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hanjunwei-lab
ICDS:Identification of Cancer Dysfunctional Subpathway with Omics Data
Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
Maintained by Junwei Han. Last updated 8 months ago.
4.54 score 3 scriptshanjunwei-lab
MiRSEA:'MicroRNA' Set Enrichment Analysis
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Maintained by Junwei Han. Last updated 5 years ago.
statisticspathwaysmicrornaenrichment analysis
4.51 score 16 scriptshanjunwei-lab
SMDIC:Identification of Somatic Mutation-Driven Immune Cells
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Maintained by Junwei Han. Last updated 6 months ago.
2 stars 4.00 score 5 scripts
shixinrui
SubpathwayLNCE:Identify Signal Subpathways Competitively Regulated by LncRNAs Based on ceRNA Theory
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
Maintained by Xinrui Shi. Last updated 9 years ago.
statisticssupathwayslncrnasenrichment analysiscerna
2.00 score 2 scripts