Approximate distance correlation for selecting highly interrelated genes across datasets

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. Mor...

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Autores principales:	Qunlun Shen, Shihua Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4e466ea68f9f4bc4aed5a9e8d1d109c5
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spelling	oai:doaj.org-article:4e466ea68f9f4bc4aed5a9e8d1d109c52021-11-25T05:42:04ZApproximate distance correlation for selecting highly interrelated genes across datasets1553-734X1553-7358https://doaj.org/article/4e466ea68f9f4bc4aed5a9e8d1d109c52021-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604336/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358With 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 Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k 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. Author summary The number and size of biological datasets (e.g., single-cell RNA-seq datasets) are booming recently. How to mine the relationships of genes across datasets is becoming an important issue. Computational tools of identifying differentially expressed genes have been comprehensively studied, but the interrelated genes across datasets are always neglected. Detecting of highly interrelated genes across datasets is hindered because the samples of them are always different and they could have different numbers of samples. To solve this problem, we present a new algorithm that can identify interrelated genes across datasets based on distance correlation. Our proposed algorithm is very efficient and works well in different technologies, i.e., RNA-seq, single-cell RNA-seq and single-cell ATAC-seq. Also, we found that the number of such highly interrelated 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.Qunlun ShenShihua ZhangPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Qunlun Shen Shihua Zhang Approximate distance correlation for selecting highly interrelated genes across datasets
description	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 Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k 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. Author summary The number and size of biological datasets (e.g., single-cell RNA-seq datasets) are booming recently. How to mine the relationships of genes across datasets is becoming an important issue. Computational tools of identifying differentially expressed genes have been comprehensively studied, but the interrelated genes across datasets are always neglected. Detecting of highly interrelated genes across datasets is hindered because the samples of them are always different and they could have different numbers of samples. To solve this problem, we present a new algorithm that can identify interrelated genes across datasets based on distance correlation. Our proposed algorithm is very efficient and works well in different technologies, i.e., RNA-seq, single-cell RNA-seq and single-cell ATAC-seq. Also, we found that the number of such highly interrelated 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.
format	article
author	Qunlun Shen Shihua Zhang
author_facet	Qunlun Shen Shihua Zhang
author_sort	Qunlun Shen
title	Approximate distance correlation for selecting highly interrelated genes across datasets
title_short	Approximate distance correlation for selecting highly interrelated genes across datasets
title_full	Approximate distance correlation for selecting highly interrelated genes across datasets
title_fullStr	Approximate distance correlation for selecting highly interrelated genes across datasets
title_full_unstemmed	Approximate distance correlation for selecting highly interrelated genes across datasets
title_sort	approximate distance correlation for selecting highly interrelated genes across datasets
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4e466ea68f9f4bc4aed5a9e8d1d109c5
work_keys_str_mv	AT qunlunshen approximatedistancecorrelationforselectinghighlyinterrelatedgenesacrossdatasets AT shihuazhang approximatedistancecorrelationforselectinghighlyinterrelatedgenesacrossdatasets
_version_	1718414515484229632

Approximate distance correlation for selecting highly interrelated genes across datasets

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