Hi All,

I'm very new to using R, and I'm trying to use it to perform an analysis of RNA-seq data, to look at differential expression. I've done a few online tutorials to try and build up some background, but I'm hitting a roadblock. 

Basically, when I run my script, I get the following error:

Error in DESeqDataSetFromMatrix(countData = countData, colData = colData, : ncol(countData) == nrow(colData) is not TRUE

I realise that this error means that my colData and countData don't quite match up. I performed the following, which showed that I have 7 columns in my countData and 6 rows in my colData file:

dim(colData)
dim(countData)

However, I cannot see how to alter the colData file to make it match up. Or how to get my countData file to skip the first row containing gene IDs, so that it only counts 6 columns. 

My countData.csv file looks like this:

GeneID                         Control1     Control2     Control3     LPO1    LPO2     LPO3

ENSG01254216542                1.1             1.3         1.14         7.0    7.5      7.2

The colData.csv file looks like this:

                     Condition         type

Control1           untreated         paired-read

Control2           untreated         paired-read        

Control3           untreated         paired-read

LPO1               treated             paired-read

LPO2               treated             paired-read

LPO3               treated             paired-read

library(DESeq2)

data <-read.csv("//csce.datastore.ed.ac.uk//Control1.output2.csv") 

se <- data

countData <- as.matrix(se,row.names="Geneid", header = TRUE, sep = '\t', row.names = 1)
colData <- read.csv("//csce.datastore.ed.ac.uk//csce//biology//users//s0348375//Win7//Desktop//colData2.csv", row.names=1)

colData <- colData[,c("condition","type")]
colnames(countData) <- NULL
dds <- (countData = countData,
                              colData = colData,
                              design = ~ condition)
dds <- dds[ rowSums(counts(dds)) > 1, ]
dds<-DESeq(dds)

I was wondering if anyone may be able to help with a solution? I'm sure that it's something very obvious, but any help would be very much appreciated, as I don't have access to bioinformatic support. 

Thanks,

Matthew

Is this Affymetric gene expression data? If so you should not use an RNA-seq method. You should use limma's methods for microarray data.