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.
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.
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.
Allows to estimate generative properties for the Lehmann subtypes, an alternative Triple-Negative Breast Cancer (TNBC) subtyping scheme. STROMA4 can measure four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These four stromal represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D).
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.
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.
Performs survival analyses and draws Kaplan–Meier (KM) plots for submitted ‘microRNA' across several available data sets, which cover more than 800 patients. A robust statistical procedure is implemented to account for multiple testing. MIRUMIR is incorporated into BioProfiling.de, analytical portal for high-throughput cell biology. MIRUMIR supports the need of biomedical researchers to estimate the power of miR to serve as potential biomarker to predict survival of cancer patients. MIRUMIR provides such analyses based on several publicly available clinical miR data sets annotated with patient survival information.
An online tool for statistical validation of hypotheses regarding the effect of p53 mutational status on gene regulation in cancer. p53MutaGene is based on several large-scale clinical gene expression data sets and currently covers breast, colon and lung cancers. The tool detects differential co-expression patterns in expression data between p53 mutated versus p53 normal samples for the user-specified genes. Statistically significant differential co-expression for a gene pair is indicative that regulation of two genes is sensitive to the presence of p53 mutations. p53MutaGene can be used in 'single mode' where the user can test a specific pair of genes or in 'discovery mode' designed for analysis of several genes.
Provides functions to assess and statistically compare the performance of survival/risk prediction models. survcomp implements state-of-the-art statistics to (i) measure the performance of risk prediction models; (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework; and (iii) statistically compare the performance of competitive models.
Determines multiple putative mediators. MultiMed is an R package that supplies a permutation method able to hold specified family-wise error rates (FWER) and false discovery rates (FDR). This approach can be used for estimating high dimensional biomarkers. It was tested on a study that investigates relationships between increased body-mass index (BMI) and an increased risk of breast cancer.
Assists users in realizing classification on the genomics data. This algorithm is a convolutional neural network that was developed to discover potential biomarkers for each tumor type. It works in several steps: it (i) preprocesses the input data, (ii) makes tumor type classification using a convolutional neural network, (iii) generates the heat map for each class and picks the genes corresponding to top intensities in the heat-maps and (iv) validates the pathways of selected genes.
Provides a toolkit for gene/protein list interpretation. BioProfiling.de is an analytical web portal that provides a user-friendly dialog-driven web interface and supports gene/protein identifiers. It implements two statistical frameworks which allows implementation of tools capable to explore biological principles to group genes into functional classes or to associate genes by edge into global gene network.
A command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported.
Builds a risk prediction signature for a specific stratum by down-weighting the observations from the other strata using a range of weights. CoxBoost actively controls the extent to which each stratum contributes to the variable selection and estimation of regression coefficients. It also focuses on building a risk prediction signature for a specific stratum by down-weighting the observations from the other strata using a range of weights. CoxBoost was designed to identify clusters of variables that either are important only in the stratum of interest or are also important to some extent in the other strata.
A user-friendly online tool that allows rapid assessment of gene expression levels, identification of co-expressed genes and association with outcome for single genes, gene sets or gene signatures in an 1881-sample breast cancer data set. Moreover, GOBO offers the possibility of investigation of gene expression levels in breast cancer subgroups and breast cancer cell lines for gene sets, as well as creation of potential metagenes based on iterative correlation analysis to a prototype gene. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform in the form of precompiled R-data sets.
Allows researchers to use publicly available data to study prognostic implications of genes of interest in multiple cancers. PROGgene is a survival analysis tool for performing biomarker identification with a large repository of public datasets. Users can upload their own gene expression datasets. The software can be used to conduct on the fly survival analysis and create survival plots (Kaplan Meier, KM plots) based on gene expression of user input genes in user selected datasets from multiple cancers.
A user-friendly open-source bioinformatic resource, which provides a set of analytic tools for the discovery and in silico evaluation of novel prognostic and predictive cancer biomarkers based on integration and re-use of gene expression signature in the context of follow-up data. The desktop client app is now supported by a dedicated web server for the statistical and computational analysis of very large databases. Furthermore, we have refurbished its graphical interface, added new visualization tools and up-graded the BioPlat data bases. BioPlat facilitates the integration, analysis, validation and feature selection of gene signatures derived from different databases in the context of follow-up data obtained from publicly available gene expression profiling repositories.
Automatically derives the currently known interactome for a gene of interest and correlates expression levels of its interactome, with survival outcome in multiple publicly available clinical expression data sets. PPISURV automatically correlates expression of an input gene interactome with survival rates on >40 publicly available clinical expression data sets covering various tumours involving about 8000 patients in total. To derive the query gene interactome, PPISURV employs several public databases including protein-protein interactions, regulatory and signalling pathways and protein post-translational modifications.
Supplies a solution for performing meta-analysis of gene expression omnibus (GEO) datasets. ImaGEO provides a step-by-step workflow that guides users through the entire analysis accelerating the re-use of publicly available gene expression data for biomarker discovery purposes. It can be used for analyzing inverse gene expression patterns among phenotypes.
Assesses the performance of prognostic microRNAs (miRNA)-based biomarkers. SurvMicro is a web tool of miRNA expression levels associated with clinical outcome that provides survival analysis and risk assessment in cancer. The software is composed of a database of about 400 cohorts in different tissues and a web tool where survival analysis can be done. It is able to produce information for the validation of miRNA signatures in various cohorts.
A Bayesian ensemble method for survival prediction in high-dimensional gene expression data. This non-parametric method incorporates both additive and interaction effects between genes, which results in high predictive accuracy compared with other methods. In addition, SurvBART provides model-free variable selection of important prognostic markers based on controlling the false discovery rates; thus providing a unified procedure to select relevant genes and predict survivor functions.
Topics (10): Transcription analysis, RNA-seq analysis, Breast Neoplasms, Breast Diseases, Neoplasm Metastasis, Neoplastic Processes, Neoplasms, Central Nervous System Neoplasms, Nervous System Neoplasms, Brain Diseases