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

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

Summary: 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 Ex...

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Bibliographic Details
Main Authors: Matthew J. Camiolo, Sally E. Wenzel, Anuradha Ray
Format: Article
Language:English
Published: Elsevier 2021-12-01
Series:STAR Protocols
Subjects:
Bioinformatics
Sequence analysis
Immunology
Gene Expression
Systems biology
Online Access:http://www.sciencedirect.com/science/article/pii/S2666166721005530
Description
Summary:Summary: 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).
ISSN:2666-1667

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