I am analyzing an RNA-seq cohort of about 90 patients for differential expression between two disease subtypes. Low-level processing was done with Salmon/tximport followed by the standard workflow with DESeq2, using the disease subtype as the only covariate in the design. I was quiet surprised that in the MAplot with apeglm, there was an absence of the "typical" cloud of data points around LFC ~ 0, especially in comparison with the default method. Is this normal and expected?
I uploaded the Rdata object and a minimal script if that helps. If you need further details, please let me know.
R version 3.5.1 (2018-07-02) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core) Matrix products: default BLAS: foo/anaconda3/lib/R/lib/libRblas.so LAPACK: foo/anaconda3/lib/R/lib/libRlapack.so locale:  en_US.UTF-8 attached base packages:  parallel stats4 stats graphics grDevices utils datasets methods base other attached packages:  apeglm_1.2.1 DESeq2_1.20.0 SummarizedExperiment_1.10.1 DelayedArray_0.6.6 BiocParallel_1.14.2  matrixStats_0.54.0 Biobase_2.40.0 GenomicRanges_1.32.7 GenomeInfoDb_1.16.0 IRanges_2.14.12  S4Vectors_0.18.3 BiocGenerics_0.26.0 data.table_1.11.8 RevoUtils_11.0.1 RevoUtilsMath_11.0.0 loaded via a namespace (and not attached):  bit64_0.9-7 splines_3.5.1 Formula_1.2-3 assertthat_0.2.0 latticeExtra_0.6-28 blob_1.1.1 GenomeInfoDbData_1.1.0  yaml_2.2.0 numDeriv_2016.8-1 pillar_1.3.0 RSQLite_2.1.1 backports_1.1.2 lattice_0.20-35 glue_1.3.0  bbmle_1.0.20 digest_0.6.18 RColorBrewer_1.1-2 XVector_0.20.0 checkmate_1.8.5 colorspace_1.3-2 htmltools_0.3.6  Matrix_1.2-14 plyr_1.8.4 XML_3.98-1.16 pkgconfig_2.0.2 emdbook_1.3.10 genefilter_1.62.0 zlibbioc_1.26.0  purrr_0.2.5 xtable_1.8-3 scales_1.0.0 htmlTable_1.12 tibble_1.4.2 annotate_1.58.0 ggplot2_3.1.0  nnet_7.3-12 lazyeval_0.2.1 survival_2.42-6 magrittr_1.5 crayon_1.3.4 memoise_1.1.0 MASS_7.3-51  foreign_0.8-71 tools_3.5.1 stringr_1.3.1 locfit_1.5-9.1 munsell_0.5.0 cluster_2.0.7-1 AnnotationDbi_1.42.1  bindrcpp_0.2.2 compiler_3.5.1 rlang_0.3.0.1 grid_3.5.1 RCurl_1.95-4.11 rstudioapi_0.8 htmlwidgets_1.3  bitops_1.0-6 base64enc_0.1-3 gtable_0.2.0 DBI_1.0.0 R6_2.3.0 gridExtra_2.3 knitr_1.20  dplyr_0.7.7 bit_1.1-14 bindr_0.1.1 Hmisc_4.1-1 stringi_1.2.4 Rcpp_0.12.19 geneplotter_1.58.0  rpart_4.1-13 acepack_1.4.1 coda_0.19-2 tidyselect_0.2.5