Gene regulatory network inference using time-stamped cross-sectional single cell expression data
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Date
2016-12Type
- Conference Paper
ETH Bibliography
yes
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Abstract
In this paper we presented a novel method for inferring gene regulatory network (GRN) from time-stamped cross-sectional single cell data. Our strategy, called SNIFS (Sparse Network Inference For Single cell data) seeks to recover the causal relationships among genes by analyzing the evolution of the distribution of gene expression levels over time, more specifically using Kolmogorov-Smirnov (KS) distance. In the proposed method, we formulated the GRN inference as a linear regression problem, where we used Lasso regularization to obtain the optimal sparse solution. We tested SNIFS using in silico single cell data from 10 - and 20-gene GRNs, and compared the performance of our method with Time Series Network Inference (TSNI), GEne Network Inference with Ensemble of trees (GENIE3), and an extension of GENIE3 for time series data called JUMP3. The results showed that SNIFS outperformed existing algorithms based on the Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall (AUPR) curves. Show more
Publication status
publishedExternal links
Book title
6th IFAC Conference on Foundations of Systems Biology in Engineering, FOSBE 2016. ProceedingsJournal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Network inference; Single cell; Gene expression; Gene regulatory networkOrganisational unit
03898 - Gunawan, Rudiyanto (ehemalig)
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ETH Bibliography
yes
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