Generate a tidy table with the DE genes from the results of DESeq

deseqresult2DEgenes(deseqresult, FDR = 0.05)

Arguments

deseqresult

A DESeqResults object

FDR

Numeric value, the significance level for thresholding adjusted p-values

Value

A "tidy" data.frame with only genes marked as differentially expressed

Examples

# with simulated data... library(DESeq2)
#> Loading required package: GenomicRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> #> Attaching package: ‘matrixStats’
#> The following objects are masked from ‘package:Biobase’: #> #> anyMissing, rowMedians
#> #> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’: #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars
#> The following object is masked from ‘package:Biobase’: #> #> rowMedians
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8, betaSD = 2) dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- results(dds) deseqresult2DEgenes(res)
#> id baseMean log2FoldChange lfcSE stat pvalue #> gene21 gene21 326.794163 3.970890 0.3761786 10.555866 4.772187e-26 #> gene88 gene88 180.804426 5.303655 0.5404432 9.813529 9.846350e-23 #> gene34 gene34 112.018028 -3.507861 0.3810008 -9.206964 3.354790e-20 #> gene86 gene86 554.011384 3.576372 0.4206956 8.501093 1.878142e-17 #> gene46 gene46 2519.674827 2.658520 0.3963833 6.706943 1.987434e-11 #> gene73 gene73 312.872780 2.454988 0.4095375 5.994539 2.040634e-09 #> gene43 gene43 79.417862 3.341366 0.5646134 5.917972 3.259358e-09 #> gene99 gene99 354.228616 2.537850 0.4456657 5.694514 1.237237e-08 #> gene81 gene81 125.597432 -2.185457 0.4007836 -5.452960 4.953821e-08 #> gene61 gene61 28.310409 -3.530787 0.6591515 -5.356565 8.481910e-08 #> gene31 gene31 36.360707 -2.811803 0.5326527 -5.278869 1.299839e-07 #> gene95 gene95 145.603866 3.589596 0.7439440 4.825089 1.399409e-06 #> gene82 gene82 56.093609 -2.550799 0.5318243 -4.796319 1.616075e-06 #> gene72 gene72 17.087512 -3.560581 0.7478572 -4.761044 1.925938e-06 #> gene83 gene83 21.449172 -3.292425 0.6965981 -4.726434 2.284973e-06 #> gene18 gene18 9.068569 -6.044623 1.3087556 -4.618603 3.863320e-06 #> gene49 gene49 13.144427 -3.978430 0.8649371 -4.599676 4.231494e-06 #> gene76 gene76 81.131673 2.313319 0.5055756 4.575615 4.748239e-06 #> gene84 gene84 147.338368 2.390671 0.5419009 4.411638 1.025914e-05 #> gene5 gene5 19.405727 -2.736749 0.6321491 -4.329278 1.495992e-05 #> gene80 gene80 106.224530 3.365594 0.7813088 4.307636 1.650085e-05 #> gene89 gene89 54.059109 1.994835 0.4753898 4.196209 2.714197e-05 #> gene67 gene67 36.542053 -2.041300 0.5122432 -3.985021 6.747411e-05 #> gene54 gene54 15.868042 -3.326063 0.8400431 -3.959395 7.513978e-05 #> gene91 gene91 74.112526 1.878567 0.4840147 3.881218 1.039346e-04 #> gene32 gene32 31.784174 2.011663 0.5315085 3.784818 1.538213e-04 #> gene12 gene12 17.340576 -2.622295 0.7175759 -3.654381 2.578035e-04 #> gene35 gene35 13.317010 -2.801431 0.7662973 -3.655802 2.563792e-04 #> gene47 gene47 9.548995 -3.481061 0.9601673 -3.625473 2.884327e-04 #> gene6 gene6 56.063905 -1.721574 0.5002358 -3.441525 5.784444e-04 #> gene44 gene44 30.390915 2.048789 0.6472545 3.165354 1.548946e-03 #> gene55 gene55 47.473016 1.615592 0.5195919 3.109348 1.875006e-03 #> gene56 gene56 49.655733 1.706033 0.5520008 3.090636 1.997284e-03 #> gene28 gene28 20.078496 -2.124868 0.6963988 -3.051222 2.279116e-03 #> gene98 gene98 188.151706 1.214227 0.4154754 2.922500 3.472341e-03 #> gene4 gene4 10.859281 -2.124804 0.7563269 -2.809372 4.963822e-03 #> gene53 gene53 6.909460 -2.775508 0.9886430 -2.807391 4.994452e-03 #> gene77 gene77 32.836129 -1.608078 0.5762311 -2.790682 5.259705e-03 #> gene23 gene23 13.923597 2.552144 0.9177698 2.780810 5.422342e-03 #> gene39 gene39 22.707829 1.815041 0.6594217 2.752474 5.914693e-03 #> gene27 gene27 6.701156 3.254970 1.1900706 2.735106 6.236014e-03 #> gene92 gene92 58.741020 -1.167472 0.4324809 -2.699476 6.944885e-03 #> gene75 gene75 166.663052 1.155936 0.4315829 2.678365 7.398260e-03 #> gene16 gene16 11.179014 6.854420 2.6027814 2.633498 8.451033e-03 #> gene2 gene2 11.098416 -2.024351 0.7745153 -2.613700 8.956753e-03 #> gene22 gene22 42.316717 1.431842 0.5634425 2.541238 1.104607e-02 #> gene24 gene24 53.742605 1.389537 0.5468029 2.541202 1.104719e-02 #> gene93 gene93 41.966342 -1.296233 0.5122289 -2.530573 1.138763e-02 #> gene36 gene36 24.153364 1.538819 0.6232964 2.468840 1.355516e-02 #> padj #> gene21 4.772187e-24 #> gene88 4.923175e-21 #> gene34 1.118263e-18 #> gene86 4.695355e-16 #> gene46 3.974867e-10 #> gene73 3.401057e-08 #> gene43 4.656226e-08 #> gene99 1.546546e-07 #> gene81 5.504246e-07 #> gene61 8.481910e-07 #> gene31 1.181672e-06 #> gene95 1.166174e-05 #> gene82 1.243134e-05 #> gene72 1.375670e-05 #> gene83 1.523315e-05 #> gene18 2.414575e-05 #> gene49 2.489114e-05 #> gene76 2.637911e-05 #> gene84 5.399545e-05 #> gene5 7.479961e-05 #> gene80 7.857546e-05 #> gene89 1.233726e-04 #> gene67 2.933657e-04 #> gene54 3.130824e-04 #> gene91 4.157384e-04 #> gene32 5.916205e-04 #> gene12 9.207268e-04 #> gene35 9.207268e-04 #> gene47 9.945957e-04 #> gene6 1.928148e-03 #> gene44 4.996600e-03 #> gene55 5.859392e-03 #> gene56 6.052376e-03 #> gene28 6.703284e-03 #> gene98 9.920974e-03 #> gene4 1.349852e-02 #> gene53 1.349852e-02 #> gene77 1.384133e-02 #> gene23 1.390344e-02 #> gene39 1.478673e-02 #> gene27 1.520979e-02 #> gene92 1.653544e-02 #> gene75 1.720526e-02 #> gene16 1.920689e-02 #> gene2 1.990389e-02 #> gene22 2.350466e-02 #> gene24 2.350466e-02 #> gene93 2.372423e-02 #> gene36 2.766360e-02