p-value the same for different contrasts DESeq2
1
0
Entering edit mode
@chitsazanalex-11765
Last seen 5.9 years ago

I'm confused in regards to the p-value for a multilevel design in deseq2. I have a timecourse (0h, 3h, 6h, 24h) and I want to know what is changing between each pairwise comparison. I'm curious as to how the p-value is the same for individual and all pairwise comparisons (aka 0hvs3h OR 6hvs24h have same pvalues).

 

From the research that i've done looking into ?results, it seems the contrast parameter stated is used for the logFC calculation and it seems from my googling (and TRYING my best to read stats equations) that the wald test would result in one p-value based on the design. I'm curious as to what I can do to look at specific up and down genes in regard to specific timepoints, aka pulling out specific p-values for speicif contrasts (0hvs3h OR 6hvs24h)

 

dds <- DESeqDataSetFromMatrix(countData = counts,
                              colData = coldata,
                              design = ~ Condition)
dds <- DESeq(dds, test="LRT", reduced=~1)
res <- results(dds, contrast = c("Condition", "0hpa", "24hpa"))

sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] DESeq2_1.16.1              SummarizedExperiment_1.6.5 DelayedArray_0.2.7         matrixStats_0.53.1         Biobase_2.36.2            
 [6] VennDiagram_1.6.20         futile.logger_1.4.3        RColorBrewer_1.1-2         knitr_1.20                 edgeR_3.18.1              
[11] limma_3.32.10              Seurat_2.3.0               Matrix_1.2-14              cowplot_0.9.2              ggplot2_2.2.1             
[16] GenomicRanges_1.28.6       GenomeInfoDb_1.12.3        IRanges_2.10.5             S4Vectors_0.14.7           BiocGenerics_0.22.1       

loaded via a namespace (and not attached):
  [1] snow_0.4-2              backports_1.1.2         Hmisc_4.1-1             VGAM_1.0-5              sn_1.5-2               
  [6] plyr_1.8.4              igraph_1.2.1            lazyeval_0.2.1          splines_3.4.4           BiocParallel_1.10.1    
 [11] digest_0.6.15           foreach_1.4.4           htmltools_0.3.6         lars_1.2                gdata_2.18.0           
 [16] memoise_1.1.0           magrittr_1.5            checkmate_1.8.5         cluster_2.0.7-1         mixtools_1.1.0         
 [21] ROCR_1.0-7              sfsmisc_1.1-2           annotate_1.54.0         recipes_0.1.2           gower_0.1.2            
 [26] dimRed_0.1.0            R.utils_2.6.0-9000      colorspace_1.3-2        blob_1.1.1              dplyr_0.7.4            
 [31] RCurl_1.95-4.10         genefilter_1.58.1       bindr_0.1.1             survival_2.42-3         zoo_1.8-1              
 [36] iterators_1.0.9         ape_5.1                 glue_1.2.0              DRR_0.0.3               gtable_0.2.0           
 [41] ipred_0.9-6             zlibbioc_1.22.0         XVector_0.16.0          kernlab_0.9-25          ddalpha_1.3.2          
 [46] prabclus_2.2-6          DEoptimR_1.0-8          abind_1.4-5             scales_0.5.0            futile.options_1.0.1   
 [51] mvtnorm_1.0-7           DBI_0.8                 Rcpp_0.12.16            metap_0.9               dtw_1.18-1             
 [56] xtable_1.8-2            htmlTable_1.11.2        magic_1.5-8             tclust_1.3-1            bit_1.1-12             
 [61] foreign_0.8-70          proxy_0.4-22            mclust_5.4              SDMTools_1.1-221        Formula_1.2-2          
 [66] tsne_0.1-3              lava_1.6.1              prodlim_2018.04.18      htmlwidgets_1.2         FNN_1.1                
 [71] gplots_3.0.1            fpc_2.1-11              acepack_1.4.1           modeltools_0.2-21       ica_1.0-1              
 [76] XML_3.98-1.11           pkgconfig_2.0.1         R.methodsS3_1.7.1       flexmix_2.3-14          nnet_7.3-12            
 [81] locfit_1.5-9.1          caret_6.0-79            labeling_0.3            AnnotationDbi_1.38.2    tidyselect_0.2.4       
 [86] rlang_0.2.0             reshape2_1.4.3          munsell_0.4.3           tools_3.4.4             RSQLite_2.1.0          
 [91] ranger_0.9.0            broom_0.4.4             ggridges_0.5.0          evaluate_0.10.1         geometry_0.3-6         
 [96] stringr_1.3.0           bit64_0.9-7             ModelMetrics_1.1.0      fitdistrplus_1.0-9      robustbase_0.92-8      
[101] caTools_1.17.1          purrr_0.2.4             RANN_2.5.1              bindrcpp_0.2.2          pbapply_1.3-4          
[106] nlme_3.1-137            formatR_1.5             R.oo_1.22.0             RcppRoll_0.2.2          compiler_3.4.4         
[111] rstudioapi_0.7          png_0.1-7               geneplotter_1.54.0      tibble_1.4.2            stringi_1.1.7          
[116] highr_0.6               lattice_0.20-35         trimcluster_0.1-2       psych_1.8.3.3           diffusionMap_1.1-0     
[121] pillar_1.2.2            lmtest_0.9-36           data.table_1.10.4-3     bitops_1.0-6            irlba_2.3.2            
[126] R6_2.2.2                latticeExtra_0.6-28     KernSmooth_2.23-15      gridExtra_2.3           codetools_0.2-15       
[131] lambda.r_1.2.2          MASS_7.3-49             gtools_3.5.0            assertthat_0.2.0        CVST_0.2-1             
[136] rprojroot_1.3-2         withr_2.1.2             mnormt_1.5-5            GenomeInfoDbData_0.99.0 diptest_0.75-7         
[141] doSNOW_1.0.16           rpart_4.1-13            timeDate_3043.102       tidyr_0.8.0             class_7.3-14           
[146] rmarkdown_1.9           segmented_0.5-3.0       Rtsne_0.13              numDeriv_2016.8-1       scatterplot3d_0.3-41   
[151] lubridate_1.7.4         base64enc_0.1-3 

 

 

deseq2 design and contrast matrix • 1.4k views
ADD COMMENT
0
Entering edit mode
@mikelove
Last seen 2 hours ago
United States

Check the help page for ?results, it’s discussed in the Details section, and in the vignette FAQ.

ADD COMMENT
0
Entering edit mode

I did that, as I stated

"""

From the research that i've done looking into ?results, it seems the contrast parameter stated is used for the logFC calculation and it seems from my googling (and TRYING my best to read stats equations) that the wald test would result in one p-value based on the design. I'm curious as to what I can do to look at specific up and down genes in regard to specific timepoints, aka pulling out specific p-values for speicif contrasts (0hvs3h OR 6hvs24h)

"""

My question is what is the common practice for figuring out which genes are changing in specific contrasts? (ie; What is changing along the time-series) I'm used to using edgeR in which a pvalue is generated for each contrast and in this case it seems that one pvalue is generated for the experiment. Is the common practice  in DESeq to accomplish my question to find regions that pass a cutoff AND the individual contrast's logFC above a threshold?

 

ADD REPLY
1
Entering edit mode

If you want to test individual contrasts, and so generate p-values per contrast, you can switch from an LRT to a Wald test, by setting test="wald" when you run results().

ADD REPLY
0
Entering edit mode

Gotcha thanks!

ADD REPLY

Login before adding your answer.

Traffic: 796 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6