-03 XM_011422632.3
## XM_011422990.3 3.028242e-02 XM_011422990.3
## XM_011423572.3 1.054567e-02 XM_011423572.3
## XM_011424137.3 3.784959e-03 XM_011424137.3
## XM_011424142.3 3.462004e-03 XM_011424142.3
## XM_011424173.3 8.509234e-03 XM_011424173.3
## XM_011424596.3 2.241339e-02 XM_011424596.3
## XM_011424712.3 2.065019e-05 XM_011424712.3
## XM_011425176.3 8.433640e-05 XM_011425176.3
## XM_011425181.3 3.995030e-02 XM_011425181.3
## XM_011425228.3 1.286456e-02 XM_011425228.3
## XM_011425478.3 2.103470e-05 XM_011425478.3
## XM_011425483.3 2.560442e-04 XM_011425483.3
## XM_011425751.3 2.190478e-02 XM_011425751.3
## XM_011425772.3 1.286456e-02 XM_011425772.3
## XM_011425989.3 1.547696e-04 XM_011425989.3
## XM_011426213.3 6.219935e-03 XM_011426213.3
## XM_011426240.3 6.219935e-03 XM_011426240.3
## XM_011426321.3 1.934906e-02 XM_011426321.3
## XM_011426616.3 3.466403e-02 XM_011426616.3
## XM_011426647.3 3.955325e-02 XM_011426647.3
## XM_011426829.3 3.870094e-04 XM_011426829.3
## XM_011426844.3 4.980298e-02 XM_011426844.3
## XM_011426889.3 4.469592e-02 XM_011426889.3
## XM_011427084.3 4.920311e-04 XM_011427084.3
## XM_011427263.3 2.583626e-03 XM_011427263.3
## XM_011427409.3 4.858168e-02 XM_011427409.3
## XM_011427593.3 3.228996e-02 XM_011427593.3
## XM_011428050.3 2.969170e-03 XM_011428050.3
## XM_011428302.3 9.728152e-03 XM_011428302.3
## XM_011428362.3 3.476908e-02 XM_011428362.3
## XM_011428813.3 2.513087e-02 XM_011428813.3
## XM_011429405.3 4.586501e-02 XM_011429405.3
## XM_011429412.3 1.941061e-02 XM_011429412.3
## XM_011429469.3 4.683948e-02 XM_011429469.3
## XM_011429964.3 2.546987e-02 XM_011429964.3
## XM_011430087.3 5.158489e-03 XM_011430087.3
## XM_011430130.2 4.954156e-02 XM_011430130.2
## XM_011430224.3 2.050346e-04 XM_011430224.3
## XM_011430225.3 5.267446e-11 XM_011430225.3
## XM_011430243.2 1.868106e-03 XM_011430243.2
## XM_011430257.3 3.042570e-02 XM_011430257.3
## XM_011430301.3 6.885873e-04 XM_011430301.3
## XM_011430339.3 8.834389e-03 XM_011430339.3
## XM_011430436.3 3.031314e-02 XM_011430436.3
## XM_011430500.3 2.728662e-16 XM_011430500.3
## XM_011430508.3 2.702488e-02 XM_011430508.3
## XM_011430645.3 4.868152e-03 XM_011430645.3
## XM_011430696.3 3.065909e-02 XM_011430696.3
## XM_011430699.3 2.160962e-03 XM_011430699.3
## XM_011430921.3 5.500090e-03 XM_011430921.3
## XM_011430923.3 4.211241e-05 XM_011430923.3
## XM_011431087.3 1.426566e-03 XM_011431087.3
## XM_011431174.3 6.977487e-03 XM_011431174.3
## XM_011431272.3 3.042570e-02 XM_011431272.3
## XM_011431577.3 1.114021e-02 XM_011431577.3
## XM_011431655.3 8.687757e-11 XM_011431655.3
## XM_011431825.3 2.154659e-02 XM_011431825.3
## XM_011431987.3 3.870941e-03 XM_011431987.3
## XM_011431991.3 7.465003e-03 XM_011431991.3
## XM_011432386.3 2.396520e-03 XM_011432386.3
## XM_011432388.3 6.088919e-03 XM_011432388.3
## XM_011432500.3 9.309979e-03 XM_011432500.3
## XM_011432571.3 2.280999e-02 XM_011432571.3
## XM_011432647.3 1.126190e-02 XM_011432647.3
## XM_011432819.3 4.951373e-02 XM_011432819.3
## XM_011432843.3 2.614249e-02 XM_011432843.3
## XM_011432913.3 4.211285e-02 XM_011432913.3
## XM_011433143.3 1.559349e-02 XM_011433143.3
## XM_011433170.3 1.306200e-02 XM_011433170.3
## XM_011433250.3 5.162939e-03 XM_011433250.3
## XM_011433640.3 9.955042e-06 XM_011433640.3
## XM_011433641.3 1.529310e-02 XM_011433641.3
## XM_011433645.3 