Hello, I need some help regarding this group of RNA-seq data sets on different stress conditions (say A, B and C). The authors who actually performed these experiments performed cluster analysis using FPKM and normalized across all the stress conditions. In the paper, they mentioned they have kept all the experimental conditions same and varied only the stress. I am not interested in DEG analysis as of now, I only want to export the vst-transformed data for downstream analysis (cluster, co-expression network analysis, etc). How do I modify the code ? Kindly help .
Data:
Stress A
Ctrl_A_rep1
Ctrl_A_rep2
Str_A_1hr_rep1
Str_A_1hr_rep2
Str_A_6hr_rep1
Str_A_6hr_rep2
Stress B
Ctrl_B_rep1
Ctrl_B_rep2
Str_B_1hr_rep1
Str_B_1hr_rep2
Str_B_6hr_rep1
Str_B_6hr_rep2
Str_B_6hr_rep3
Stress C
Ctrl_Crep1
Ctrl_C_rep2
Str_C_1hr_rep1
Str_C_1hr_rep2
Str_C_6hr_rep1
Str_C_6hr_rep2
R code I tried as of now:
library(DESeq2)
count_table<-read.table("all_expr.txt", header=T, row.names=1, sep="\t")
batch<-factor(c(rep("stress_A",6), rep("stress_B",7), rep("stress_C",6)
exp_design<-data.frame(row.names=colnames(count_table), condition = c("ctrl_A", "ctrl_A", "A_1hr", "A_1hr", "A_6hr", "A_6hr", "ctrl_B", "ctrl_B", "B_1hr", "B_1hr", "B_6hr", "B_6hr", "B_6hr", "ctrl_C", "ctrl_C", "C_1hr", "C_1hr", "C_6hr", "C_6hr"), batch)
conditions=exp_design$condition
ds <- DESeqDataSetFromMatrix(countData=count_table, exp_design, formula(~batch+condition))
I am getting this error:
error:
Error in checkFullRank(modelMatrix) :
the model matrix is not full rank, so the model cannot be fit as specified.
One or more variables or interaction terms in the design formula are linear
combinations of the others and must be removed.
