Question: p-value the same for different contrasts DESeq2
0
gravatar for chitsazanalex
17 months ago by
chitsazanalex10 wrote:

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 

 

 

ADD COMMENTlink modified 17 months ago by Michael Love25k • written 17 months ago by chitsazanalex10
Answer: p-value the same for different contrasts DESeq2
0
gravatar for Michael Love
17 months ago by
Michael Love25k
United States
Michael Love25k wrote:

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

ADD COMMENTlink modified 17 months ago • written 17 months ago by Michael Love25k

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 REPLYlink modified 17 months ago • written 17 months ago by chitsazanalex10
1

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 REPLYlink written 17 months ago by Michael Love25k

Gotcha thanks!

ADD REPLYlink written 17 months ago by chitsazanalex10
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