WGCNA (version 0.95)

standardScreeningBinaryTrait: Standard screening for binatry traits

Description

The function standardScreeningBinaryTrait computes widely used statistics for relating the columns of the input data frame (argument datE) to a binary sample trait (argument y). The statistics include Student t-test p-value and the corresponding local false discovery rate (known as q-value, Storey et al 2004), the fold change, the area under the ROC curve (also known as C-index), mean values etc. If the input option KruskalTest is set to TRUE, it also computes the Kruskal Wallist test p-value and corresponding q-value. The Kruskal Wallis test is a non-parametric, rank-based group comparison test.

Usage

standardScreeningBinaryTrait(datExpr, y, kruskalTest = FALSE)

Arguments

datExpr

a data frame or matrix whose columns will be related to the binary trait

y

a binary vector whose length (number of components) equals the number of rows of datE

kruskalTest

logical: should the Kruskal test be performed?

Value

• A data frame whose rows correspond to the columns of datE and whose columns report
• ID
  column names of the input datExpr.
• corPearson
  pearson correlation with a binary numeric version of the input variable. The numeric variable equals 1 for level 1 and 2 for level 2. The levels are given by levels(factor(y)).
• pvalueStudent
  two-sided Student t-test p-value.
• qvalueStudent
  q-value (local false discovery rate) based on the Student T-test p-value (Storey et al 2004).
• foldChange
  a (signed) ratio of mean values. If the mean in the first group (corresponding to level 1) is larger than that of the second group, it equals meanFirstGroup/meanSecondGroup. But if the mean of the second group is larger than that of the first group it equals -meanSecondGroup/meanFirstGroup (notice the minus sign).
• meanFirstGroup
  means of columns in input datExpr across samples in the first group.
• meanSecondGroup
  means of columns in input datExpr across samples in the second group.
• areaUnderROC
  the area under the ROC, also known as the concordance index or C.index. This is a measure of discriminatory power. The measure lies between 0 and 1 where 0.5 indicates no discriminatory power. 0 indicates that the "opposite" predictor has perfect discriminatory power. To compute it we use the function rcorr.cens with outx=T (from Frank Harrel's package Hmisc).

References

Storey JD, Taylor JE, and Siegmund D. (2004) Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B, 66: 187-205.