03.code Combat (nov April)mr. Mac's Virtual Existence

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  1. 03.code Combat (nov April)mr. Mac's Virtual Existence Server
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03.code Combat (nov April)mr. Mac's Virtual Existence Server

SrCode NameThe dataLatest data Post dateThe difference between the previous
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Adjust for batch effects using an empirical Bayes framework

ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodologydescribed in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data forbatch effects. Users are returned an expression matrix that has been corrected for batch effects. The inputdata are assumed to be cleaned and normalized before batch effect removal.

Usage
Arguments
dat
Genomic measure matrix (dimensions probe x sample) - for example, expression matrix
batch
Batch covariate (multiple batches are not allowed)
mod
Model matrix for outcome of interest and other covariates besides batch
par.prior
(Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used
prior.plots
(Optional)TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric
mean.only
(Optional)FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment)
Value
data A probe x sample genomic measure matrix, adjusted for batch effects.
Aliases
  • ComBat
03.code combat (nov april)mr. mac Documentation reproduced from package sva, version 3.20.0, License: Artistic-2.0

03.code Combat (nov April)mr. Mac's Virtual Existence Date

Community examples

## Correction of Batch Effects in Proteomics Data Using Combat*This is an excerpt from some code I used to prepare some proteomics data for hierarchical cluster analysis; the data was showing strong grouping tendencies associated with two separated batch preparations / mass spec analyses of the samples. An introduction to finite element method reddy pdf download. 'Ion counts' in this context is approximately analogous to e.g. expression level in an RNA context.*### Prepare dataComBat requires two data types: - a metadata `data.frame` - ion counts data in a `matrix`For the **metadata** `data.frame`, we simply need: - a column of samples - a column enumerating to which batch they belong (only two in this case)The two separate batches were distinguishable by whether or not the sample name contained the pattern 'bis':```cb.df.mdata <- cbind.data.frame('sample' = colnames(df.sdat.avgd.cleaned[, -c(1)]), # exclude uid column, c(1) 'batch' = ifelse(grepl('bis', colnames(df.sdat.avgd.cleaned[, -c(1)])), 'batch_A', 'batch_B')))```For the **sample data** (ion counts, in this case) `matrix`, the format is: - features in rows - samples in columnsConvert to matrix, sample ID in first column c(1):```cb.mtx.sdata <- as.matrix(df.sdat.avgd.cleaned[, -c(1)])rownames(cb.mtx.sdata) <- df.sdat.avgd.cleaned$uid```### Create Model & Apply the ComBat AlgorithmIn this case I am only correcting for the batch effects; however, see documentation for further explanation of how to define the model.```cb.corr.model <- model.matrix(~1, data = cb.df.mdata)cb.corr.counts = ComBat(dat=cb.mtx.sdata, batch=cb.df.mdata$batch, mod=cb.corr.model, par.prior=TRUE, prior.plot=FALSE)```

03.code Combat (nov April)mr. Mac's Virtual Existence Items

API documentation