Ok, thank you. This is everything from my session:

> library("ggplot2")
> library("dplyr")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

> library("pheatmap")
> library("RColorBrewer")
> library("PoiClaClu")
> library("glmpca")
> library("DESeq2")
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:dplyr':

    combine, intersect, setdiff, union

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname,
    do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min


Attaching package: 'S4Vectors'

The following objects are masked from 'package:dplyr':

    first, rename

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    expand.grid, I, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following objects are masked from 'package:dplyr':

    collapse, desc, slice

The following object is masked from 'package:grDevices':

    windows

Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'matrixStats'

The following object is masked from 'package:dplyr':

    count


Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs,
    colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps,
    colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds,
    colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates,
    colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians,
    colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs,
    rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2,
    rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads,
    rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite
    Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

> library(edgeR)
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:DESeq2':

    plotMA

The following object is masked from 'package:BiocGenerics':

    plotMA

> 
> 
> ## Create a matrix
> 
> countdata<-read.csv("All.csv", header=TRUE, row.names=1)
> sampleInfo<-read.csv("SI_All.csv", header=TRUE)
> matMatrix<-as.matrix(countdata)
> rownames <- countdata[,1]
> 
> ## Create A DESeq2 dataset
> 
> dds <- DESeqDataSetFromMatrix(countData =matMatrix,colData = sampleInfo, design= ~ Group)
Warning message:
In DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
> 
> ## Get normalized values
> 
> dds_norm <- estimateSizeFactors(dds)
> normalized_counts <- counts(dds_norm, normalized=TRUE)
> write.csv(normalized_counts, file="All_norm_counts.csv")
> 
> ## Run DE analysis
> 
> dds <- DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
> res <- results(dds)
> 
> ## Visualization of the data
> 
> DESeq2::plotMA(res,ylim=c(-8,8))
> 
> sampleDists <- dist(t(assay(dds)))
> library("RColorBrewer")
> sampleDistMatrix <- as.matrix(sampleDists)
> rownames(sampleDistMatrix) <- paste(dds$Sample, dds$Group, sep="-")
> colnames(sampleDistMatrix) <- NULL
> colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
> pheatmap(sampleDistMatrix,
+          clustering_distance_rows=sampleDists,
+          clustering_distance_cols=sampleDists,
+          col=colors)
> 
> rdds <- rlog(dds)
> pcaData <- DESeq2::plotPCA(rdds, intgroup=c("Passage","Day"), returnData=TRUE)
> percentVar <- round(100 * attr(pcaData, "percentVar"))
> ggplot(pcaData, aes(PC1, PC2,color=factor(rdds$Passage),shape=factor(rdds$Day))) +
+     geom_point(size=3) +
+     xlab(paste0("PC1: ",percentVar[1],"% variance")) +
+     ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
+     coord_fixed()
> head(rdds)
class: DESeqTransform 
dim: 6 22 
metadata(1): version
assays(1): ''
rownames(6): SEC24B-AS1 A1BG ... A2M A2ML1
rowData names(35): baseMean baseVar ... dispFit rlogIntercept
colnames(22): P1_0 P1_5 ... P5_5 P5_10
colData names(5): Sample Group Passage Day sizeFactor
> head(res)
log2 fold change (MLE): Group E vs A 
Wald test p-value: Group E vs A 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat    pvalue      padj
           <numeric>      <numeric> <numeric> <numeric> <numeric> <numeric>
SEC24B-AS1   4.11519       1.498284  0.948900  1.578969  0.114343  0.343207
A1BG        11.27725      -0.241909  0.590541 -0.409640  0.682070  0.869248
A1CF         0.00000             NA        NA        NA        NA        NA
GGACT        1.68097      -0.491430  1.309797 -0.375196  0.707515  0.882295
A2M        115.00317       0.391490  1.302733  0.300514  0.763785  0.907665
A2ML1        0.00000             NA        NA        NA        NA        NA
> head(sampleInfo)
  Sample Group Passage Day
1   P1_0     A       A   0
2   P2_0     A       B   0
3   P3_0     A       C   0
4   P5_0     A       E   0
5   P1_5     B       A   5
6   P2_5     B       B   5
> head(pcaData)
             PC1        PC2 group Passage Day  name
P1_0  -41.403120  12.592169  A: 0       A   0  P1_0
P1_5  -17.307177 -12.442635  B: 0       B   0  P1_5
P1_10   3.735443  -7.785943  C: 0       C   0 P1_10
P1_15  18.733162   1.016479  E: 0       E   0 P1_15
P1_30  30.117248  16.528119  A: 5       A   5 P1_30
P2_0  -44.477247  19.774373  B: 5       B   5  P2_0
> vignette(DESeq2)
Error: object 'DESeq2' not found
> vignette("DESeq2")
> ?plotPCA