combatPlot

Genomic measure matrix (dimensions probe x sample) - for example,
expression matrix

batch

Model matrix for outcome of interest and other covariates 
besides batch

(Optional) TRUE indicates parametric adjustments will be 
used, FALSE indicates non-parametric adjustments will be used

par.prior

(Optional)TRUE give prior plots with black as a kernel 
estimate of the empirical batch effect density and red as the parametric

prior.plots


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 methodology described in Johnson et al. 2007.
It uses either parametric or non-parametric empirical Bayes frameworks for 
adjusting data for batch effects. Users are returned an expression matrix 
that has been corrected for batch effects. The input
data are assumed to be cleaned and normalized before batch effect removal.


Sequencing and microarray samples often are collected or
processed in multiple batches or at different times. This often
produces technical biases that can lead to incorrect results in
the downstream analysis. BatchQC is a software tool that
streamlines batch preprocessing and evaluation by providing
interactive diagnostics, visualizations, and statistical
analyses to explore the extent to which batch variation impacts
the data. BatchQC diagnostics help determine whether batch
adjustment needs to be done, and how correction should be
applied before proceeding with a downstream analysis. Moreover,
BatchQC interactively applies multiple common batch effect
approaches to the data, and the user can quickly see the
benefits of each method. BatchQC is developed as a Shiny App.
The output is organized into multiple tabs, and each tab
features an important part of the batch effect analysis and
visualization of the data. The BatchQC interface has the
following analysis groups: Summary, Differential Expression,
Median Correlations, Heatmaps, Circular Dendrogram, PCA
Analysis, Shape, ComBat and SVA.

Solaiappan Manimaran

BatchQC

Batch Effects Quality Control Software

combatPlot function

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 methodology described in Johnson et al. 2007.
It uses either parametric or non-parametric empirical Bayes frameworks for 
adjusting data for batch effects. Users are returned an expression matrix 
that has been corrected for batch effects. The input
data are assumed to be cleaned and normalized before batch effect removal.

Description

Usage

Arguments

Value

Examples