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Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations

Bulk expression data from heterogeneous cell populations pose a challenge for investigators, as differences in cell numbers and transcriptional programs may complicate analysis. To improve the performance of bulk RNA sequencing on mixed populations, we created Immune Cell Linkage through Exploratory...

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Detalles Bibliográficos
Autores principales: Camiolo, Matthew J., Wenzel, Sally E., Ray, Anuradha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488363/
https://www.ncbi.nlm.nih.gov/pubmed/34632417
http://dx.doi.org/10.1016/j.xpro.2021.100847
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author Camiolo, Matthew J.
Wenzel, Sally E.
Ray, Anuradha
author_facet Camiolo, Matthew J.
Wenzel, Sally E.
Ray, Anuradha
author_sort Camiolo, Matthew J.
collection PubMed
description Bulk expression data from heterogeneous cell populations pose a challenge for investigators, as differences in cell numbers and transcriptional programs may complicate analysis. To improve the performance of bulk RNA sequencing on mixed populations, we created Immune Cell Linkage through Exploratory Matrices (ICLite). The ICLite package for R constructs modules of correlated genes and identifies their relationship to specific lineages in mixed cell populations. This protocol details formatting, optimization of run parameters, and interpretation of results following implementation of ICLite. For complete details on the use and execution of this protocol, please refer to Camiolo et al. (2021).
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spelling pubmed-84883632021-10-08 Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations Camiolo, Matthew J. Wenzel, Sally E. Ray, Anuradha STAR Protoc Protocol Bulk expression data from heterogeneous cell populations pose a challenge for investigators, as differences in cell numbers and transcriptional programs may complicate analysis. To improve the performance of bulk RNA sequencing on mixed populations, we created Immune Cell Linkage through Exploratory Matrices (ICLite). The ICLite package for R constructs modules of correlated genes and identifies their relationship to specific lineages in mixed cell populations. This protocol details formatting, optimization of run parameters, and interpretation of results following implementation of ICLite. For complete details on the use and execution of this protocol, please refer to Camiolo et al. (2021). Elsevier 2021-09-27 /pmc/articles/PMC8488363/ /pubmed/34632417 http://dx.doi.org/10.1016/j.xpro.2021.100847 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Camiolo, Matthew J.
Wenzel, Sally E.
Ray, Anuradha
Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title_full Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title_fullStr Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title_full_unstemmed Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title_short Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
title_sort using iclite for deconvolution of bulk transcriptional data from mixed cell populations
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488363/
https://www.ncbi.nlm.nih.gov/pubmed/34632417
http://dx.doi.org/10.1016/j.xpro.2021.100847
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