Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease
Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biom...
Gespeichert in:
Veröffentlicht in: | NAR genomics and bioinformatics 2024-08, Vol.6 (3) |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 3 |
container_start_page | |
container_title | NAR genomics and bioinformatics |
container_volume | 6 |
creator | Liu, Yuanhang Vierkant, Robert A Bhagwate, Aditya Jons, William A Stallings-Mann, Melody L McCauley, Bryan M Carter, Jodi M Stephens, Melissa T Pfrender, Michael E Littlepage, Laurie E Radisky, Derek C Cunningham, Julie M Degnim, Amy C Winham, Stacey J Wang, Chen |
description | Abstract
Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses. |
doi_str_mv | 10.1093/nargab/lqae098 |
format | Article |
fullrecord | <record><control><sourceid>oup_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1093_nargab_lqae098</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/nargab/lqae098</oup_id><sourcerecordid>10.1093/nargab/lqae098</sourcerecordid><originalsourceid>FETCH-LOGICAL-c198t-3440267ab3668bdb8571cec129e506af270c560bc7c3703119a51fe1a4851ff33</originalsourceid><addsrcrecordid>eNqFkDFPwzAQRi0EElXpyuyVIe05TpyEDVUtIFWCAVais3upjIwTYqdS_z2FFImN6d3wvhseY9cC5gIqufDY71Av3CcSVOUZm6RKiqRKVXn-575ksxDeASDNszwDMWFvqz26AaP1O27IOR4PHfEtmdbvWzdE23puPV-vn1dc94Qh8mhDGOiWY9c5a_BHiS3X5O3O_0pbG46kK3bRoAs0O3HKXterl-VDsnm6f1zebRIjqjImMssgVQVqqVSpt7rMC2HIiLSiHBQ2aQEmV6BNYWQBUogKc9GQwKw8spFyyubjX9O3IfTU1F1vP7A_1ALq70D1GKg-BToObsZBO3T_uV8uMGpY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease</title><source>DOAJ Directory of Open Access Journals</source><source>Oxford Journals Open Access Collection</source><source>PubMed Central</source><creator>Liu, Yuanhang ; Vierkant, Robert A ; Bhagwate, Aditya ; Jons, William A ; Stallings-Mann, Melody L ; McCauley, Bryan M ; Carter, Jodi M ; Stephens, Melissa T ; Pfrender, Michael E ; Littlepage, Laurie E ; Radisky, Derek C ; Cunningham, Julie M ; Degnim, Amy C ; Winham, Stacey J ; Wang, Chen</creator><creatorcontrib>Liu, Yuanhang ; Vierkant, Robert A ; Bhagwate, Aditya ; Jons, William A ; Stallings-Mann, Melody L ; McCauley, Bryan M ; Carter, Jodi M ; Stephens, Melissa T ; Pfrender, Michael E ; Littlepage, Laurie E ; Radisky, Derek C ; Cunningham, Julie M ; Degnim, Amy C ; Winham, Stacey J ; Wang, Chen</creatorcontrib><description>Abstract
Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.</description><identifier>ISSN: 2631-9268</identifier><identifier>EISSN: 2631-9268</identifier><identifier>DOI: 10.1093/nargab/lqae098</identifier><language>eng</language><publisher>Oxford University Press</publisher><ispartof>NAR genomics and bioinformatics, 2024-08, Vol.6 (3)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c198t-3440267ab3668bdb8571cec129e506af270c560bc7c3703119a51fe1a4851ff33</cites><orcidid>0000-0003-2638-3081 ; 0000-0002-8159-3025 ; 0000-0002-0147-8394</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,862,1601,27911,27912</link.rule.ids></links><search><creatorcontrib>Liu, Yuanhang</creatorcontrib><creatorcontrib>Vierkant, Robert A</creatorcontrib><creatorcontrib>Bhagwate, Aditya</creatorcontrib><creatorcontrib>Jons, William A</creatorcontrib><creatorcontrib>Stallings-Mann, Melody L</creatorcontrib><creatorcontrib>McCauley, Bryan M</creatorcontrib><creatorcontrib>Carter, Jodi M</creatorcontrib><creatorcontrib>Stephens, Melissa T</creatorcontrib><creatorcontrib>Pfrender, Michael E</creatorcontrib><creatorcontrib>Littlepage, Laurie E</creatorcontrib><creatorcontrib>Radisky, Derek C</creatorcontrib><creatorcontrib>Cunningham, Julie M</creatorcontrib><creatorcontrib>Degnim, Amy C</creatorcontrib><creatorcontrib>Winham, Stacey J</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><title>Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease</title><title>NAR genomics and bioinformatics</title><description>Abstract
Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.</description><issn>2631-9268</issn><issn>2631-9268</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqFkDFPwzAQRi0EElXpyuyVIe05TpyEDVUtIFWCAVais3upjIwTYqdS_z2FFImN6d3wvhseY9cC5gIqufDY71Av3CcSVOUZm6RKiqRKVXn-575ksxDeASDNszwDMWFvqz26AaP1O27IOR4PHfEtmdbvWzdE23puPV-vn1dc94Qh8mhDGOiWY9c5a_BHiS3X5O3O_0pbG46kK3bRoAs0O3HKXterl-VDsnm6f1zebRIjqjImMssgVQVqqVSpt7rMC2HIiLSiHBQ2aQEmV6BNYWQBUogKc9GQwKw8spFyyubjX9O3IfTU1F1vP7A_1ALq70D1GKg-BToObsZBO3T_uV8uMGpY</recordid><startdate>20240806</startdate><enddate>20240806</enddate><creator>Liu, Yuanhang</creator><creator>Vierkant, Robert A</creator><creator>Bhagwate, Aditya</creator><creator>Jons, William A</creator><creator>Stallings-Mann, Melody L</creator><creator>McCauley, Bryan M</creator><creator>Carter, Jodi M</creator><creator>Stephens, Melissa T</creator><creator>Pfrender, Michael E</creator><creator>Littlepage, Laurie E</creator><creator>Radisky, Derek C</creator><creator>Cunningham, Julie M</creator><creator>Degnim, Amy C</creator><creator>Winham, Stacey J</creator><creator>Wang, Chen</creator><general>Oxford University Press</general><scope>TOX</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-2638-3081</orcidid><orcidid>https://orcid.org/0000-0002-8159-3025</orcidid><orcidid>https://orcid.org/0000-0002-0147-8394</orcidid></search><sort><creationdate>20240806</creationdate><title>Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease</title><author>Liu, Yuanhang ; Vierkant, Robert A ; Bhagwate, Aditya ; Jons, William A ; Stallings-Mann, Melody L ; McCauley, Bryan M ; Carter, Jodi M ; Stephens, Melissa T ; Pfrender, Michael E ; Littlepage, Laurie E ; Radisky, Derek C ; Cunningham, Julie M ; Degnim, Amy C ; Winham, Stacey J ; Wang, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c198t-3440267ab3668bdb8571cec129e506af270c560bc7c3703119a51fe1a4851ff33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yuanhang</creatorcontrib><creatorcontrib>Vierkant, Robert A</creatorcontrib><creatorcontrib>Bhagwate, Aditya</creatorcontrib><creatorcontrib>Jons, William A</creatorcontrib><creatorcontrib>Stallings-Mann, Melody L</creatorcontrib><creatorcontrib>McCauley, Bryan M</creatorcontrib><creatorcontrib>Carter, Jodi M</creatorcontrib><creatorcontrib>Stephens, Melissa T</creatorcontrib><creatorcontrib>Pfrender, Michael E</creatorcontrib><creatorcontrib>Littlepage, Laurie E</creatorcontrib><creatorcontrib>Radisky, Derek C</creatorcontrib><creatorcontrib>Cunningham, Julie M</creatorcontrib><creatorcontrib>Degnim, Amy C</creatorcontrib><creatorcontrib>Winham, Stacey J</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>CrossRef</collection><jtitle>NAR genomics and bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yuanhang</au><au>Vierkant, Robert A</au><au>Bhagwate, Aditya</au><au>Jons, William A</au><au>Stallings-Mann, Melody L</au><au>McCauley, Bryan M</au><au>Carter, Jodi M</au><au>Stephens, Melissa T</au><au>Pfrender, Michael E</au><au>Littlepage, Laurie E</au><au>Radisky, Derek C</au><au>Cunningham, Julie M</au><au>Degnim, Amy C</au><au>Winham, Stacey J</au><au>Wang, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease</atitle><jtitle>NAR genomics and bioinformatics</jtitle><date>2024-08-06</date><risdate>2024</risdate><volume>6</volume><issue>3</issue><issn>2631-9268</issn><eissn>2631-9268</eissn><abstract>Abstract
Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.</abstract><pub>Oxford University Press</pub><doi>10.1093/nargab/lqae098</doi><orcidid>https://orcid.org/0000-0003-2638-3081</orcidid><orcidid>https://orcid.org/0000-0002-8159-3025</orcidid><orcidid>https://orcid.org/0000-0002-0147-8394</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2631-9268 |
ispartof | NAR genomics and bioinformatics, 2024-08, Vol.6 (3) |
issn | 2631-9268 2631-9268 |
language | eng |
recordid | cdi_crossref_primary_10_1093_nargab_lqae098 |
source | DOAJ Directory of Open Access Journals; Oxford Journals Open Access Collection; PubMed Central |
title | Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T05%3A42%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oup_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluating%20cell%20type%20deconvolution%20in%20FFPE%20breast%20tissue:%20application%20to%20benign%20breast%20disease&rft.jtitle=NAR%20genomics%20and%20bioinformatics&rft.au=Liu,%20Yuanhang&rft.date=2024-08-06&rft.volume=6&rft.issue=3&rft.issn=2631-9268&rft.eissn=2631-9268&rft_id=info:doi/10.1093/nargab/lqae098&rft_dat=%3Coup_cross%3E10.1093/nargab/lqae098%3C/oup_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_oup_id=10.1093/nargab/lqae098&rfr_iscdi=true |