5.125499e-04 XM_011433645.3
## XM_011433685.2 7.740042e-03 XM_011433685.2
## XM_011433770.3 6.772854e-03 XM_011433770.3
## XM_011433782.3 4.531115e-02 XM_011433782.3
## XM_011433858.3 3.421831e-03 XM_011433858.3
## XM_011434034.3 2.114458e-02 XM_011434034.3
## XM_011434093.3 1.655800e-02 XM_011434093.3
## XM_011434186.3 1.718840e-02 XM_011434186.3
## XM_011434432.3 6.591552e-03 XM_011434432.3
## XM_011434464.3 3.984011e-02 XM_011434464.3
## XM_011434591.3 1.899353e-02 XM_011434591.3
## XM_011434867.3 6.739095e-11 XM_011434867.3
## XM_011435315.3 1.498644e-02 XM_011435315.3
## XM_011435569.3 4.832535e-02 XM_011435569.3
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## XM_034458843.1 1.052268e-03 XM_034458843.1
## XM_034458844.1 3.102143e-02 XM_034458844.1
## XM_034458868.1 6.835820e-03 XM_034458868.1
## XM_034458912.1 6.071327e-09 XM_034458912.1
## XM_034458915.1 1.614843e-02 XM_034458915.1
## XM_034459041.1 1.508021e-02 XM_034459041.1
## XM_034459403.1 6.100889e-03 XM_034459403.1
## XM_034459707.1 3.468423e-02 XM_034459707.1
## XM_034460299.1 2.186397e-02 XM_034460299.1
## XM_034460679.1 1.211336e-02 XM_034460679.1
## XM_034460779.1 3.984011e-02 XM_034460779.1
## XM_034460988.1 3.598379e-02 XM_034460988.1
## XM_034461638.1 2.213983e-03 XM_034461638.1
## XM_034461819.1 1.435497e-05 XM_034461819.1
## XM_034461937.1 4.158417e-02 XM_034461937.1
## XM_034462019.1 2.448471e-03 XM_034462019.1
## XM_034462237.1 2.455034e-02 XM_034462237.1
## XM_034462308.1 6.088919e-03 XM_034462308.1
## XM_034462375.1 6.478862e-03 XM_034462375.1
## XM_034463330.1 1.267948e-02 XM_034463330.1
## XM_034463341.1 2.190478e-02 XM_034463341.1
## XM_034463907.1 7.110593e-03 XM_034463907.1
## XM_034463916.1 5.606800e-03 XM_034463916.1
## XM_034463962.1 1.729368e-02 XM_034463962.1
## XM_034464015.1 1.100754e-02 XM_034464015.1
## XM_034464143.1 3.031314e-02 XM_034464143.1
## XM_034464492.1 2.322518e-02 XM_034464492.1
## XM_034464576.1 4.858168e-02 XM_034464576.1
## XM_034465003.1 4.158417e-02 XM_034465003.1
## XM_034465520.1 3.042570e-02 XM_034465520.1
## XM_034465546.1 7.975798e-04 XM_034465546.1
## XM_034465703.1 2.186907e-02 XM_034465703.1
## XM_034466028.1 7.212943e-04 XM_034466028.1
## XM_034466255.1 1.490770e-02 XM_034466255.1
## XM_034466358.1 3.462004e-03 XM_034466358.1
## XM_034466383.1 3.389853e-02 XM_034466383.1
## XM_034467108.1 3.516686e-02 XM_034467108.1
## XM_034467233.1 2.728662e-16 XM_034467233.1
## XM_034467405.1 1.319818e-08 XM_034467405.1
## XM_034467444.1 4.127933e-02 XM_034467444.1
## XM_034467569.1 1.868106e-03 XM_034467569.1
## XM_034467743.1 3.553161e-02 XM_034467743.1
## XM_034467848.1 1.423538e-02 XM_034467848.1
## XM_034467900.1 3.188808e-02 XM_034467900.1
## XM_034467910.1 2.025902e-06 XM_034467910.1
## XM_034468024.1 4.043783e-02 XM_034468024.1
## XM_034468098.1 3.445553e-02 XM_034468098.1
## XM_034468207.1 1.356624e-02 XM_034468207.1
## XM_034468303.1 1.075260e-02 XM_034468303.1
## XM_034468386.1 3.070958e-02 XM_034468386.1
## XM_034468456.1 2.025483e-02 XM_034468456.1
## XM_034468483.1 1.013114e-02 XM_034468483.1
## XM_034468505.1 4.721249e-02 XM_034468505.1
## XM_034469181.1 4.402744e-02 XM_034469181.1
## XM_034469434.1 9.979637e-03 XM_034469434.1
## XM_034469641.1 8.620309e-03 XM_034469641.1
## XM_034469689.1 4.721249e-02 XM_034469689.1
## XM_034469778.1 2.513775e-02 