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:
Protocol
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
Descripción
Sumario: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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