Hello,
This may be a trivial question, but I am trying to use DESeq2 to find dispersion estimates with a reduced model (using no covariates). My dataset has 15400 genes with non-zero expression, but when I use the following commands, "environment(dds@dispersionFunction)[["fit"]][["fitted.values"]]" only has only 14116 values:
> dds <- estimateSizeFactors(dds)
> dds <- estimateDispersions(dds)
sessionInfo( )
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.3 pasilla_1.14.0 DESeq2_1.26.0
[4] SummarizedExperiment_1.16.1 DelayedArray_0.12.3 BiocParallel_1.20.1
[7] matrixStats_0.58.0 Biobase_2.46.0 GenomicRanges_1.38.0
[10] GenomeInfoDb_1.22.1 IRanges_2.20.2 S4Vectors_0.24.4
[13] BiocGenerics_0.32.0 ImpulseDE2_1.10.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 bit64_4.0.5 RColorBrewer_1.1-2 tools_3.6.3
[5] backports_1.2.1 utf8_1.2.1 R6_2.5.0 rpart_4.1-15
[9] Hmisc_4.5-0 DBI_1.1.1 colorspace_2.0-1 nnet_7.3-13
[13] GetoptLong_1.0.5 withr_2.4.2 tidyselect_1.1.1 gridExtra_2.3
[17] bit_4.0.4 compiler_3.6.3 htmlTable_2.1.0 labeling_0.4.2
[21] scales_1.1.1 checkmate_2.0.0 genefilter_1.68.0 stringr_1.4.0
[25] digest_0.6.27 foreign_0.8-75 XVector_0.26.0 base64enc_0.1-3
[29] jpeg_0.1-8.1 pkgconfig_2.0.3 htmltools_0.5.1.1 fastmap_1.1.0
[33] htmlwidgets_1.5.3 rlang_0.4.11 GlobalOptions_0.1.2 rstudioapi_0.13
[37] RSQLite_2.2.7 shape_1.4.5 generics_0.1.0 farver_2.1.0
[41] dplyr_1.0.6 RCurl_1.98-1.3 magrittr_2.0.1 GenomeInfoDbData_1.2.2
[45] Formula_1.2-4 Matrix_1.2-18 Rcpp_1.0.6 munsell_0.5.0
[49] fansi_0.4.2 lifecycle_1.0.0 stringi_1.5.3 zlibbioc_1.32.0
[53] grid_3.6.3 blob_1.2.1 crayon_1.4.1 lattice_0.20-40
[57] cowplot_1.1.1 splines_3.6.3 annotate_1.64.0 circlize_0.4.12
[61] locfit_1.5-9.4 knitr_1.33 ComplexHeatmap_2.2.0 pillar_1.6.0
[65] rjson_0.2.20 geneplotter_1.64.0 XML_3.99-0.3 glue_1.4.2
[69] latticeExtra_0.6-29 data.table_1.14.0 png_0.1-7 vctrs_0.3.8
[73] gtable_0.3.0 purrr_0.3.4 clue_0.3-59 cachem_1.0.4
[77] xfun_0.22 xtable_1.8-4 survival_3.1-8 tibble_3.1.1
[81] AnnotationDbi_1.48.0 memoise_2.0.0 cluster_2.1.0 ellipsis_0.3.2
For context, I am using these dispersion estimates as input for running ImpulseDE2 to identify time-dependent genes using time series RNA-seq data with only one sample per time point. I've emailed the people who wrote ImpulseDE2 and they told me this was possible by first running DESeq2 with a reduced model.
Are some genes not able to be used for calculating dispersion estimates? If so, is there a way for me to identify which genes these are?
Thanks!