XM_034469778.1
## XM_034469790.1 2.875053e-06 XM_034469790.1
## XM_034469941.1 4.235783e-02 XM_034469941.1
## XM_034470068.1 3.248099e-02 XM_034470068.1
## XM_034470240.1 1.460811e-04 XM_034470240.1
## XM_034470321.1 1.014203e-04 XM_034470321.1
## XM_034470338.1 2.592259e-02 XM_034470338.1
## XM_034470402.1 4.789511e-02 XM_034470402.1
## XM_034470406.1 2.063771e-02 XM_034470406.1
## XM_034470452.1 4.880913e-03 XM_034470452.1
## XM_034470781.1 8.320025e-03 XM_034470781.1
## XM_034471023.1 3.130593e-02 XM_034471023.1
## XM_034471029.1 1.214904e-02 XM_034471029.1
## XM_034471052.1 2.546987e-02 XM_034471052.1
## XM_034471087.1 1.174977e-02 XM_034471087.1
## XM_034471156.1 5.411089e-03 XM_034471156.1
## XM_034471171.1 4.789511e-02 XM_034471171.1
## XM_034471181.1 1.286456e-02 XM_034471181.1
## XM_034471468.1 4.910307e-02 XM_034471468.1
## XM_034471632.1 2.595280e-03 XM_034471632.1
## XM_034471694.1 4.646590e-05 XM_034471694.1
## XM_034471697.1 4.135239e-05 XM_034471697.1
## XM_034471759.1 4.499270e-02 XM_034471759.1
## XM_034472096.1 3.908154e-02 XM_034472096.1
## XM_034472105.1 2.692947e-02 XM_034472105.1
## XM_034472119.1 4.262933e-02 XM_034472119.1
## XM_034472167.1 7.700001e-03 XM_034472167.1
## XM_034472465.1 3.985925e-02 XM_034472465.1
## XM_034472524.1 9.309979e-03 XM_034472524.1
## XM_034472602.1 1.802147e-02 XM_034472602.1
## XM_034472902.1 6.835820e-03 XM_034472902.1
## XM_034473158.1 3.843408e-05 XM_034473158.1
## XM_034473208.1 3.421831e-03 XM_034473208.1
## XM_034473522.1 7.289621e-03 XM_034473522.1
## XM_034473839.1 4.980298e-02 XM_034473839.1
## XM_034474036.1 2.546987e-02 XM_034474036.1
## XM_034474325.1 3.912795e-02 XM_034474325.1
## XM_034474636.1 3.163873e-06 XM_034474636.1
## XM_034474649.1 4.030905e-02 XM_034474649.1
## XM_034474866.1 1.739012e-02 XM_034474866.1
## XM_034474911.1 1.370306e-02 XM_034474911.1
## XM_034475156.1 2.787402e-03 XM_034475156.1
## XM_034475157.1 4.868152e-03 XM_034475157.1
## XM_034475818.1 2.666543e-02 XM_034475818.1
## XM_034476264.1 1.891093e-02 XM_034476264.1
## XM_034476388.1 3.516686e-02 XM_034476388.1
## XM_034476392.1 1.440874e-05 XM_034476392.1
## XM_034476517.1 3.253262e-02 XM_034476517.1
## XM_034476550.1 1.202989e-02 XM_034476550.1
## XM_034476829.1 3.437285e-02 XM_034476829.1
## XM_034476955.1 6.219935e-03 XM_034476955.1
## XM_034477015.1 1.214904e-02 XM_034477015.1
## XM_034477591.1 3.130593e-02 XM_034477591.1
## XM_034478134.1 8.812503e-03 XM_034478134.1
## XM_034478177.1 6.835820e-03 XM_034478177.1
## XM_034478323.1 8.465679e-04 XM_034478323.1
## XM_034478793.1 2.546987e-02 XM_034478793.1
## XM_034478830.1 9.288071e-05 XM_034478830.1
## XM_034479290.1 6.112729e-03 XM_034479290.1
## XM_034479293.1 4.424902e-14 XM_034479293.1
## XM_034479294.1 8.795376e-04 XM_034479294.1
## XM_034479721.1 1.743963e-03 XM_034479721.1
## XM_034480188.1 4.683948e-02 XM_034480188.1
## XM_034480222.1 2.134507e-02 XM_034480222.1
## XM_034480331.1 2.236528e-02 XM_034480331.1
## XM_034480902.1 1.084587e-02 XM_034480902.1
## XM_034481530.1 4.225494e-02 XM_034481530.1
## XM_034481710.1 2.332309e-11 XM_034481710.1
## XM_034481737.1 6.219935e-03 XM_034481737.1
## XM_034481740.1 2.690310e-04 XM_034481740.1
## XM_034481838.1 1.723154e-03 XM_034481838.1
## XM_034481868.1 1.706564e-02 XM_034481868.1
## XM_034481890.1 4.262933e-02 XM_034481890.1
## XM_034482247.1 