Generate a results table
get_results.Rdget_results generates a results table from a proteomics or RNA-seq dataset
on which differential analysis was performed.
Arguments
- object
SummarizedExperiment or DEGdata, (output from
test_diff()andadd_rejections()).
Examples
#Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")
# Differential test
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
#> filter base on missing number is <= 0 in at least one condition.
#> filter base on missing number fraction < 0.4 in each row
#> filter base on giving formula
norm <- normalize_vsn(filt)
#> vsn2: 8762 x 20 matrix (1 stratum).
#> Please use 'meanSdPlot' to verify the fit.
imputed <- impute(norm, fun = "MinProb", q = 0.05)
#> Imputing along margin 2 (samples/columns).
#> [1] 0.3026531
diff <- test_diff(imputed, type = "control", control = c("PBS"), fdr.type = "Storey's qvalue")
#> Tested contrasts: W10_vs_PBS, W2_vs_PBS, W4_vs_PBS, W6_vs_PBS, W9_vs_PBS
#> Storey's qvalue
dep <- add_rejections(diff, alpha = 0.01,lfc = 2)
# Get results
results <- get_results(dep)
colnames(results)
#> [1] "name" "ID" "W10_vs_PBS_p.val"
#> [4] "W2_vs_PBS_p.val" "W4_vs_PBS_p.val" "W6_vs_PBS_p.val"
#> [7] "W9_vs_PBS_p.val" "W10_vs_PBS_p.adj" "W2_vs_PBS_p.adj"
#> [10] "W4_vs_PBS_p.adj" "W6_vs_PBS_p.adj" "W9_vs_PBS_p.adj"
#> [13] "W10_vs_PBS_significant" "W2_vs_PBS_significant" "W4_vs_PBS_significant"
#> [16] "W6_vs_PBS_significant" "W9_vs_PBS_significant" "significant"
#> [19] "W10_vs_PBS_ratio" "W2_vs_PBS_ratio" "W4_vs_PBS_ratio"
#> [22] "W6_vs_PBS_ratio" "W9_vs_PBS_ratio" "PBS_centered"
#> [25] "W10_centered" "W2_centered" "W4_centered"
#> [28] "W6_centered" "W9_centered"
significant_proteins <- results[results$significant,]
head(significant_proteins)
#> name ID W10_vs_PBS_p.val W2_vs_PBS_p.val W4_vs_PBS_p.val
#> 1 Acp5 Q05117 1.415141e-05 5.919033e-04 4.783118e-03
#> 2 Adamts8 P57110 2.345582e-01 3.067337e-02 2.095571e-02
#> 3 Add2 Q9QYB8 2.630809e-04 7.354807e-03 6.517427e-05
#> 4 Aif1 O70200 1.087675e-05 1.699023e-07 3.484965e-07
#> 5 Alox12 P39655 3.542542e-03 3.936133e-03 4.803149e-03
#> 6 Ank1 Q02357 2.105076e-04 4.835279e-04 1.669377e-05
#> W6_vs_PBS_p.val W9_vs_PBS_p.val W10_vs_PBS_p.adj W2_vs_PBS_p.adj
#> 1 2.103349e-04 3.200817e-04 0.000671 0.002380
#> 2 7.457791e-04 8.558215e-03 0.185000 0.036300
#> 3 1.776733e-03 1.461579e-02 0.003200 0.013100
#> 4 9.224154e-06 5.011573e-04 0.000578 0.000015
#> 5 1.479979e-04 5.415674e-03 0.012700 0.008430
#> 6 7.328290e-05 7.969721e-05 0.002780 0.002070
#> W4_vs_PBS_p.adj W6_vs_PBS_p.adj W9_vs_PBS_p.adj W10_vs_PBS_significant
#> 1 1.32e-02 0.001090 0.00282 TRUE
#> 2 3.46e-02 0.002360 0.02000 FALSE
#> 3 1.02e-03 0.004320 0.02830 TRUE
#> 4 4.76e-05 0.000177 0.00362 TRUE
#> 5 1.32e-02 0.000874 0.01480 FALSE
#> 6 4.76e-04 0.000569 0.00130 TRUE
#> W2_vs_PBS_significant W4_vs_PBS_significant W6_vs_PBS_significant
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE TRUE
#> 3 FALSE TRUE FALSE
#> 4 TRUE TRUE TRUE
#> 5 FALSE FALSE TRUE
#> 6 FALSE TRUE TRUE
#> W9_vs_PBS_significant significant W10_vs_PBS_ratio W2_vs_PBS_ratio
#> 1 FALSE TRUE 2.26 1.29
#> 2 FALSE TRUE 1.21 1.89
#> 3 FALSE TRUE -2.60 -1.40
#> 4 FALSE TRUE 2.84 3.21
#> 5 FALSE TRUE -1.80 -1.45
#> 6 TRUE TRUE -2.35 -1.76
#> W4_vs_PBS_ratio W6_vs_PBS_ratio W9_vs_PBS_ratio PBS_centered W10_centered
#> 1 0.994 1.44 1.69 -1.14 1.120
#> 2 2.040 3.30 2.92 -1.86 -0.649
#> 3 -2.430 -1.71 -1.53 1.52 -1.070
#> 4 3.040 2.35 1.98 -2.20 0.639
#> 5 -1.410 -2.12 -1.70 1.35 -0.457
#> 6 -2.420 -2.12 -2.58 1.75 -0.593
#> W2_centered W4_centered W6_centered W9_centered
#> 1 0.15100 -0.1470 0.300 0.5490
#> 2 0.03150 0.1820 1.440 1.0600
#> 3 0.11800 -0.9080 -0.187 -0.0131
#> 4 1.01000 0.8400 0.150 -0.2260
#> 5 -0.10500 -0.0643 -0.773 -0.3510
#> 6 -0.00249 -0.6710 -0.370 -0.8260