Create a outlier summary table (used in plot_heatmap_annotated)
Source:R/get_outliers.R
get_outliers.RdCreate a outlier summary table (used in plot_heatmap_annotated)
Examples
data("ribo_toy")
get_outliers(ribo = ribo_toy)
#> samplename condition run filename comp1 median_coverage
#> S1 S1 cond1 L2 S1_5_counts.csv cond1 1761
#> S2 S2 cond2 L1 S2_5_counts.csv cond2 1292
#> S3 S3 cond2 L2 S3_5_counts.csv cond2 2212
#> S4 S4 cond2 L1 S4_5_counts.csv cond2 2147
#> S5 S5 cond2 L2 S5_5_counts.csv cond2 1682
#> S6 S6 cond2 L2 S6_5_counts.csv cond2 1774
#> S7 S7 cond2 L1 S7_5_counts.csv cond2 696
#> S8 S8 cond1 L1 S8_5_counts.csv cond1 1759
#> S9 S9 cond1 L2 S9_5_counts.csv cond1 1394
#> S10 S10 cond1 L1 S10_5_counts.csv cond1 1625
#> S11 S11 cond1 L1 S11_5_counts.csv cond1 1632
#> S12 S12 cond1 L2 S12_5_counts.csv cond1 1683
#> RNA1 RNA1 RNA ref L1 RNA1_5_counts.csv <NA> 1593
#> RNA2 RNA2 RNA ref L2 RNA2_5_counts.csv <NA> 2316
#> coverage_quality rlc_median rlc_median_quality total_outliers
#> S1 pass 0.057513954 pass 0
#> S2 pass -0.343901151 pass 0
#> S3 pass 0.395425908 pass 0
#> S4 pass 0.333701202 pass 0
#> S5 pass -0.011397603 pass 0
#> S6 pass 0.049972127 pass 0
#> S7 pass -1.254760930 warning 1
#> S8 pass 0.061861102 pass 0
#> S9 pass -0.250486582 pass 0
#> S10 pass -0.076701175 pass 0
#> S11 pass -0.047129087 pass 0
#> S12 pass 0.006275325 pass 0
#> RNA1 pass -0.106199404 pass 0
#> RNA2 pass 0.382232951 pass 0