perform DESeq2 based on paired sample
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@xiaofeiwang18266-13498
Last seen 18 days ago

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I have 3 questions about Paired Test in DESeq2.

First, I'd like to double check the design for the paired test. Here is the link https://support.bioconductor.org/p/58893/ , I got the answer. This design is for paired test. But, it looks like a two-way factorial without interaction for me. Could you confirm me it is similar to paired t-test?

design(dds) <- ~ patient + condition

Where 'patient' and 'condition' are factors which are columns in
colData(dds), and condition has levels: normal, tumor (where normal is
the
base level).

Then results(dds) will build a result table for tumor vs normal,
controlling for the patient effect.

Here is my colData:

colData(dds_matched)
DataFrame with 144 rows and 3 columns
                                                                                    Barcodes sample_type      patient
                                                                                  <character>    <factor>     <factor>
TCGA-A3-3358-01A-01R-1541-07 TCGA-A3-3358-01A-01R..      tumour TCGA_A3_3358
TCGA-A3-3358-11A-01R-1541-07 TCGA-A3-3358-11A-01R..      normal TCGA_A3_3358
TCGA-A3-3387-01A-01R-1541-07 TCGA-A3-3387-01A-01R..      tumour TCGA_A3_3387
TCGA-A3-3387-11A-01R-1541-07 TCGA-A3-3387-11A-01R..      normal TCGA_A3_3387
TCGA-B0-4700-01A-02R-1541-07 TCGA-B0-4700-01A-02R..      tumour TCGA_B0_4700
...                                             ...         ...          ...

Second, I have 72 patients, and each patient has tumour and normal data, so I have 144 samples in total. I used the design above, but it took a long time to run. Also, I got this as below. While, I also run this design WO considering the matching information "design =~ sample_type", it finished very quickly and WO anything reported.

dds_matched <- DESeqDataSetFromMatrix(expressionData.matched.for.DESeq, colData = condition_table, design =~ sample_type+patient)

dds_DE_matched <- DESeq(dds_matched)


estimating size factors
estimating dispersions
gene-wise dispersion estimates
2021-02-05 21:05:03.118 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (1.25 secs).
2021-02-05 21:05:14.738 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (1.09 secs).
2021-02-05 21:05:34.790 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (1.05 secs).
2021-02-05 21:13:01.764 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (2.30 secs).
2021-02-05 21:13:11.213 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (2.44 secs).
2021-02-05 21:13:14.387 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (2.37 secs).
mean-dispersion relationship
final dispersion estimates
2021-02-05 21:26:30.894 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (2.02 secs).
2021-02-05 21:28:05.292 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (1.93 secs).
2021-02-05 21:33:08.983 R[48867:3744773] IMKClient Stall detected, *please Report* your user scenario attaching a spindump (or sysdiagnose) that captures the problem - (imkxpc_attributesForCharacterIndex:reply:) block performed very slowly (1.89 secs).
fitting model and testing
15 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest

Third, if I used this design "design =~ sample_type+patient", how should I feed the value for contrast? If it is 2-way factorial design, this means the effect of sample type. But, my experiment is paired data.

res_DE_matched <- results(dds_DE_matched, alpha=0.05, contrast=c("sample_type","tumour","normal"))

Thanks you so much!

Code should be placed in three backticks as shown below


# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

sessionInfo( )
DESeq2 • 168 views
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Oh, and the IMKClient Stall has nothing to do with DESeq2. I'd recommend to consult with your local IT.

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@mikelove
Last seen 12 hours ago
United States

Yes, that is the design for a paired test (also it is listed in the FAQ in the DESeq2 vignette).

Yes, it is slower to perform such a paired analysis than to ignore the pairing information.

I recommend to use a design of ~patient + condition, and then either just call results() (which will give you a Wald test of the condition effect, with the reference level in the denominator), or you can use the LRT as shown below in the glmGamPoi code, which will test ~patient + condition vs ~patient.

Here on my machine with simulated data, 500 genes takes 62 seconds on my laptop using a single core, and an alternative method shown below takes 30 seconds.

With a large experimental design, I'd recommend performing filtering to ensure that a good number of samples have a minimal count, before running DESeq. If you were to filter to ~10,000 genes, the analysis should take 20 min (or 10 min with glmGamPoi), which isn't too slow. Also this is just running on a laptop with a single core, and isn't making use of multiple cores which is easy in DESeq2 (see vignette). I think multiple cores is not yet implemented for glmGamPoi analysis though.

E.g.:

keep <- rowSums(counts(dds) >= 10) >= X
dds <- dds[keep,]

where you may pick X to be the minimum number of samples that you want to have a minimal count, in order to be interested in that gene.

> dds <- makeExampleDESeqDataSet(n=500, m=140)
> dds$condition <- factor(rep(1:2,each=70))
> dds$patient <- factor(rep(1:70,times=2))
> design(dds) <- ~patient + condition
> system.time({ dds <- DESeq(dds, minRep=Inf) })
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
   user  system elapsed
 55.505   0.741  62.502

Note, fitting the model is 2x faster with glmGamPoi:

> system.time({ dds <- DESeq(dds, minRep=Inf, fitType="glmGamPoi", reduced=~patient, test="LRT") })
using pre-existing size factors
estimating dispersions
found already estimated dispersions, replacing these
gene-wise dispersion estimates
using 'glmGamPoi' as fitType. If used in published research, please cite:
    Ahlmann-Eltze, C., Huber, W. (2020) glmGamPoi: Fitting Gamma-Poisson
    Generalized Linear Models on Single Cell Count Data. bioRxiv.
    https://doi.org/10.1101/2020.08.13.249623
mean-dispersion relationship
final dispersion estimates
fitting model and testing
Fit reduced model
Calculate quasi likelihood ratio
Preprare results
   user  system elapsed
 29.069   0.340  30.326
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Does it matter, if the order is switched? The results of ~patient + condition is same as ~condition + patient?

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This is answered in the vignette. It only matters if you provide no arguments to results.

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@xiaofeiwang18266-13498
Last seen 18 days ago

Went through the details of vignette and plus your patience to answer the duplicated questions, it is clearly for me now. Thanks a lot!

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