DE.statistics: Statistics for DEA Results Based Golden Standards
In LXQin/PRECISION.seq: Comparison of Depth Normalization Methods

Description Usage Arguments Details Value Examples

Computes the agreement of differential expression (DE) wihtin one data set with an assumed true DE status usually given based on a gold standard. The following statistics are computed based of the differentially expressed markers id.list compared to the list truly differentially expressed markers truth:

False Discovery Rate (FDR)
False Negative Rate (FNR)
True Positive Rate (TPR)
False Positive Rate (FPR)

1	DE.statistics(markers, id.list, truth, selected.marker = NULL)

`markers`	Vector of all markers considered in the analysis.
`id.list`	Vector of markers that are differentially expressed in a data set — typically in the data set `data.test`. Differential expression can be computed via `DE.edgeR` or `DE.voom`.
`truth`	Vector of genes that are (assumedly) truly differential expressed, e.g. basen on a gold standard — typically the data set `data.benchmark`.
`selected.marker`	optional Vector of a subset of `markers`. If given, the analysis will be limited to the given subset. Leave `NULL` if all markers are considered for the analysis.

Both, id.list and truth are a subset of markers from markers.

A list of:

TPR: True positive Rate
FPR: False Positive Rate
FDR: False Discovery Rate
FNR: False Negative Rate

DE.bench <- DE.voom(data.benchmark, data.group)
DE.test <- DE.voom(data.test, data.group)
stats <- DE.statistics(rownames(data.benchmark), DE.test$id.list, DE.bench$id.list)
print(stats)