Question

Log2 fold-change shrinkage fails with lfcShrink when using the 'apeglm' method

1

Entering edit mode

coulsonr ▴ 20

@coulsonr-16748

Last seen 5.0 years ago

Hi,

I'm running lfcShrink to shrink log2 fold-changes (produced by DESeq2) so I can then use them in a gene set enrichment analysis. The design is (39 samples) :-

> pheno.df
         Group      Rate Quartile
4988-043    DN  37.89901       Q1
4988-016    DN  38.92224       Q1
4988-073    DN  26.35618       Q1
4988-062    DN 101.00866       Q4
4988-048    DN  28.87489       Q1
4988-046    DN  29.64660       Q1
4988-080    DN  39.42701       Q2
4988-027    DN  48.10482       Q2
4988-015    DN  35.50630       Q1
4988-076    DN  29.29898       Q1
4988-025    HC  98.10911       Q3
4988-012    HC  77.29580       Q2
4988-032    HC  99.41596       Q4
4988-058    HC  82.57723       Q3
4988-033    HC  99.63906       Q4
4988-030    HC 115.55412       Q4
4988-079    HC  69.73859       Q2
4988-078    HC  84.11147       Q3
4988-069    HC  91.72246       Q3
4988-010    HC 103.40455       Q4
4988-031    HC  79.19619       Q3
4988-066    HC 100.36594       Q4
4988-007    HC  80.73362       Q3
4988-050    HC  96.55857       Q3
4988-072    HC  75.23332       Q2
4988-026    HC  75.85582       Q2
4988-045    HC  99.00126       Q4
4988-063    HC  84.41203       Q3
4988-024    HC 101.97429       Q4
4988-023    DN  42.63891       Q2
4988-013    DN  29.06251       Q1
4988-051    DN  66.90664       Q2
4988-001    DN  77.62536       Q3
4988-038    DN  56.71115       Q2
4988-006    DN  29.15782       Q1
4988-052    DN  26.86037       Q1
4988-040    DN  57.97926       Q2
4988-037    DN 105.57987       Q4
4988-077    HC 119.42443       Q4

The contrast I'm interested in is DN vs. HC ('Group' variable), using either 'Quartile' as a categorical or 'Rate' as a continuous covariate.

> lapply(pheno.df, class)
$Group
[1] "factor"

$Rate
[1] "numeric"

$Quartile
[1] "factor"

dds.Discr <- DESeqDataSetFromMatrix(countData=raw.counts, colData=pheno.df, design= ~Quartile + Group)
ref.Level <- "HC"
dds.Discr$Group <- relevel(dds.Discr$Group, ref=ref.Level)

dds.Discr <- DESeq(dds.Discr)
Discr.coef.idx <- which( resultsNames(dds.Discr)=="Group_DN_vs_HC" )


dds.Cont <- DESeqDataSetFromMatrix(countData=raw.counts, colData=pheno.df, design= ~Rate + Group)

dds.Cont$Group <- relevel(dds.Cont$Group, ref=ref.Level)

dds.Cont <- DESeq(dds.Cont)

Cont.coef.idx <- which( resultsNames(dds.Cont)=="Group_DN_vs_HC" )

This all appears to run without any problems, and both:

lfcShrink(dds.Discr, coef=Discr.coef.idx)

lfcShrink(dds.Cont, coef=Cont.coef.idx)

using the default shrinkage method are fine.

However, with

lfcShrink(dds.Discr, coef=Discr.coef.idx, type="apeglm")

lfcShrink(dds.Cont, coef=Cont.coef.idx, type="apeglm")

both fail immediately with the error:

# using 'apeglm' for LFC shrinkage
# Error in if (intercept == 0) { : missing value where TRUE/FALSE needed

I've taken a look at the 'apeglm' & 'apeglm:::apeglm.single' functions and it appears to suggest that there is a problem with the design matrices, but I can't figure out what it is! Please could anyone help?

Many thanks.

Cheers,

Richard Coulson.

