Generate a long data.frame from a SummarizedExperiment
get_df_long.Rdget_df_long generate a wide data.frame from a SummarizedExperiment.
Arguments
- se
SummarizedExperiment, Proteomics data (output from
make_se()ormake_se_parse()).
Value
A data.frame object containing all data in a wide format, where each row represents a single measurement.
Examples
# Load example
data(Silicosis_pg)
data_unique <- make_unique(Silicosis_pg, "Gene.names", "Protein.IDs", delim = ";")
# Make SummarizedExperiment
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols, mode = "delim", sep = "_")
# Filter and normalize
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 = "MinDet")
#> Imputing along margin 2 (samples/columns).
diff <- test_diff(imputed,type = "control", control = "PBS")
#> Tested contrasts: W10_vs_PBS, W2_vs_PBS, W4_vs_PBS, W6_vs_PBS, W9_vs_PBS
#> Strimmer's qvalue(t)
dep <- add_rejections(diff)
long <- get_df_long(dep)
colnames(long)
#> [1] "label" "condition"
#> [3] "replicate" "name"
#> [5] "intensity" "Protein.IDs"
#> [7] "Majority.protein.IDs" "Peptide.counts..all."
#> [9] "Peptide.counts..razor.unique." "Peptide.counts..unique."
#> [11] "Protein.names" "Gene.names"
#> [13] "Fasta.headers" "Number.of.proteins"
#> [15] "Peptides" "Razor...unique.peptides"
#> [17] "Unique.peptides" "iBAQ.PBS_1"
#> [19] "iBAQ.PBS_2" "iBAQ.PBS_3"
#> [21] "iBAQ.PBS_4" "iBAQ.W10_2"
#> [23] "iBAQ.W10_4" "iBAQ.W2_1"
#> [25] "iBAQ.W2_3" "iBAQ.W2_4"
#> [27] "iBAQ.W2_5" "iBAQ.W4_2"
#> [29] "iBAQ.W4_3" "iBAQ.W4_4"
#> [31] "iBAQ.W4_5" "iBAQ.W6_2"
#> [33] "iBAQ.W6_3" "iBAQ.W6_4"
#> [35] "iBAQ.W6_6" "iBAQ.W9_2"
#> [37] "iBAQ.W9_4" "Only.identified.by.site"
#> [39] "Reverse" "Potential.contaminant"
#> [41] "ID" "imputed"
#> [43] "num_NAs" "W10_vs_PBS_CI.L"
#> [45] "W10_vs_PBS_CI.R" "W10_vs_PBS_diff"
#> [47] "W10_vs_PBS_p.adj" "W10_vs_PBS_p.val"
#> [49] "W10_vs_PBS_t.stastic" "W2_vs_PBS_CI.L"
#> [51] "W2_vs_PBS_CI.R" "W2_vs_PBS_diff"
#> [53] "W2_vs_PBS_p.adj" "W2_vs_PBS_p.val"
#> [55] "W2_vs_PBS_t.stastic" "W4_vs_PBS_CI.L"
#> [57] "W4_vs_PBS_CI.R" "W4_vs_PBS_diff"
#> [59] "W4_vs_PBS_p.adj" "W4_vs_PBS_p.val"
#> [61] "W4_vs_PBS_t.stastic" "W6_vs_PBS_CI.L"
#> [63] "W6_vs_PBS_CI.R" "W6_vs_PBS_diff"
#> [65] "W6_vs_PBS_p.adj" "W6_vs_PBS_p.val"
#> [67] "W6_vs_PBS_t.stastic" "W9_vs_PBS_CI.L"
#> [69] "W9_vs_PBS_CI.R" "W9_vs_PBS_diff"
#> [71] "W9_vs_PBS_p.adj" "W9_vs_PBS_p.val"
#> [73] "W9_vs_PBS_t.stastic" "W10_vs_PBS_significant"
#> [75] "W2_vs_PBS_significant" "W4_vs_PBS_significant"
#> [77] "W6_vs_PBS_significant" "W9_vs_PBS_significant"
#> [79] "significant"