i'm not getting any significant differentially expressed genes. i'm analysisng the following data set- https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137081 which has also been used in the publication https://pubmed.ncbi.nlm.nih.gov/31708475/

they have found 3,008 degs when comparing ground vs flight conditions but the code below is giving none...

what should i do? this is code i'm using. please do tell if i need to include anything else...

sessionInfo( )

```

library(DESeq2) library(pheatmap) library(RColorBrewer) setwd("H:/My Drive/jnu/nasa cancer/osd_258")

count_table<- read.csv("osd_258_counts_1ofeach.tsv", sep='\t', row.names=1) head(count_table) FB1 GB1 FB2 GB2 A1BG 5.646248 4.531114 4.847910 6.022161 A1BG-AS1 7.176647 7.013026 6.609566 6.895027 A1CF 2.782489 5.668356 7.737875 7.485374 A2M 14.773602 14.361068 13.320749 12.432167 A2M-AS1 7.131630 6.476938 5.537371 5.876339 A2ML1 3.298944 2.041441 4.566462 2.041441 dim(count_table) [1] 56272 4 count_table <- round(count_table)

sample_info<- read.csv('osd_258_meta_1ofeach.tsv', sep='\t', row.names=1) sample_info group FB1 flight GB1 ground FB2 flight GB2 ground dim(sample_info) [1] 4 1

factors<- factor(sample_info$group) factors [1] flight ground flight ground Levels: flight ground group<- unique(sample_info$group) group [1] "flight" "ground" group<-rev(group) sample_info$group<- factors

dds<- DESeqDataSetFromMatrix(countData = count_table,

• colData=sample_info,
• design= ~group) converting counts to integer mode

dds$group<-relevel(dds$group, ref="ground")

dim(dds) [1] 56272 4

statistical test???

dds<- DESeq(dds)
estimating size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing

to get the results

deseq_result<- results(dds) deseq_result<-as.data.frame(deseq_result) class(deseq_result)
[1] "data.frame" head(deseq_result)
baseMean log2FoldChange lfcSE stat pvalue padj A1BG 5.50 1.198038e-06 0.9870063 1.213810e-06 0.9999990 0.9999994 A1BG-AS1 7.00 1.112173e-06 0.8901004 1.249492e-06 0.9999990 0.9999994 A1CF 6.00 -2.410066e-01 0.9734478 -2.475804e-01 0.8044590 0.9999994 A2M 13.50 1.069160e-01 0.6915243 1.546092e-01 0.8771294 0.9999994 A2M-AS1 6.25 1.154783e-01 0.9342030 1.236115e-01 0.9016229 0.9999994 A2ML1 3.00 9.999992e-01 1.3289759 7.524585e-01 0.4517754 0.9999994

rearrangement

deseq_result$GeneName<- row.names(deseq_result) names(deseq_result) [1] "baseMean" "log2FoldChange" "lfcSE" "stat" "pvalue" "padj" "GeneName"

deseq_result <-subset(deseq_result, select=c("GeneName", "padj","pvalue", "lfcSE", "stat","log2FoldChange", "baseMean"))

deg<- subset(deseq_result, padj<0.05) dim(deg) #0 7 [1] 0 7 dim(deseq_result) #56272 7 [1] 56272 7 head(deg) [1] GeneName padj pvalue lfcSE stat log2FoldChange baseMean

<0 rows> (or 0-length row.names)

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