> model.matrix(design(dds.Discr), colData(dds.Discr))
         (Intercept) QuartileQ2 QuartileQ3 QuartileQ4 GroupDN
4988-043           1          0          0          0       1
4988-016           1          0          0          0       1
4988-073           1          0          0          0       1
4988-062           1          0          0          1       1
4988-048           1          0          0          0       1
4988-046           1          0          0          0       1
4988-080           1          1          0          0       1
4988-027           1          1          0          0       1
4988-015           1          0          0          0       1
4988-076           1          0          0          0       1
4988-025           1          0          1          0       0
4988-012           1          1          0          0       0
4988-032           1          0          0          1       0
4988-058           1          0          1          0       0
4988-033           1          0          0          1       0
4988-030           1          0          0          1       0
4988-079           1          1          0          0       0
4988-078           1          0          1          0       0
4988-069           1          0          1          0       0
4988-010           1          0          0          1       0
4988-031           1          0          1          0       0
4988-066           1          0          0          1       0
4988-007           1          0          1          0       0
4988-050           1          0          1          0       0
4988-072           1          1          0          0       0
4988-026           1          1          0          0       0
4988-045           1          0          0          1       0
4988-063           1          0          1          0       0
4988-024           1          0          0          1       0
4988-023           1          1          0          0       1
4988-013           1          0          0          0       1
4988-051           1          1          0          0       1
4988-001           1          0          1          0       1
4988-038           1          1          0          0       1
4988-006           1          0          0          0       1
4988-052           1          0          0          0       1
4988-040           1          1          0          0       1
4988-037           1          0          0          1       1
4988-077           1          0          0          1       0
attr(,"assign")
[1] 0 1 1 1 2
attr(,"contrasts")
attr(,"contrasts")$Quartile
[1] "contr.treatment"

attr(,"contrasts")$Group
[1] "contr.treatment"

>  model.matrix(design(dds.Cont), colData(dds.Cont))
         (Intercept)      Rate GroupDN
4988-043           1  37.89901       1
4988-016           1  38.92224       1
4988-073           1  26.35618       1
4988-062           1 101.00866       1
4988-048           1  28.87489       1
4988-046           1  29.64660       1
4988-080           1  39.42701       1
4988-027           1  48.10482       1
4988-015           1  35.50630       1
4988-076           1  29.29898       1
4988-025           1  98.10911       0
4988-012           1  77.29580       0
4988-032           1  99.41596       0
4988-058           1  82.57723       0
4988-033           1  99.63906       0
4988-030           1 115.55412       0
4988-079           1  69.73859       0
4988-078           1  84.11147       0
4988-069           1  91.72246       0
4988-010           1 103.40455       0
4988-031           1  79.19619       0
4988-066           1 100.36594       0
4988-007           1  80.73362       0
4988-050           1  96.55857       0
4988-072           1  75.23332       0
4988-026           1  75.85582       0
4988-045           1  99.00126       0
4988-063           1  84.41203       0
4988-024           1 101.97429       0
4988-023           1  42.63891       1
4988-013           1  29.06251       1
4988-051           1  66.90664       1
4988-001           1  77.62536       1
4988-038           1  56.71115       1
4988-006           1  29.15782       1
4988-052           1  26.86037       1
4988-040           1  57.97926       1
4988-037           1 105.57987       1
4988-077           1 119.42443       0
attr(,"assign")
[1] 0 1 2
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"

> sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.4 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets
[8] methods   base     

