Hi,
I am using the DESeq2 package to analyze RNA-seq data. I have two conditions: treatmentand cell_type, and I would like the provide the function DESeqDataSetFromMatrix with a design that allows me to compute the results for every possible combination of treatmentand cell_type; To be clearer, if A, B and C are the treatment and 1, 2 the cell types:
A1:A2
B1:B2
C1:C2
A1:B1
A1:C1
B1:A1
B1:C1
C1:A1
C1:B1
... and so on.
I have read through the documentation but still haven't found a way to do this. I would also be willingly to manually define the contrasts I need (since I don't need them all, but what I need is pretty random and I want it to be flexible, so I can't define it exactly in the design) prior or after calling the DESeqDataSetFromMatrix, but I am not sure on how to do this; I have tried a couple of options but nothing seems to produce the output I want.
cds <- DESeqDataSetFromMatrix(countData =count.table, colData =m, design = ~ ?)
What should I have instead of the ?
I thank you very much in advance for any help!
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS
Matrix products: default
BLAS/LAPACK: /path/to/anaconda3/lib/R/lib/libRblas.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=de_DE.UTF-8 LC_TIME=de_DE.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=de_DE.UTF-8 LC_ADDRESS=de_DE.UTF-8
[10] LC_TELEPHONE=de_DE.UTF-8 LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=de_DE.UTF-8
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] hexbin_1.27.2 svglite_1.2.1 pheatmap_1.0.12
[4] gplots_3.0.1.1 vsn_3.50.0 plyr_1.8.4
[7] ggplot2_3.1.0 DESeq2_1.22.2 SummarizedExperiment_1.12.0
[10] DelayedArray_0.8.0 BiocParallel_1.16.6 matrixStats_0.54.0
[13] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2 genefilter_1.64.0
[16] reshape2_1.4.3 RColorBrewer_1.1-2 AnnotationDbi_1.44.0
[19] IRanges_2.16.0 S4Vectors_0.20.1 Biobase_2.42.0
[22] dplyr_0.8.0.1 clusterProfiler_3.10.1 AnnotationHub_2.14.4
[25] BiocGenerics_0.28.0 biomaRt_2.38.0
loaded via a namespace (and not attached):
[1] backports_1.1.3 Hmisc_4.2-0 fastmatch_1.1-0
[4] igraph_1.2.4 lazyeval_0.2.1 splines_3.5.1
[7] urltools_1.7.2 digest_0.6.18 htmltools_0.3.6
[10] GOSemSim_2.8.0 viridis_0.5.1 GO.db_3.7.0
[13] gdata_2.18.0 magrittr_1.5 checkmate_1.9.1
[16] memoise_1.1.0 cluster_2.0.7-1 limma_3.38.3
[19] annotate_1.60.1 enrichplot_1.2.0 prettyunits_1.0.2
[22] colorspace_1.4-0 blob_1.1.1 ggrepel_0.8.0
[25] xfun_0.5 crayon_1.3.4 RCurl_1.95-4.12
[28] jsonlite_1.6 survival_2.43-3 glue_1.3.0
[31] polyclip_1.9-1 gtable_0.2.0 zlibbioc_1.28.0
[34] XVector_0.22.0 UpSetR_1.3.3 scales_1.0.0
[37] DOSE_3.8.2 DBI_1.0.0 Rcpp_1.0.0
[40] viridisLite_0.3.0 xtable_1.8-3 progress_1.2.0
[43] htmlTable_1.13.1 gridGraphics_0.3-0 foreign_0.8-71
[46] bit_1.1-14 europepmc_0.3 preprocessCore_1.44.0
[49] Formula_1.2-3 htmlwidgets_1.3 httr_1.4.0
[52] fgsea_1.8.0 acepack_1.4.1 pkgconfig_2.0.2
[55] XML_3.98-1.19 farver_1.1.0 nnet_7.3-12
[58] locfit_1.5-9.1 ggplotify_0.0.3 tidyselect_0.2.5
[61] labeling_0.3 rlang_0.3.1 later_0.8.0
[64] munsell_0.5.0 tools_3.5.1 RSQLite_2.1.1
[67] ggridges_0.5.1 stringr_1.4.0 yaml_2.2.0
[70] knitr_1.22 bit64_0.9-7 caTools_1.17.1.2
[73] purrr_0.3.1 ggraph_1.0.2 mime_0.6
[76] DO.db_2.9 xml2_1.2.0 compiler_3.5.1
[79] rstudioapi_0.9.0 curl_3.3 interactiveDisplayBase_1.20.0
[82] affyio_1.52.0 tibble_2.0.1 tweenr_1.0.1
[85] geneplotter_1.60.0 stringi_1.3.1 gdtools_0.1.7
[88] lattice_0.20-38 Matrix_1.2-16 pillar_1.3.1
[91] BiocManager_1.30.4 triebeard_0.3.0 data.table_1.12.0
[94] cowplot_0.9.4 bitops_1.0-6 httpuv_1.4.5.1
[97] qvalue_2.14.1 R6_2.4.0 latticeExtra_0.6-28
[100] affy_1.60.0 promises_1.0.1 KernSmooth_2.23-15
[103] gridExtra_2.3 MASS_7.3-51.1 gtools_3.8.1
[106] assertthat_0.2.0 withr_2.1.2 GenomeInfoDbData_1.2.0
[109] hms_0.4.2 grid_3.5.1 rpart_4.1-13
[112] tidyr_0.8.3 rvcheck_0.1.3 ggforce_0.2.0
[115] shiny_1.2.0 base64enc_0.1-3
