Survival analysis using gene expression to derive predictive gene signatures is a commonly used feature in research studies employing high throughput genomic data. Gene signatures predictive of overall, relapse free or metastasis free survival are popular and several such signatures are published periodically and the data submitted to public repositories. Data from such studies which is available on the public domain can be leveraged to identify prognostic markers in different cancer types.
Provides a web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. cBioPortal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface, combined with customized data storage, enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes.
Provides various next-generation sequencing (NGS) data analysis applications which are developed or optimized by Illumina, or from a growing ecosystem of third-party app providers. BAseSpace is a cloud platform that can be integrated with the industry’s leading sequencing platforms, without cumbersome or time consuming data transfer steps.
Assesses the effect of genes on survival using cancer samples. KM plotter is a web application, developed for meta-analysis-based biomarker assessment, that can be used for breast, ovarian, lung, gastric, and liver cancer. The software includes patients with a mean follow-up of 69 / 40/ 49 /33 months. The subtool miRpower enables the validation of the prognostic relevance of microRNAs (miRNAs) in breast and liver cancer.
A unified treatment of Breiman's random forests for survival, regression and classification problems. RSF provides a unified treatment of Breiman’s random forests for a variety of data settings. Regression and classification forests are grown when the response is numeric or categorical. Multivariate regression and classification responses as well as mixed outcomes are also handled as are unsupervised forests. Different splitting rules invoked under deterministic or random splitting are available for all families. Variable predictiveness can be assessed using variable importance measures for single, as well as grouped variables. Missing data can be imputed on both training and test data.
Provides a powerful platform for evaluating potential tumor markers and therapeutic targets. PrognoScan is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan searches the relation between gene expression and patient prognosis such as overall survival (OS) and disease free survival (DFS) across a large collection of publicly available cancer microarray datasets.
Provides an assortment of methods to establish and fit a wide range of models. BhGLM offers an R package which is developed to handle about six different types of models including Bayesian hierarchical, negative binomial, or Cox survival models. The application includes features to compute measures to evaluate a given model as well as utilities which serves to numerically and graphically summarize it.
A multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. Genefu provides bioinformatics implementations of classification algorithms to identify molecular subtypes, as well as prognostic predictors along with their published gene signatures. It also includes other functions to facilitate quick manipulation of gene expression datasets, including gene selection and probe-gene mapping across microarray platforms. The genefu package provides a unified framework for integration of molecular subtype and survival analysis of breast cancer. We have demonstrated how the package can be utilized to perform both meta-analyses across datasets and across algorithms, to facilitate integrated analysis of breast cancer gene expression profiles.