Article

scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder

2025-03-19

Abstract excerpt

<title>Abstract</title> <p>scRNA-seq data has the potential to provide new insights into cellular heterogeneity and data acquisition; however, a major challenge is unraveling confounding from technical and biological batch effects. Existing batch correction algorithms suppress and discard these effects, rather than quantifying and modeling them. Here, we present scMEDAL, a framework for single-cell Mixed Effects...

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Literature Corpus work: fce59c01-19f8-5f33-b855-bf8f71be989e
DOI: 10.21203/rs.3.rs-6081478/v1

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