Showing 5 of total 5 results (show query)
bioc
scClassify:scClassify: single-cell Hierarchical Classification
scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.
Maintained by Yingxin Lin. Last updated 5 months ago.
singlecellgeneexpressionclassification
23 stars 6.92 score 30 scriptsbioc
Statial:A package to identify changes in cell state relative to spatial associations
Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
Maintained by Farhan Ameen. Last updated 5 months ago.
singlecellspatialclassificationsingle-cell
5 stars 6.49 score 23 scriptsbioc
canceR:A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC
The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
Maintained by Karim Mezhoud. Last updated 5 months ago.
guigeneexpressionclusteringgogenesetenrichmentkeggmultiplecomparisoncancercancer-datagenegene-expressiongene-methylationgene-mutationgene-setsmethylationmskccmutationstcltk
7 stars 5.08 score 17 scriptsbioc
phenoTest:Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.
Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided.
Maintained by Evarist Planet. Last updated 5 months ago.
microarraydifferentialexpressionmultiplecomparisonclusteringclassification
4.56 score 9 scripts 1 dependentsbioc
treekoR:Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations
treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results.
Maintained by Adam Chan. Last updated 5 months ago.
clusteringdifferentialexpressionflowcytometryimmunooncologymassspectrometrysinglecellsoftwarestatisticalmethodvisualization
4.56 score 12 scripts 1 dependents