PathCellNet: Cell-type specific pathogen-response network explorer

Katanic, Dejan; Khan, Atif Ali; Thakar, Juilee

doi:10.1016/j.jim.2016.09.005

Cited by 7 publications

(13 citation statements)

References 51 publications

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“…Integration procedure and calculations of associations between genes has been described in detail previously [20]. Briefly, transcriptomic data measuring changes in gene-expression in monocyte derived dendritic cells (DC) and A549 epithelial cells (EC) upon influenza infections were used.…”

Section: Methodsmentioning

confidence: 99%

“…There were two datasets for DCs (GSE41067 and GSE55278) and 9 datasets for ECs (GSE19580, GSE31469, GSE31470, GSE31471, GSE31472, GSE31473, GSE31474, GSE31518 and GSE47937). All the datasets were log2 transformed and quantile normalized individually in a platform specific manner as described previously [20]. To facilitate comparison across independent datasets, 14,894 genes commonly present across all the studies were used in this analysis.…”

Section: Methodsmentioning

confidence: 99%

“…1) based on Fuzzy C-Means (FCM) clustering method [18, 19] which allows overlap across gene-sets, thus reproducing the observed topology of biological pathways, and associate novel gene-sets to other gene-sets with enrichment of known pathways. Particularly, we apply the FCM pipeline to our previously curated context-specific data [20]. To facilitate use of our pipeline we developed a downloadable ‘Fuzzy Inference of Gene-sets (FIGS)’ package available at GitHub (https://github.com/Thakar-Lab/FIGS).…”

Section: Introductionmentioning

confidence: 99%

“…To facilitate use of our pipeline we developed a downloadable ‘Fuzzy Inference of Gene-sets (FIGS)’ package available at GitHub (https://github.com/Thakar-Lab/FIGS). Here, we demonstrate its application using transcriptomic data obtained from Gene Expression Omnibus (GEO) measuring response to infections of monocyte derived dendritic cells (DC) and A549 epithelial cells (EC) with influenza virus [20]. The gene-sets and overlapping genes identified in this study are validated by assessing enrichments of known pathway genes.…”

Section: Introductionmentioning

confidence: 99%

“…1Schematic representation of FIGS pipeline. The context-specific datasets obtained from public repositories were integrated as described in [20]. FCM is performed on gene-gene mutual information matrix.…”

Section: Introductionmentioning

confidence: 99%

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Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

Khan

Katanic

Thakar³

2017

BMC Bioinformatics

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BackgroundDespite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.ResultsWe developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward’s method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.ConclusionWe validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive ‘Fuzzy Inference of Gene-sets (FIGS)’ package (GitHub: https://github.com/Thakar-Lab/FIGS) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1669-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%