cleaningY: Regress out covariates

In LieberInstitute/jaffelab: Commonly used functions by the Jaffe lab

Regress out covariates

Description

Regress out covariates such as surrogate variables or principal components.

Usage

cleaningY(y, mod, P)

Arguments

y	A matrix such as the outcome matrix from sva or a gene expression matrix.
mod	A full rank model matrix.
P	The number of SVs or PCs to protect based on the column order. For example, P=2 would keep the intercept term and a case vs diagnosis term in a model that is ~ Dx + more covariates.

Value

An object of the same type as y with the SVs/PCs regressed out.

Author(s)

Rafael Irizarry, Leonardo Collado-Torres (examples)

Examples

## Define a model generating function for 30 'samples'
set.seed(20190827)
model_fun <- function(x) {
    ## Baseline + a group effect (2 groups) + a second covariate effect
    rnorm(30) +
        c(rnorm(15, mean = 3), rnorm(15, mean = 1)) +
        c(
            rnorm(5, sd = 0.5), rnorm(5, sd = 0.2, mean = 0.5),
            rnorm(5, sd = 0.2, mean = 0.9)
        )
}

## Generate the data for 20 'genes'
y <- t(sapply(seq_len(20), model_fun))

## Define the phenotype data for these 30 'samples'
pheno <- data.frame(
    group = rep(c("A", "B"), each = 15),
    batch = rep(seq_len(3), each = 5)
)

## Define a full model
mod <- with(pheno, model.matrix(~ group + batch))

## Check the raw data for gene 1
boxplot(y[1, ] ~ pheno$group, ylab = "Gene 1 Raw Expr")

## Now regress out the batch covariate from the gene expression matrix
y_clean_p2 <- cleaningY(y, mod, P = 2)

## Check the cleaned data for gene 1 (with P = 2)
boxplot(y_clean_p2[1, ] ~ pheno$group, ylab = "Gene 1 Clean Expr (P = 2)")

## Or regress out the group and batch effects
y_clean_p3 <- cleaningY(y, mod, P = 1)

## Check the cleaned data for gene 1 (with P = 3)
boxplot(y_clean_p3[1, ] ~ pheno$group, ylab = "Gene 1 Clean Expr (P = 3)")




## The function also supports NAs observations as detailed below

## Make one observation 0, clean the data
y[1, 1] <- 0
y_clean_p2_0 <- cleaningY(y, mod, P = 2)
## then NA and clean again
y[1, 1] <- NA
y_clean_p2_NA <- cleaningY(y, mod, P = 2)

## Compare the results
corner(y_clean_p2_0)
corner(y_clean_p2_NA)

## They are identical except for that NA in [1, 1]
table(y_clean_p2_0 - y_clean_p2_NA, useNA = "ifany")

## Compared to the original y, there are differences since we lost
## one observation which affects all of the first row of the cleaned Y
y_clean_p2[1, ] - y_clean_p2_NA[1, ]
all(y_clean_p2[-1, ] - y_clean_p2_NA[-1, ] == 0)

LieberInstitute/jaffelab documentation built on April 1, 2024, 7:26 a.m.