Question

Factor levels and Differential Expression analysis: Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels

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bak16 • 0

@bak16-23268

Last seen 4.0 years ago

Hi,

I am a relatively novice bioinformatician and want to perform differential expression on my RNASeq data (12 treatment groups with 6 replicates of each). I have built a sample table:

Sample_Table <- data.frame(Sample = samples, 
                           FileName = files, 
                           Treatment = Treatment, 
                           Cell = Cell, 
                           Type = Type, 
                           Replicate = Replicate)

and then the DESeq dataset from my HTSeqcount outputs:

dds <- DESeqDataSetFromHTSeqCount(sampleTable = Sample_Table,
                                            directory = directory,
                                            design = ~Treatment)

Then, I have tried to filter, subset my dataset and run differential expression on the subset:

dds_V <- dds[,dds$Treatment=="Vehicle"]
keep <- rowSums (counts (dds_V, normalized = TRUE)>=50) >=3
dds_V <- dds_V[keep,]
dds_V$Treatment <- droplevels(dds_V$Treatment)
dds_V$Cell <- relevel(dds_V$Cell,ref = "T") 
dds_V <- DESeq(dds_V)

Unfortunately this returns the message: 'Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels'.

I have also tried:

dds_V <- dds[,dds$Treatment=="Vehicle"]
keep <- rowSums (counts (dds_V, normalized = TRUE)>=50) >=3
dds_V <- dds_V[keep,]
dds_V$Cell <- factor(dds_V$Cell, levels = c("T","M"))
dds_V <- DESeq(dds_V)

which returns this: 'Error in designAndArgChecker(object, betaPrior) : full model matrix is less than full rank'.

I understand there seems to be a problem with recognising the 2 factors but I can't seem to find a way round it. I'd be really grateful if anyone has any suggestions on how to proceed.

Thank you

deseq2 • 2.6k views

ADD COMMENT • link updated 4.0 years ago by Michael Love 41k • written 4.0 years ago by bak16 • 0

score 1 · Answer 1 · 2020-04-06

1

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Michael Love 41k

@mikelove

Last seen 3 hours ago

United States

DESeq2 is for differential analysis.

DE analysis can't be performed without a minimum of two groups as specified by the design.

This is the problematic line where you remove all samples except for vehicle.

dds_V <- dds[,dds$Treatment=="Vehicle"]

If you did this subsetting before DESeqDataSet, it would also tell you that you can't perform DE on samples without any difference among them, as specified by your design.

ADD COMMENT • link 4.0 years ago Michael Love 41k

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Thanks so much for this, Michael.

The 'vehicle' samples actually comprise 2 different cell types, which are what I'd like to look at the differential expression of. I labelled my sample table as follows:

samples <- gsub("htseq_|.txt", "",files)
Treatment <- ifelse(grepl("DSSV",samples),"Vehicle", 
                    (ifelse(grepl("DSSE2",samples), "E2", 
                    (ifelse(grepl("VP",samples), "P",
                    (ifelse(grepl("VR",samples), "R",
                    (ifelse(grepl("EP",samples), "EP", "ER")))))))))
Cell <- ifelse(grepl("47",samples), "T", "M")                
Type <- ifelse(grepl("47",samples), "Wildtype", "Mutant")
Replicate <- str_sub(samples, -1, -1)

The output I get for:

dds_V <- dds[,dds$Treatment=="Vehicle"]

is:

class: DESeqDataSet 
dim: 6 12 
metadata(1): version
assays(1): counts
rownames(6): ENSG00000000003.15 ENSG00000000419.12 ... ENSG00000001036.14
  ENSG00000001084.13
rowData names(0):
colnames(12): T1 T2 ... M3 M4
colData names(5): Treatment Cell Type Replicate sizeFactor

So ddsV contains 12 samples, T1-6 and M1-6 (all vehicle-treated) as I'd expect. But how do I then perform DE on ddsV (i.e. between vehicle-treated samples T1-6 and M1-6)?

Thank you again

ADD REPLY • link 4.0 years ago bak16 • 0

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Hi,

I'd recommend working with a statistician on the design part. I can help with software usage questions but how to specify the correct design is beyond the level of support I can provide. I just don't have extra time in my schedule to work out statistical analysis plans.

You told DESeq2 the design is ~treatment but then you only give samples belonging to one level of treatment, so DESeq2 can't "see" the other aspects of the variables that are of interest for modeling differences.