Log2FC values slightly higher in some genes after DESeq2 shrinkage
1
1
Entering edit mode
altuda ▴ 10
@91f63ac0
Last seen 15 days ago
Czechia

Hi, I have a question about DESeq2 LFCshrinkage: Is it possible that some genes have a slightly higher LFC after shrinkage? It happened during my RNAseq DE analysis, I have very deeply sequenced samples with large base means. I tried visualizing using MAplot check, and it looks fine. I'm mainly just curious why it could be happening.

Thank you very much!

shrinkage1 shrinkage2

Before shrinkage

gene_name gene_biotype baseMean log2FoldChange lfcSE stat pvalue padj

Man2b1 protein_coding 13964.1811689668 1.14901659101663 0.026320542427396 43.6547458771468 0 0

Acsl1 protein_coding 12428.1198150372 -1.19203974862738 0.0309164869284314 -38.5567658895669 0 0

Lpar1 protein_coding 10110.7963562404 -1.2202928328298 0.0304299001078297 -40.1017692633114 0 0

Plbd2 protein_coding 11931.1301365076 1.3739819601851 0.0319518259803669 43.0016726126813 0 0

Colgalt1 protein_coding 20463.1536063121 -1.41111184542647 0.0360342333779327 -39.1603126567592 0 0

Pld3 protein_coding 10409.4761223403 1.45876217014098 0.0342221971577909 42.6261985288365 0 0

Serpinh1 protein_coding 130803.363492491 -1.60180488761581 0.038333572697764 -41.7859535359521 0 0

Itm2c protein_coding 16741.2345729904 1.61533171987466 0.0363497339741802 44.4386118760058 0 0

Fam20c protein_coding 31312.9603458304 1.66573800250627 0.0356904438344024 46.6718209007153 0 0

Arl6ip5 protein_coding 6917.92975304822 1.99977697101572 0.049957690177568 40.029412166731 0 0

Ctsa protein_coding 19094.5177324866 2.01155711378371 0.0450333903613202 44.668125087723 0 0

Aplp1 protein_coding 6144.4413345581 2.03447790224768 0.0423078628847808 48.0874656275663 0 0

After shrinkage

gene_name gene_biotype baseMean log2FoldChange lfcSE pvalue padj

Man2b1 protein_coding 13964.1811689668 1.15088428333462 0.0263176482387476 0 0

Acsl1 protein_coding 12428.1198150372 -1.1904574704259 0.0309164525315082 0 0

Lpar1 protein_coding 10110.7963562404 -1.21964450839398 0.0304314704060129 0 0

Plbd2 protein_coding 11931.1301365076 1.37367794538455 0.0319515611635686 0 0

Colgalt1 protein_coding 20463.1536063121 -1.40983149622625 0.0360380229694581 0 0

Pld3 protein_coding 10409.4761223403 1.45774280936718 0.0342249709778493 0 0

Serpinh1 protein_coding 130803.363492491 -1.60093511162113 0.0383282083050976 0 0

Itm2c protein_coding 16741.2345729904 1.61350967070109 0.0363573760545177 0 0

Fam20c protein_coding 31312.9603458304 1.66751229278783 0.0356897852459903 0 0

Arl6ip5 protein_coding 6917.92975304822 1.99771245849146 0.0499730301660154 0 0

Ctsa protein_coding 19094.5177324866 2.01001220283617 0.0450386387713646 0 0

Aplp1 protein_coding 6144.4413345581 2.03381754211172 0.0423164419717999 0 0

> condsToCompare
[1] "CTRL" "24h" 

res<-results(dds, contrast=c("condition", condsToCompare[2], condsToCompare[1]))


resLFC <- lfcShrink(dds, coef = "condition_24h_vs_CTRL", type="apeglm")



sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 12.3

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] tidyr_1.2.0                 edgeR_3.32.1                limma_3.46.0               
 [4] data.table_1.14.2           biomaRt_2.50.1              tximport_1.18.0            
 [7] dendsort_0.3.4              pheatmap_1.0.12             ggpubr_0.4.0               
[10] openxlsx_4.2.5              EnhancedVolcano_1.8.0       PCAtools_2.2.0             
[13] ggrepel_0.9.1               gplots_3.1.1                DESeq2_1.30.1              
[16] SummarizedExperiment_1.20.0 Biobase_2.50.0              MatrixGenerics_1.2.1       
[19] matrixStats_0.61.0          GenomicRanges_1.42.0        GenomeInfoDb_1.26.7        
[22] IRanges_2.24.1              S4Vectors_0.28.1            BiocGenerics_0.36.1        
[25] dplyr_1.0.8                 magrittr_2.0.3              ggplot2_3.3.5              
[28] RColorBrewer_1.1-3          BiocManager_1.30.16        

loaded via a namespace (and not attached):
  [1] ggbeeswarm_0.6.0          colorspace_2.0-3          ggsignif_0.6.3            ellipsis_0.3.2           
  [5] XVector_0.30.0            rstudioapi_0.13           farver_2.1.0              bit64_4.0.5              
  [9] mvtnorm_1.1-3             apeglm_1.12.0             AnnotationDbi_1.52.0      fansi_1.0.3              
 [13] xml2_1.3.3                splines_4.0.4             sparseMatrixStats_1.2.1   extrafont_0.18           
 [17] cachem_1.0.6              geneplotter_1.68.0        broom_0.7.12              Rttf2pt1_1.3.10          
 [21] annotate_1.68.0           dbplyr_2.1.1              readr_2.1.2               compiler_4.0.4           
 [25] httr_1.4.2                dqrng_0.3.0               backports_1.4.1           assertthat_0.2.1         
 [29] Matrix_1.4-1              fastmap_1.1.0             cli_3.2.0                 BiocSingular_1.6.0       
 [33] prettyunits_1.1.1         tools_4.0.4               rsvd_1.0.5                coda_0.19-4              
 [37] gtable_0.3.0              glue_1.6.2                GenomeInfoDbData_1.2.4    reshape2_1.4.4           
 [41] rappdirs_0.3.3            maps_3.4.0                Rcpp_1.0.8.3              bbmle_1.0.24             
 [45] carData_3.0-5             Biostrings_2.58.0         vctrs_0.4.0               ggalt_0.4.0              
 [49] extrafontdb_1.0           DelayedMatrixStats_1.12.3 stringr_1.4.0             beachmat_2.6.4           
 [53] lifecycle_1.0.1           irlba_2.3.5               gtools_3.9.2              rstatix_0.7.0            
 [57] XML_3.99-0.9              zlibbioc_1.36.0           MASS_7.3-56               scales_1.1.1             
 [61] vroom_1.5.7               hms_1.1.1                 proj4_1.0-11              curl_4.3.2               
 [65] memoise_2.0.1             ggrastr_1.0.1             emdbook_1.3.12            bdsmatrix_1.3-4          
 [69] stringi_1.7.6             RSQLite_2.2.12            genefilter_1.72.1         caTools_1.18.2           
 [73] zip_2.2.0                 BiocParallel_1.24.1       rlang_1.0.2               pkgconfig_2.0.3          
 [77] bitops_1.0-7              lattice_0.20-45           purrr_0.3.4               labeling_0.4.2           
 [81] cowplot_1.1.1             bit_4.0.4                 tidyselect_1.1.2          plyr_1.8.7               
 [85] R6_2.5.1                  generics_0.1.2            DelayedArray_0.16.3       DBI_1.1.2                
 [89] pillar_1.7.0              withr_2.5.0               survival_3.3-1            abind_1.4-5              
 [93] RCurl_1.98-1.6            ash_1.0-15                tibble_3.1.6              crayon_1.5.1             
 [97] car_3.0-12                KernSmooth_2.23-20        utf8_1.2.2                BiocFileCache_1.14.0     
[101] tzdb_0.3.0                progress_1.2.2            locfit_1.5-9.4            grid_4.0.4               
[105] blob_1.2.3                digest_0.6.29             xtable_1.8-4              numDeriv_2016.8-1.1      
[109] munsell_0.5.0             beeswarm_0.4.0            vipor_0.4.5
DESeq2 • 135 views
ADD COMMENT
3
Entering edit mode
@mikelove
Last seen 8 hours ago
United States

This is possible just due to numeric convergence differences between the two algorithms. Note that the SE is more than an order of magnitude larger than the difference — it’s changing by much less than your precision to estimate the effect size.

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