Jambusaria, Ankit Klomp, Jeff Hong, Zhigang Rafii, Shahin Dai, Yang Malik, Asrar Rehman, Jalees Additional file 1: of A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks Figure S1. A) Hierarchical clustering of Endothelial cells from 7 mouse organs Intra- and inter-tissue heterogeneity. Tree plot generated via hierarchical clustering of 500 most variable genes across all distinct tissue endothelial cell samples B) Hierarchical clustering of Neuronal cells from 5 different regions of the mouse forebrain Intra- and inter-tissue heterogeneity. Tree plot generated via hierarchical clustering of 500 most variable genes across all distinct tissue neuronal cell samples. Figure S2. Comparison of statistical power and type-I error rate between HeteroPath, GSEA, and PGSEA for DE Gene Set size of 50 genes. The averaged results of 500 simulations are depicted as function of the sample size on the x-axis, for each of the methods. On the y-axis either the statistical power or the empirical type-I error rate is shown. GSE scores were calculated with each method with respect to two gene sets, one of them differentially expressed (DE) and the other one not. Statistical power and empirical type-I error rates were estimated by performing an ANOVA on the DE and non-DE gene sets, respectively, at a significance level of α = 0.05. Figure S3. Comparison of statistical power and type-I error rate between HeteroPath, GSEA, and PGSEA for DE Gene Set size of 150 genes. The averaged results of 500 simulations are depicted as function of the sample size on the x-axis, for each of the methods. On the y-axis either the statistical power or the empirical type-I error rate is shown. GSE scores were calculated with each method with respect to two gene sets, one of them differentially expressed (DE) and the other one not. Statistical power and empirical type-I error rates were estimated by performing an ANOVA on the DE and non-DE gene sets, respectively, at a significance level of α = 0.05. Figure S4. A) Enriched Wnt Signaling Motifs from Brain endothelial cells The table shows the five most enriched motifs in ChIP-seq peaks and the associated transcription factors. Significance values and significant p-values (p ≤ 0.05) are shown. B) Enriched Oxidative Phosphorylation Motifs from Hippocampal Neurons The table shows the five most enriched motifs in ChIP-seq peaks and the associated transcription factors. Significance values and significant p-values (p ≤ 0.05) are shown. (PPTX 1265 kb) Gene set enrichment;Systems biology;Tissue specificity;Gene expression;Transcriptional networks;Transcription factor binding motifs;Pathway analysis;Therapeutic targets;Endothelial cells;Endothelial heterogeneity;Neurons;Neuronal heterogeneity;Vascular biology 2018-06-07
    https://springernature.figshare.com/articles/presentation/Additional_file_1_of_A_computational_approach_to_identify_cellular_heterogeneity_and_tissue-specific_gene_regulatory_networks/6679493
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