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:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
 [1] biom_0.3.12                DESeq2_1.16.1
 [3] SummarizedExperiment_1.6.5 DelayedArray_0.2.7
 [5] matrixStats_0.54.0         Biobase_2.36.2
 [7] GenomicRanges_1.28.6       GenomeInfoDb_1.12.3
 [9] IRanges_2.10.5             S4Vectors_0.14.7
[11] BiocGenerics_0.22.1        optparse_1.6.0

loaded via a namespace (and not attached):
 [1] bit64_0.9-7             splines_3.4.0           Formula_1.2-3
 [4] assertthat_0.2.1        latticeExtra_0.6-28     blob_1.1.1
 [7] GenomeInfoDbData_0.99.0 pillar_1.4.1            RSQLite_2.1.1
[10] backports_1.1.2         lattice_0.20-35         glue_1.3.1
[13] digest_0.6.15           RColorBrewer_1.1-2      XVector_0.16.0
[16] checkmate_1.9.3         colorspace_1.3-2        htmltools_0.3.6
[19] Matrix_1.2-17           plyr_1.8.4              XML_3.98-1.11
[22] pkgconfig_2.0.2         genefilter_1.58.1       zlibbioc_1.22.0
[25] purrr_0.3.2             xtable_1.8-2            scales_1.0.0
[28] getopt_1.20.2           BiocParallel_1.10.1     htmlTable_1.13.1
[31] tibble_2.1.1            annotate_1.54.0         ggplot2_3.1.1
[34] nnet_7.3-12             lazyeval_0.2.1          RJSONIO_1.3-0
[37] survival_2.42-3         magrittr_1.5            crayon_1.3.4
[40] memoise_1.1.0           foreign_0.8-70          tools_3.4.0
[43] data.table_1.12.2       stringr_1.4.0           locfit_1.5-9.1
[46] munsell_0.5.0           cluster_2.0.7-1         AnnotationDbi_1.38.2
[49] compiler_3.4.0          rlang_0.3.4             grid_3.4.0
[52] RCurl_1.95-4.10         rstudioapi_0.10         htmlwidgets_1.3
[55] bitops_1.0-6            base64enc_0.1-3         gtable_0.2.0
[58] DBI_1.0.0               R6_2.4.0                gridExtra_2.3
[61] knitr_1.23              dplyr_0.8.1             bit_1.1-14
[64] Hmisc_4.1-1             stringi_1.4.3           Rcpp_1.0.1
[67] geneplotter_1.54.0      rpart_4.1-13            acepack_1.4.1
[70] tidyselect_0.2.5        xfun_0.7

It's not clear what you are asking. You know that having unpaired samples doesn't help, and yet you want to keep them in the analysis. While I understand your desire, that alone doesn't affect the underlying issue. You can the unpaired samples in, or remove them at your discretion, but it won't really change anything.

Also, that's a really old version of R and Bioconductor. You should update.