LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between...
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Autor*in:	Min Lu [verfasserIn] Yifan Sha [verfasserIn] Tiago C. Silva [verfasserIn] Antonio Colaprico [verfasserIn] Xiaodian Sun [verfasserIn] Yuguang Ban [verfasserIn] Lily Wang [verfasserIn] Brian D. Lehmann [verfasserIn] X. Steven Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 12(2021)
Übergeordnetes Werk:	volume:12 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2021.708835

Katalog-ID:	DOAJ001296507

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520			\|a Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
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allfields	10.3389/fgene.2021.708835 doi (DE-627)DOAJ001296507 (DE-599)DOAJc03b4ebffd10498d9be64115fadf21eb DE-627 ger DE-627 rakwb eng QH426-470 Min Lu verfasserin aut LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset. random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq Genetics Yifan Sha verfasserin aut Tiago C. Silva verfasserin aut Antonio Colaprico verfasserin aut Xiaodian Sun verfasserin aut Yuguang Ban verfasserin aut Yuguang Ban verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Brian D. Lehmann verfasserin aut Brian D. Lehmann verfasserin aut X. Steven Chen verfasserin aut X. Steven Chen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 12(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:12 year:2021 https://doi.org/10.3389/fgene.2021.708835 kostenfrei https://doaj.org/article/c03b4ebffd10498d9be64115fadf21eb kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2021.708835/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
spelling	10.3389/fgene.2021.708835 doi (DE-627)DOAJ001296507 (DE-599)DOAJc03b4ebffd10498d9be64115fadf21eb DE-627 ger DE-627 rakwb eng QH426-470 Min Lu verfasserin aut LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset. random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq Genetics Yifan Sha verfasserin aut Tiago C. Silva verfasserin aut Antonio Colaprico verfasserin aut Xiaodian Sun verfasserin aut Yuguang Ban verfasserin aut Yuguang Ban verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Brian D. Lehmann verfasserin aut Brian D. Lehmann verfasserin aut X. Steven Chen verfasserin aut X. Steven Chen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 12(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:12 year:2021 https://doi.org/10.3389/fgene.2021.708835 kostenfrei https://doaj.org/article/c03b4ebffd10498d9be64115fadf21eb kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2021.708835/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
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allfieldsGer	10.3389/fgene.2021.708835 doi (DE-627)DOAJ001296507 (DE-599)DOAJc03b4ebffd10498d9be64115fadf21eb DE-627 ger DE-627 rakwb eng QH426-470 Min Lu verfasserin aut LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset. random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq Genetics Yifan Sha verfasserin aut Tiago C. Silva verfasserin aut Antonio Colaprico verfasserin aut Xiaodian Sun verfasserin aut Yuguang Ban verfasserin aut Yuguang Ban verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Brian D. Lehmann verfasserin aut Brian D. Lehmann verfasserin aut X. Steven Chen verfasserin aut X. Steven Chen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 12(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:12 year:2021 https://doi.org/10.3389/fgene.2021.708835 kostenfrei https://doaj.org/article/c03b4ebffd10498d9be64115fadf21eb kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2021.708835/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfieldsSound	10.3389/fgene.2021.708835 doi (DE-627)DOAJ001296507 (DE-599)DOAJc03b4ebffd10498d9be64115fadf21eb DE-627 ger DE-627 rakwb eng QH426-470 Min Lu verfasserin aut LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset. random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq Genetics Yifan Sha verfasserin aut Tiago C. Silva verfasserin aut Antonio Colaprico verfasserin aut Xiaodian Sun verfasserin aut Yuguang Ban verfasserin aut Yuguang Ban verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Lily Wang verfasserin aut Brian D. Lehmann verfasserin aut Brian D. Lehmann verfasserin aut X. Steven Chen verfasserin aut X. Steven Chen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 12(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:12 year:2021 https://doi.org/10.3389/fgene.2021.708835 kostenfrei https://doaj.org/article/c03b4ebffd10498d9be64115fadf21eb kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2021.708835/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
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authorswithroles_txt_mv	Min Lu @@aut@@ Yifan Sha @@aut@@ Tiago C. Silva @@aut@@ Antonio Colaprico @@aut@@ Xiaodian Sun @@aut@@ Yuguang Ban @@aut@@ Lily Wang @@aut@@ Brian D. Lehmann @@aut@@ X. Steven Chen @@aut@@
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callnumber-first	Q - Science
author	Min Lu
spellingShingle	Min Lu misc QH426-470 misc random forests misc ligand-receptor interaction misc cell–cell interaction misc cell–cell communications misc single-cell RNA-seq misc Genetics LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data
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topic_title	QH426-470 LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data random forests ligand-receptor interaction cell–cell interaction cell–cell communications single-cell RNA-seq
topic	misc QH426-470 misc random forests misc ligand-receptor interaction misc cell–cell interaction misc cell–cell communications misc single-cell RNA-seq misc Genetics
topic_unstemmed	misc QH426-470 misc random forests misc ligand-receptor interaction misc cell–cell interaction misc cell–cell communications misc single-cell RNA-seq misc Genetics
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title	LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data
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title_full	LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data
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author_browse	Min Lu Yifan Sha Tiago C. Silva Antonio Colaprico Xiaodian Sun Yuguang Ban Lily Wang Brian D. Lehmann X. Steven Chen
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title_sort	lr hunting: a random forest based cell–cell interaction discovery method for single-cell gene expression data
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title_auth	LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data
abstract	Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
abstractGer	Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
abstract_unstemmed	Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
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title_short	LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data
url	https://doi.org/10.3389/fgene.2021.708835 https://doaj.org/article/c03b4ebffd10498d9be64115fadf21eb https://www.frontiersin.org/articles/10.3389/fgene.2021.708835/full https://doaj.org/toc/1664-8021
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author2	Yifan Sha Tiago C. Silva Antonio Colaprico Xiaodian Sun Yuguang Ban Lily Wang Brian D. Lehmann X. Steven Chen
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up_date	2024-07-03T19:35:16.184Z
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LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

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