Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution
Name
1-s2.0-S2405471219303837-main.pdf
Description
Published version
Size
3.19 MB
Format
Adobe PDF
Checksum (MD5)
57cfdfd9bef10cbeef722c7f71871e24
Author(s)
Mohammadi, Shahin
Davila Velderrain, Jose
Kellis, Manolis
Date Issued
December 2019
Journal
Cell Systems
Publisher
Elsevier BV
Citation
Mohammadi, Shahin et al. "Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution." Cell Systems 9, 6 (December 2019): P559-568.e4 © 2019 The Authors
Version
Final published version
Abstract
The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cels.2019.10.007
