To the developers of DESeq2,
First of all, thank you for creating such a great tool!
I am currently trying to analyze 16S rRNA gene amplicon sequencing data of samples which were taken from the oral microbiota of patients at two timepoints: before and after treatment.
My colData is as below:
Group ParticipantID 1 BEFORE Participant_ONE 2 BEFORE Participant_TWO 3 BEFORE Participant_THREE 4 BEFORE Participant_FOUR 5 AFTER Participant_ONE 6 AFTER Participant_TWO 7 AFTER Participant_THREE
My planned analysis is as below
dds <- DESeqDataSetFromMatrix(count, colData, design = ~ ParticipantID + Group) dds <- DESeq(dds, fitType = "local") res <- results(dds, contrast = c("Group", "BEFORE", "AFTER"))
I noticed that in a previous post you mentioned that "samples without pair doesn't help you estimate the treatment effect".
However, because the sample size of my study is quite small, I would like to keep the non-paired sample (sample belonging to Participant_FOUR) in the analysis instead of removing them completely.
If you could please give some idea on whether this makes sense or not in the current analysis, that would be great.
P.S. Here is the sessionInfo() if needed:
R version 3.4.0 (2017-04-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Red Hat Enterprise Linux Matrix products: default BLAS/LAPACK: /usr/appli/freeware/OpenBLAS/0.3.6.gcc.THREAD/lib/libopenblas_haswellp-r0.3.6.so locale:  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C  LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8  LC_PAPER=en_US.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages:  parallel stats4 stats graphics grDevices utils datasets  methods base other attached packages:  biom_0.3.12 DESeq2_1.16.1  SummarizedExperiment_1.6.5 DelayedArray_0.2.7  matrixStats_0.54.0 Biobase_2.36.2  GenomicRanges_1.28.6 GenomeInfoDb_1.12.3  IRanges_2.10.5 S4Vectors_0.14.7  BiocGenerics_0.22.1 optparse_1.6.0 loaded via a namespace (and not attached):  bit64_0.9-7 splines_3.4.0 Formula_1.2-3  assertthat_0.2.1 latticeExtra_0.6-28 blob_1.1.1  GenomeInfoDbData_0.99.0 pillar_1.4.1 RSQLite_2.1.1  backports_1.1.2 lattice_0.20-35 glue_1.3.1  digest_0.6.15 RColorBrewer_1.1-2 XVector_0.16.0  checkmate_1.9.3 colorspace_1.3-2 htmltools_0.3.6  Matrix_1.2-17 plyr_1.8.4 XML_3.98-1.11  pkgconfig_2.0.2 genefilter_1.58.1 zlibbioc_1.22.0  purrr_0.3.2 xtable_1.8-2 scales_1.0.0  getopt_1.20.2 BiocParallel_1.10.1 htmlTable_1.13.1  tibble_2.1.1 annotate_1.54.0 ggplot2_3.1.1  nnet_7.3-12 lazyeval_0.2.1 RJSONIO_1.3-0  survival_2.42-3 magrittr_1.5 crayon_1.3.4  memoise_1.1.0 foreign_0.8-70 tools_3.4.0  data.table_1.12.2 stringr_1.4.0 locfit_1.5-9.1  munsell_0.5.0 cluster_2.0.7-1 AnnotationDbi_1.38.2  compiler_3.4.0 rlang_0.3.4 grid_3.4.0  RCurl_1.95-4.10 rstudioapi_0.10 htmlwidgets_1.3  bitops_1.0-6 base64enc_0.1-3 gtable_0.2.0  DBI_1.0.0 R6_2.4.0 gridExtra_2.3  knitr_1.23 dplyr_0.8.1 bit_1.1-14  Hmisc_4.1-1 stringi_1.4.3 Rcpp_1.0.1  geneplotter_1.54.0 rpart_4.1-13 acepack_1.4.1  tidyselect_0.2.5 xfun_0.7