6.112729e-03 XM_034482247.1
## XM_034482265.1 4.890770e-03 XM_034482265.1
## XM_034482414.1 1.739012e-02 XM_034482414.1
## XM_034482422.1 3.102143e-02 XM_034482422.1
## XM_034482608.1 4.980298e-02 XM_034482608.1
## XM_034482887.1 5.667021e-20 XM_034482887.1
## XM_034483005.1 4.402744e-02 XM_034483005.1
## XM_034483031.1 3.769794e-06 XM_034483031.1
## XM_034483390.1 4.402744e-02 XM_034483390.1
## XM_034483436.1 1.723154e-03 XM_034483436.1
## XM_034483547.1 2.305879e-02 XM_034483547.1
## XM_034483593.1 3.010890e-02 XM_034483593.1
## XR_004596035.1 8.412572e-04 XR_004596035.1
## XR_004596351.1 3.932111e-02 XR_004596351.1
## XR_004596743.1 1.010180e-02 XR_004596743.1
## XR_004596906.1 1.278817e-02 XR_004596906.1
## XR_004596944.1 4.467230e-02 XR_004596944.1
## XR_004597797.1 3.348758e-28 XR_004597797.1
## XR_004598294.1 3.912795e-02 XR_004598294.1
## XR_004598549.1 6.656238e-07 XR_004598549.1
## XR_004598733.1 2.103470e-05 XR_004598733.1
## XR_004598744.1 2.103470e-05 XR_004598744.1
## XR_004598752.1 2.103470e-05 XR_004598752.1
## XR_004599693.1 1.388529e-04 XR_004599693.1
## XR_004599841.1 5.411089e-03 XR_004599841.1
## XR_004600141.1 4.804657e-02 XR_004600141.1
## XR_004600166.1 1.555486e-02 XR_004600166.1
## XR_004600498.1 4.402744e-02 XR_004600498.1
## XR_004600500.1 1.423538e-02 XR_004600500.1
## XR_004600655.1 4.721249e-02 XR_004600655.1
## XR_004601051.1 8.490410e-07 XR_004601051.1
## XR_004601053.1 2.446126e-06 XR_004601053.1
## XR_004601648.1 3.797608e-14 XR_004601648.1
## XR_004602054.1 2.305879e-02 XR_004602054.1
## XR_004602139.1 4.910307e-02 XR_004602139.1
## XR_004602422.1 6.273693e-03 XR_004602422.1
## XR_004602535.1 6.765578e-07 XR_004602535.1
## XR_004602542.1 7.554080e-12 XR_004602542.1
## XR_004602869.1 8.748869e-05 XR_004602869.1
## XR_004603302.1 3.722308e-02 XR_004603302.1
## XR_004604213.1 3.102143e-02 XR_004604213.1
## XR_898264.3 1.267948e-02 XR_898264.3
## XR_901070.3 6.273693e-03 XR_901070.3
## XR_902175.3 5.520423e-05 XR_902175.3
## XR_903526.3 1.453152e-02 XR_903526.3#Check datable
datatable(deglist)1 What genes are these?
1.1 The gene enrichment analysis
gene_deg_stat <- res_df %>%
mutate(degstaus = ifelse(padj < 0.05, 1, 0))# Read the FASTA file
fasta_data <- readDNAStringSet("../data/rna.fna")
# Calculate gene lengths
gene_lengths <- width(fasta_data)
# Extract gene names/IDs from sequence IDs
gene_names <- sapply(names(fasta_data), function(x) strsplit(x, " ")[[1]][1])
# Create a data frame with gene IDs and lengths
gene_lengths_df <- data.frame(geneID = gene_names, length = gene_lengths)---
title: "3.0-RNASeq"
author: Susan Garcia
date: "2023-04-14"
output: 
  html_document:
    theme: cosmo
    highlight: tango
    toc: true
    toc_float: true
    number_sections: true
    code_folding: show
    code_download: true
---

```{r setup, include=FALSE}
library(knitr)
library(tidyverse)
library(kableExtra)
library(DESeq2)
library(pheatmap)
library(RColorBrewer)
library(data.table)
library(DT)
library(Biostrings)
knitr::opts_chunk$set(
  echo = TRUE,         # Display code chunks
  eval = FALSE,         # Evaluate code chunks
  warning = FALSE,     # Hide warnings
  message = FALSE,     # Hide messages
  fig.width = 6,       # Set plot width in inches
  fig.height = 4,      # Set plot height in inches
  fig.align = "center" # Align plots to the center
)
```


