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

Matthew J Camiolo; Sally E Wenzel; Anuradha Ray

doi:10.1016/j.xpro.2021.100847

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

STAR Protoc. 2021 Sep 27;2(4):100847. doi: 10.1016/j.xpro.2021.100847. eCollection 2021 Dec 17.

Authors

Matthew J Camiolo^{1

2}, Sally E Wenzel^{1

3

4}, Anuradha Ray^{1

4}

Affiliations

¹ Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
² Center for Systems Immunology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
³ Department of Environmental Medicine and Occupational Health, Graduate School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
⁴ Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.

Abstract

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).

Keywords: Bioinformatics; Gene Expression; Immunology; Sequence analysis; Systems biology.

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

Authors

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Abstract

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