{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lin L"],"funding":["National Natural Science Foundation of China"],"pagination":["309"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9338487"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information.<h4>Results</h4>Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq f"],"journal":["BMC bioinformatics"],"pubmed_title":["Joint analysis of scATAC-seq datasets using epiConv."],"pmcid":["PMC9338487"],"funding_grant_id":["NSF 31871332"],"pubmed_authors":["Zhang L","Lin L"],"additional_accession":[]},"is_claimable":false,"name":"Joint analysis of scATAC-seq datasets using epiConv.","description":"<h4>Background</h4>Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information.<h4>Results</h4>Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq f","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jul","modification":"2025-04-04T08:44:55.441Z","creation":"2025-04-04T08:44:55.441Z"},"accession":"S-EPMC9338487","cross_references":{"pubmed":["35906531"],"doi":["10.1186/s12859-022-04858-w"]}}