batchQC_sva

Given data or simulated data from rnaseq_sim()

data.matrix

Model matrix for outcome of interest and other covariates 
besides batch

modmatrix


Estimate the surrogate variables using the 2 step approach proposed by 
Leek and Storey 2007


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.

batchQC_sva: Estimate the surrogate variables using the 2 step approach proposed by Leek and Storey 2007

Description

Usage

Arguments

Value

Examples