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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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). |
format | Online Article Text |
id | pubmed-8488363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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