other attached packages:
 [1] apeglm_1.0.3               DESeq2_1.18.1             
 [3] SummarizedExperiment_1.8.1 DelayedArray_0.4.1        
 [5] matrixStats_0.53.1         Biobase_2.38.0            
 [7] GenomicRanges_1.30.3       GenomeInfoDb_1.14.0       
 [9] IRanges_2.12.0             S4Vectors_0.16.0          
[11] BiocGenerics_0.24.0       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17           locfit_1.5-9.1         lattice_0.20-35       
 [4] digest_0.6.15          plyr_1.8.4             emdbook_1.3.10        
 [7] backports_1.1.2        acepack_1.4.1          coda_0.19-1           
[10] RSQLite_2.1.1          ggplot2_2.2.1          pillar_1.2.3          
[13] zlibbioc_1.24.0        rlang_0.2.1            lazyeval_0.2.1        
[16] rstudioapi_0.7         data.table_1.11.4      annotate_1.56.2       
[19] blob_1.1.1             rpart_4.1-13           Matrix_1.2-14         
[22] bbmle_1.0.20           checkmate_1.8.5        splines_3.4.4         
[25] BiocParallel_1.12.0    geneplotter_1.56.0     stringr_1.3.1         
[28] foreign_0.8-70         htmlwidgets_1.2        RCurl_1.95-4.10       
[31] bit_1.1-14             munsell_0.5.0          numDeriv_2016.8-1     
[34] compiler_3.4.4         base64enc_0.1-3        htmltools_0.3.6       
[37] tcltk_3.4.4            nnet_7.3-12            tibble_1.4.2          
[40] gridExtra_2.3          htmlTable_1.12         GenomeInfoDbData_1.0.0
[43] Hmisc_4.1-1            XML_3.98-1.11          MASS_7.3-50           
[46] bitops_1.0-6           grid_3.4.4             xtable_1.8-2          
[49] gtable_0.2.0           DBI_1.0.0              magrittr_1.5          
[52] scales_0.5.0           stringi_1.2.3          XVector_0.18.0        
[55] genefilter_1.60.0      latticeExtra_0.6-28    Formula_1.2-3         
[58] RColorBrewer_1.1-2     tools_3.4.4            bit64_0.9-7           
[61] survival_2.42-4        AnnotationDbi_1.40.0   colorspace_1.3-2      
[64] cluster_2.0.7-1        memoise_1.1.0          knitr_1.20

deseq2 apeglm lfcshrink • 1.9k views

ADD COMMENT • link 6.4 years ago coulsonr ▴ 20

score 0 · Answer 1 · 2018-08-01

0

Entering edit mode

Michael Love 43k

@mikelove

Last seen 2 days ago

United States

Thanks for the bug report!

This is a simple bug, and I fixed it just now in the release branch (v1.2.1) and devel branch (v1.3.2), but I can't fix it for version 1.0 because developers can't fix bugs earlier than the release branch.

Are you able to update to the latest version of R, and re-install Bioconductor? If so, if you wait 1-2 days, the fixed version (v1.2.1) will show up and you will be able to download it.

If you cannot update to the latest version of R/Bioc, then a hack that will work is to set either Q2, 3 or 4 as the reference level.

And for Rate, you'd have to do:

dds$Rate <- dds$Rate - dds$Rate[1]

Again, this is just a hack to solve the bug for your dataset, it won't be necessary for the fixed version of apeglm.

By the way you can do this as well:

res <- lfcShrink(dds, coef="Group_DN_vs_HC")

ADD COMMENT • link 6.4 years ago Michael Love 43k

0

Entering edit mode

Many thanks Mike.

I tried setting dds$Rate to dds$Rate - dds$Rate[1], and this failed. However with dds$Rate[11] (the first row in the design matrix with GroupDN==0) it works. Though this produced:

Warning messages:
1: In sqrt(diag(sigma)) : NaNs produced
2: In sqrt(diag(sigma)) : NaNs produced
3: In sqrt(diag(sigma)) : NaNs produced
4: In sqrt(diag(sigma)) : NaNs produced
5: In sqrt(diag(sigma)) : NaNs produced
6: In sqrt(diag(sigma)) : NaNs produced

Is this an issue and could it have something to do with the message I got with DESeq, please:

2 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest

Longer term, I will update to the latest version of R & re-install Bioconductor in the next few days.

Cheers,

Richard.

ADD REPLY • link 6.4 years ago coulsonr ▴ 20

0

Entering edit mode

As, I see, yes I see why my proposal didn't work.

The warnings are rows that had a hard time converging, and I bet that its similar to the ones that were difficult for DESeq2. Typically, these rows tend to be genes with mostly zeros and only a few non-zero counts (or I may be wrong, but this is my experience). One option is to remove these rows with a conservative filter:

keep <- rowSums(counts(dds) >= 5) >= 10
dds <- dds[keep,]

...before running DESeq(dds).

ADD REPLY • link 6.4 years ago Michael Love 43k

score 0 · Answer 2 · 2018-08-07

0

Entering edit mode

coulsonr ▴ 20

@coulsonr-16748

Last seen 5.0 years ago

Thanks Mike, this filtering out of genes with low counts solved the issue.

Cheers,

Richard.

ADD COMMENT • link 6.4 years ago coulsonr ▴ 20