detect_effect: Detecting batch-effect in raw merged dataset

In madhulika-EBI/Batchevaluation: Toolkit for batch correction

Detecting batch-effect in raw merged dataset

Description

This is an accessory function that performs a subset of evaluation tests of 'evaluation_matrix' function and provides estimates whether the merged dataset obtained after 'merge_experiments' requires batch correction or not. A higher value of pvca.batch, silhouette, pcRegression, and entropy is indicative of batch-effects in a raw merged dataset without having any correction.

Usage

detect_effect(result, experiment, batch.factors, N1, N2, filter)

Arguments

result	A merged experiment without batch correction obtained from step ('merge_experiments').
experiment	A merged experiment without batch correction obtained from step ('merge_experiments').
batch.factors	A list of factors to perform PVCA analysis. Along with the batch factor, one biological factor which can be used to assess over-fitting should be provided.
N1	is the number of randomly picked cells for the BatchEntropy function.
N2	is the number of nearest nearest neighbors of the sample (from all batches) to check (for BatchEntropy function).
filter	A string. Should be one of following string- 'symbol', 'ensembl_gene_id', or 'entrezgene_id' depending on gene label for the given dataset.

Value

A list of the evaluation methods on the batch-corrected experiment.

Examples

 detect_effect(experiments,experiment = experiments, batch.factors=c("batch","Disease"),10,10,'symbol')

madhulika-EBI/Batchevaluation documentation built on Jan. 27, 2023, 5:27 p.m.