#> using pre-existing size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> Doing 1 element(s).
#> Doing results() for each element.
#> Doing lcfSrink() for each element.
degSummary(dse, contrast = "group_Male_vs_Female")
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.1
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.05
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.01
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 1, 0.1%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#> 0.1 0.05 0.01
#> LFC > 0 (up) <NA> <NA> <NA>
#> LFC < 0 (down) <NA> <NA> <NA>
#> outliers <NA> <NA> <NA>
#> low counts <NA> <NA> <NA>
#> cutoff <NA> <NA> <NA>
degSummary(res1)
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.1
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.05
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.01
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 1, 0.1%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#> 0.1 0.05 0.01
#> LFC > 0 (up) <NA> <NA> <NA>
#> LFC < 0 (down) <NA> <NA> <NA>
#> outliers <NA> <NA> <NA>
#> low counts <NA> <NA> <NA>
#> cutoff <NA> <NA> <NA>
degSummary(res1, kable = TRUE)
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.1
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.05
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.01
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 1, 0.1%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#>
#> | |0.1 |0.05 |0.01 |
#> |:--------------|:---|:----|:----|
#> |LFC > 0 (up) |NA |NA |NA |
#> |LFC < 0 (down) |NA |NA |NA |
#> |outliers |NA |NA |NA |
#> |low counts |NA |NA |NA |
#> |cutoff |NA |NA |NA |
degSummary(res2[[1]])
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.1
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.05
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 3, 0.3%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#>
#> out of1000with nonzero total read count
#> adjusted p-value < 0.01
#> LFC > 0 (up) : 4, 0.4%
#> LFC < 0 (down) : 1, 0.1%
#> outliers [1] : 1, 0.1%
#> low counts [2] : 0, 0%
#> (mean count < 46)
#> [1] see 'cooksCutoff' argument of ?results
#> [2] see 'independentFiltering' argument of ?results
#>
#> 0.1 0.05 0.01
#> LFC > 0 (up) <NA> <NA> <NA>
#> LFC < 0 (down) <NA> <NA> <NA>
#> outliers <NA> <NA> <NA>
#> low counts <NA> <NA> <NA>
#> cutoff <NA> <NA> <NA>