#Kallisto Assignment: 

Creating a workflow for RNA Seq

##Downloading reference

```{r, engine='bash'}
# Setting Directory and downloading data
cd ../data
curl --insecure -O https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/rna.fna
```

##Kallisto is installed on the Raven server.

```{r, engine='bash'}
/home/shared/kallisto/kallisto \
index -i \
../data/cgigas_roslin_rna.index \
../data/rna.fna
```

##Downloading sequence reads from the server.

```{r, engine='bash'}
#Setting Directory
cd ../data 

#Downloading fastq files using wget
wget --recursive --no-parent --no-directories \
--no-check-certificate \
--accept '*.fastq.gz' \
https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/nopp/
```

#Running Kallisto on fastq for alignment.
```{r, engine='bash'}
#Creating kallisto_01 folder before alignment/run.
mkdir ../output/kallisto_01

find ../data/*fastq.gz \
| xargs basename -s _L001_R1_001.fastq.gz | xargs -I{} /home/shared/kallisto/kallisto \
quant -i ../data/cgigas_roslin_rna.index \
-o ../output/kallisto_01/{} \
-t 40 \
--single -l 100 -s 10 ../data/{}_L001_R1_001.fastq.gz
```

##COmbining output files for visualization

```{r, engine='bash'}
perl /home/shared/trinityrnaseq-v2.12.0/util/abundance_estimates_to_matrix.pl \
--est_method kallisto \
    --gene_trans_map none \
    --out_prefix ../output/kallisto_01 \
    --name_sample_by_basedir \
    ../output/kallisto_01/D54_S145/abundance.tsv \
    ../output/kallisto_01/D56_S136/abundance.tsv \
    ../output/kallisto_01/D58_S144/abundance.tsv \
    ../output/kallisto_01/M45_S140/abundance.tsv \
    ../output/kallisto_01/M48_S137/abundance.tsv \
    ../output/kallisto_01/M89_S138/abundance.tsv \
    ../output/kallisto_01/D55_S146/abundance.tsv \
    ../output/kallisto_01/D57_S143/abundance.tsv \
    ../output/kallisto_01/D59_S142/abundance.tsv \
    ../output/kallisto_01/M46_S141/abundance.tsv \
    ../output/kallisto_01/M49_S139/abundance.tsv \
    ../output/kallisto_01/M90_S147/abundance.tsv \
    ../output/kallisto_01/N48_S194/abundance.tsv \
    ../output/kallisto_01/N50_S187/abundance.tsv \
    ../output/kallisto_01/N52_S184/abundance.tsv \
    ../output/kallisto_01/N54_S193/abundance.tsv \
    ../output/kallisto_01/N56_S192/abundance.tsv \
    ../output/kallisto_01/N58_S195/abundance.tsv \
    ../output/kallisto_01/N49_S185/abundance.tsv \
    ../output/kallisto_01/N51_S186/abundance.tsv \
    ../output/kallisto_01/N53_S188/abundance.tsv \
    ../output/kallisto_01/N55_S190/abundance.tsv \
    ../output/kallisto_01/N57_S191/abundance.tsv \
    ../output/kallisto_01/N59_S189/abundance.tsv
```

