detectBatchEffects: Detect batch effects

In theMILOlab/cypro: A Toolbox for Analysis of Single Cell Microscopy Data

Detect batch effects

Description

This function does the necessary computation to detect batch effects in your data. See details for more.

Usage

detectBatchEffects(object, verbose = TRUE)

Arguments

object	A valid cypro object.
verbose	Logical. If set to TRUE informative messages regarding the computational progress will be printed. (Warning messages will always be printed.)

Details

Batch effects result from undesired technical variation and can lead to misinterpretation and false conclusion. This does two things: First it creates summarized median profiles for each well by grouping the cell data by well plate name and well via dplyr::group_by() and then summarizes all stat variables via dplyr::summarize_all().

It then computes the distances between all wells. If wells of well plates that were set up in one batch cluster together it is likely that batch effects impact your data.

Hint: This logic only applies to experiment designs in which conditions are randomly distributed over well plates as a wells condition highly impacts its median profile.

Value

An updated version of the input cypro-object.

See Also

plotBatchHeatmap(), getBatchEffectDf()

theMILOlab/cypro documentation built on April 5, 2022, 2:03 a.m.