SIMLR - Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.
Last updated 25 days ago
immunooncologyclusteringgeneexpressionsequencingsinglecell
8.47 score 109 stars 68 scripts 348 downloadsTRONCO - TRONCO, an R package for TRanslational ONCOlogy
The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).
Last updated 25 days ago
biomedicalinformaticsbayesiangraphandnetworksomaticmutationnetworkinferencenetworkclusteringdataimportsinglecellimmunooncologyalgorithmscancer-inferencetumors
8.30 score 28 stars 36 scripts 250 downloadsSparseSignatures - SparseSignatures
Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients.
Last updated 25 days ago
biomedicalinformaticssomaticmutation
6.38 score 10 stars 4 scripts 205 downloadsOncoScore - A tool to identify potentially oncogenic genes
OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries.
Last updated 25 days ago
biomedicalinformatics
6.15 score 5 stars 2 scripts 236 downloads