I am using DESeq2 for differential expression analysis. But I got big different results between the two versions while using the same set of data as input and the same set of r code for data processing for the two DESeq2 versions. I would like to ask what is the key point changed between the two versions to make the results so different.

I used DESeq2 package version 1.14.1 and pasilla version 1.2.0 two months ago to get 98 and 4200 differential expression transcripts, while I installed a new version of R in another computer and applied the updated DESeq2 package version 1.16.1 and pasilla 1.4.0 recently and I got 1988 and 8313 differential expressed transcripts. And most of the 98 and 4200 are covered by the 1988 and 8313 DE transcripts, respectively. 

pasCts <- system.file( "transcript_count_matrix.csv",package="pasilla", mustWork=TRUE)

pasAnno <- system.file( "phenotypic_data.csv", package="pasilla", mustWork=TRUE)

a = read.csv(pasCts,row.names=1)

countData <- as.matrix(a)

colData <- read.csv(pasAnno, row.names=1)

all(rownames(colData) %in% colnames(countData))

all(rownames(colData) == colnames(countData))

dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ clone + block + condition)

dds

featureData <- data.frame(transcript=rownames(countData))

head(featureData)

(mcols(dds) <- DataFrame(mcols(dds), featureData))

dds$condition <- factor(dds$condition, levels=c("C","D"))

dds$condition

dds <- DESeq(dds, fitType = c("local"))

res <- results(dds, alpha = 0.05, lfcThreshold = 0.5)

sum(res$padj < 0.05, na.rm=TRUE)

Let me know if you need more information and thanks for any help!

Jingjia