Dataset Information

CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types.

ABSTRACT: We present a sensitive approach to predict genes expressed selectively in specific cell types, by searching publicly available expression data for genes with a similar expression profile to known cell-specific markers. Our method, CellMapper, strongly outperforms previous computational algorithms to predict cell type-specific expression, especially for rare and difficult-to-isolate cell types. Furthermore, CellMapper makes accurate predictions for human brain cell types that have never been isolated, and can be rapidly applied to diverse cell types from many tissues. We demonstrate a clinically relevant application to prioritize candidate genes in disease susceptibility loci identified by GWAS.

SUBMITTER: Nelms BD

PROVIDER: S-EPMC5043525 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types.

Nelms Bradlee D BD Waldron Levi L Barrera Luis A LA Weflen Andrew W AW Goettel Jeremy A JA Guo Guoji G Montgomery Robert K RK Neutra Marian R MR Breault David T DT Snapper Scott B SB Orkin Stuart H SH Bulyk Martha L ML Huttenhower Curtis C Lencer Wayne I WI

Genome biology 20160929 1

We present a sensitive approach to predict genes expressed selectively in specific cell types, by searching publicly available expression data for genes with a similar expression profile to known cell-specific markers. Our method, CellMapper, strongly outperforms previous computational algorithms to predict cell type-specific expression, especially for rare and difficult-to-isolate cell types. Furthermore, CellMapper makes accurate predictions for human brain cell types that have never been isol ...[more]

PMID: 27687735

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Project description:Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

Dataset Information

CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types.

Publications

CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types.

Similar Datasets

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