{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:40:54Z","timestamp":1767181254608,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009548","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000}}],"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFA0709501"],"award-info":[{"award-number":["2019YFA0709501"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDPB17"],"award-info":[{"award-number":["XDPB17"]}]},{"name":"Key-Area Research and Development of Guangdong Province","award":["2020B1111190001"],"award-info":[{"award-number":["2020B1111190001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61621003"],"award-info":[{"award-number":["61621003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Ten Thousand Talent Program for Young Top-notch Talents"},{"name":"CAS Frontier Science Research Key Project for Top Young Scientist","award":["QYZDB-SSW-SYS008"],"award-info":[{"award-number":["QYZDB-SSW-SYS008"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose\n                    <jats:underline>A<\/jats:underline>\n                    pproximate\n                    <jats:underline>D<\/jats:underline>\n                    istance\n                    <jats:underline>C<\/jats:underline>\n                    orrelation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the\n                    <jats:italic>k<\/jats:italic>\n                    most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009548","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T14:00:24Z","timestamp":1636466424000},"page":"e1009548","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Approximate distance correlation for selecting highly interrelated genes across datasets"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7149-6803","authenticated-orcid":true,"given":"Qunlun","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0192-7118","authenticated-orcid":true,"given":"Shihua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"issue":"1A","key":"pcbi.1009548.ref001","first-page":"A68","article-title":"The cancer genome atlas (tcga): an immeasurable source of knowledge","volume":"19","author":"Katarzyna Tomczak","year":"2015","journal-title":"Contemp Oncol"},{"issue":"8","key":"pcbi.1009548.ref002","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1038\/s41587-019-0206-z","article-title":"Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral t cell exhaustion","volume":"37","author":"Ansuman T Satpathy","year":"2019","journal-title":"Nature Biotechnol"},{"issue":"7","key":"pcbi.1009548.ref003","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/nrg3229","article-title":"Comparative studies of gene expression and the evolution of gene regulation","volume":"13","author":"Irene Gallego Romero","year":"2012","journal-title":"Nature Rev Genet"},{"issue":"19","key":"pcbi.1009548.ref004","doi-asserted-by":"crossref","first-page":"9379","DOI":"10.1093\/nar\/gks725","article-title":"Discovery of multi-dimensional modules by integrative analysis of cancer genomic data","volume":"40","author":"Shihua Zhang","year":"2012","journal-title":"Nucleic Acids Res"},{"issue":"13","key":"pcbi.1009548.ref005","doi-asserted-by":"crossref","first-page":"6606","DOI":"10.1093\/nar\/gkz488","article-title":"Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization","volume":"47","author":"Lihua Zhang","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"pcbi.1009548.ref006","doi-asserted-by":"crossref","first-page":"bbaa120","DOI":"10.1093\/bib\/bbaa120","article-title":"Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data","volume":"22","author":"Kangning Dong","year":"2021","journal-title":"Brief Bioinform"},{"key":"pcbi.1009548.ref007","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/0-387-29362-0_23","volume-title":"Bioinformatics and computational biology solutions using R and Bioconductor","author":"Gordon K Smyth","year":"2005"},{"issue":"1","key":"pcbi.1009548.ref008","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edger: a bioconductor package for differential expression analysis of digital gene expression data","volume":"26","author":"Mark D Robinson","year":"2010","journal-title":"Bioinformatics"},{"issue":"3","key":"pcbi.1009548.ref009","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41591-018-0323-0","article-title":"Deciphering the genomic, epigenomic, and transcriptomic landscapes of preinvasive lung cancer lesions","volume":"25","author":"Vitor H Teixeira","year":"2019","journal-title":"Nat Med"},{"key":"pcbi.1009548.ref010","first-page":"101","volume-title":"Adv Genet","author":"Jason H Moore","year":"2010"},{"issue":"6062","key":"pcbi.1009548.ref011","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1126\/science.1205438","article-title":"Detecting novel associations in large data sets","volume":"334","author":"David N Reshef","year":"2011","journal-title":"Science"},{"key":"pcbi.1009548.ref012","unstructured":"Noah Simon and Robert Tibshirani. 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