I am working with 6 RNA libraries known as KDR1, KDR2, KDR3 wild-type zebrafish embryos, and three mutant embryos let71,let72,let7-3 mi -RNA mutant zebrafish embryos. I am using the DEseq2 pipeline for differential gene expressions. When I am trying to plot the PCA results I am getting only 5 data points. Conditions used are mutant v wild type. The code snippet used is

library(DESeq2)

library(pheatmap)

# library(pheatmap)

# Set working Directory
# setwd(dir)

# specify the name input directory that contains filtered count files

directory <-  "E:\\KDR-mir24 16.02.2021"

# specify which files to read in using a list. files 
# select those files which contain the string 'filtered.txt' using grep

samplefiles <- grep('filtered.txt', list.files(directory), value=TRUE)
# To keep files in the same order
samplefiles1 <-sort(samplefiles,decreasing = TRUE)
# print the value of the variable to check that it is correct

sample files

# View() function

View(samplefiles)

# use sub to remove 'filtered.txt' from the filenames to obtain the sample names

samplenames <- sub('filtered.txt','',samplefiles)
samplenames

# input the sample conditions manually

sampleconditions <- c('wt','wt','wt','mut','mut','mut')
sampleconditions

# create the sampleTable using the data.frame function
# data frame has three variables (columns): samplename, filename, and condition


sampletable <- data.frame(samplename = samplenames,
                          filename = samplefiles,
                          condition = sampleconditions)
View(sampletable)

# build the DESeqDataSet using the following function

dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampletable,
                                  directory = directory,
                                  design = ~ condition)
dds

# set the factor levels using the relevel function, and specifying the reference level

dds$condition <- relevel(dds$condition, ref = 'wt')

# standard differential expression analysis steps are wrapped into a single function, DESeq

dds <- DESeq(dds, betaPrior = TRUE)

dds

res <- results(dds)

res

plotMA(res, ylim=c(-2, 2))

rld <- rlog(dds, blind=FALSE)

plotPCA(rld, intgroup=c("condition")).