#Run DESeq2

```{r, eval=T}
countmatrix <- read.delim("../output/kallisto_01.isoform.counts.matrix", header = TRUE, sep = '\t')
rownames(countmatrix) <- countmatrix$X
countmatrix <- countmatrix[,-1]


head(countmatrix)
```

Rounding to whole numbers.

```{r, eval=T}
countmatrix <- round(countmatrix, 0)
str(countmatrix)
```

#GET DEGs BASED ON DESICATION
```{r, eval=T}
deseq2.colData <- data.frame(condition=factor(c(rep("control", 12), rep("desicated", 12))), 
                             type=factor(rep("single-read", 24)))

rownames(deseq2.colData) <- colnames(data)
#Creates the DESeq dataframe
deseq2.dds <- DESeqDataSetFromMatrix(countData = countmatrix,
                                     colData = deseq2.colData, 
                                     design = ~ condition)
```

```{r, eval=T}
#Runs the DESeq
deseq2.dds <- DESeq(deseq2.dds)

#Makes a dataframe for the results
deseq2.res <- results(deseq2.dds)
deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ]
```

```{r PCA, eval=TRUE}
vsd <- vst(deseq2.dds, blind = FALSE)
#Plots Graph based on condition
plotPCA(vsd, intgroup = "condition")
```

```{r heatmap, eval=TRUE}
#Selecting the top 50 differentially expressed genes
res <- results(deseq2.dds)
res_ordered <- res[order(res$padj), ]
top_genes <- row.names(res_ordered)[1:50]

#Extracting the counts and normalizing
counts <- counts(deseq2.dds, normalized = TRUE)
counts_top <- counts[top_genes, ]

#Log-transform counts
log_counts_top <- log2(counts_top + 1)

#Generate a heat map (assure package is installed)
pheatmap(log_counts_top, scale = "row")

head(deseq2.res)
```

```{r, eval=TRUE}

# Count number of hits with adjusted p-value less then 0.05

dim(deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ])
```

#Visualizing Data

```{r, eval=TRUE}

temp <- deseq2.res

# The main plot
plot(temp$baseMean, temp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log="x", col="gray",
     main="DEG Dessication  (pval <= 0.05)",
     xlab="mean of normalized counts",
     ylab="Log2 Fold Change")

# Getting the significant points and plotting them again so they're a different color
temp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]
points(temp.sig$baseMean, temp.sig$log2FoldChange, pch=20, cex=0.45, col="green")

# 2 FC lines
abline(h=c(-1,1), col="purple")
```

#Create a new plot.
```{r, eval=TRUE}

# Prepping data
res_df <- as.data.frame(deseq2.res)
res_df$gene <- row.names(res_df)

#Volcano Plot
volcano_plot <- ggplot(res_df, aes(x = log2FoldChange, y = -log10(padj), color = padj < 0.05)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("yellow", "red")) +
  labs(title = "Volcano Plot",
       x = "Log2 Fold Change",
       y = "-Log10 Adjusted P-value",
       color = "Significantly\nDifferentially Expressed") +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "top")

#See your plot
print(volcano_plot)

```

```{r, eval=TRUE}
#Create a table for output
write.table(temp.sig, "../output/DEGlist.tab", sep = '\t', row.names = T)
```

```{r, eval=TRUE}
deglist <- read.csv("../output/DEGlist.tab", sep = '\t', header = TRUE)
deglist$RowName <- rownames(deglist)
deglist2 <- deglist[, c("RowName", "pvalue")] 

#See your plot
print(deglist)

#Check datable
datatable(deglist)
```

# What genes are these?
## The gene enrichment analysis

```{r, eval=TRUE}
gene_deg_stat <- res_df %>%
  mutate(degstaus = ifelse(padj < 0.05, 1, 0))
```

```{r, eval=TRUE}

# Read the FASTA file
fasta_data <- readDNAStringSet("../data/rna.fna")


# Calculate gene lengths
gene_lengths <- width(fasta_data)


# Extract gene names/IDs from sequence IDs
gene_names <- sapply(names(fasta_data), function(x) strsplit(x, " ")[[1]][1])


# Create a data frame with gene IDs and lengths
gene_lengths_df <- data.frame(geneID = gene_names, length = gene_lengths)
```



