Showing 106 of total 106 results (show query)
bioc
TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data
The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.
Maintained by Tiago Chedraoui Silva. Last updated 29 days ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksequencingsurvivalsoftwarebiocbioconductorgdcintegrative-analysistcgatcga-datatcgabiolinks
55.7 match 305 stars 14.45 score 1.6k scripts 6 dependentsbioc
TCGAutils:TCGA utility functions for data management
A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API.
Maintained by Marcel Ramos. Last updated 3 months ago.
softwareworkflowsteppreprocessingdataimportbioconductor-packagetcgau24ca289073utilities
43.5 match 27 stars 9.66 score 210 scripts 10 dependents
openbiox
UCSCXenaShiny:Interactive Analysis of UCSC Xena Data
Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Maintained by Shixiang Wang. Last updated 4 months ago.
cancer-datasetshiny-appsucsc-xena
48.1 match 96 stars 8.54 score 35 scriptsbioc
maftools:Summarize, Analyze and Visualize MAF Files
Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.
Maintained by Anand Mayakonda. Last updated 5 months ago.
datarepresentationdnaseqvisualizationdrivermutationvariantannotationfeatureextractionclassificationsomaticmutationsequencingfunctionalgenomicssurvivalbioinformaticscancer-genome-atlascancer-genomicsgenomicsmaf-filestcgacurlbzip2xz-utilszlib
22.4 match 459 stars 14.63 score 948 scripts 18 dependentsbioc
GenomicDataCommons:NIH / NCI Genomic Data Commons Access
Programmatically access the NIH / NCI Genomic Data Commons RESTful service.
Maintained by Sean Davis. Last updated 1 months ago.
dataimportsequencingapi-clientbioconductorbioinformaticscancercore-servicesdata-sciencegenomicsncitcgavignette
21.5 match 87 stars 11.94 score 238 scripts 12 dependentsbioc
Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data
The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.
Maintained by Matteo Tiberti. Last updated 2 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
25.2 match 5 stars 6.59 score 43 scriptsbioc
MultiAssayExperiment:Software for the integration of multi-omics experiments in Bioconductor
Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis.
Maintained by Marcel Ramos. Last updated 2 months ago.
infrastructuredatarepresentationbioconductorbioconductor-packagegenomicsnci-itcrtcgau24ca289073
11.0 match 71 stars 14.95 score 670 scripts 127 dependentsbioc
ELMER:Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes
ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue.
Maintained by Tiago Chedraoui Silva. Last updated 5 months ago.
dnamethylationgeneexpressionmotifannotationsoftwaregeneregulationtranscriptionnetwork
21.8 match 7.42 score 176 scripts
ropensci
UCSCXenaTools:Download and Explore Datasets from UCSC Xena Data Hubs
Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
Maintained by Shixiang Wang. Last updated 5 months ago.
api-clientbioinformaticsccledownloadericgctcgatoiltreehouseucscucsc-xena
18.4 match 106 stars 8.55 score 163 scripts 1 dependentsbioc
RTCGA:The Cancer Genome Atlas Data Integration
The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use.
Maintained by Marcin Kosinski. Last updated 5 months ago.
immunooncologysoftwaredataimportdatarepresentationpreprocessingrnaseqsurvivaldnamethylationprincipalcomponentvisualization
16.0 match 51 stars 8.91 score 106 scripts 1 dependentsbioc
psichomics:Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation
Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.
Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.
sequencingrnaseqalternativesplicingdifferentialsplicingtranscriptionguiprincipalcomponentsurvivalbiomedicalinformaticstranscriptomicsimmunooncologyvisualizationmultiplecomparisongeneexpressiondifferentialexpressionalternative-splicingbioconductordata-analysesdifferential-gene-expressiondifferential-splicing-analysisgene-expressiongtexrecount2rna-seq-datasplicing-quantificationsratcgavast-toolscpp
19.4 match 36 stars 6.95 score 31 scriptsbioc
TRONCO: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).
Maintained by Luca De Sano. Last updated 5 months ago.
biomedicalinformaticsbayesiangraphandnetworksomaticmutationnetworkinferencenetworkclusteringdataimportsinglecellimmunooncologyalgorithmscancer-inferencetumors
15.5 match 30 stars 6.50 score 38 scriptsbioc
OmicCircos:High-quality circular visualization of omics data
OmicCircos is an R application and package for generating high-quality circular plots for omics data.
Maintained by Ying Hu. Last updated 5 months ago.
visualizationstatisticsannotation
17.9 match 5.20 score 80 scriptsbioc
signeR:Empirical Bayesian approach to mutational signature discovery
The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.
Maintained by Renan Valieris. Last updated 5 months ago.
genomicvariationsomaticmutationstatisticalmethodvisualizationbioconductorbioinformaticsopenblascpp
11.8 match 13 stars 7.67 score 22 scriptsbioc
discordant:The Discordant Method: A Novel Approach for Differential Correlation
Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016).
Maintained by McGrath Max. Last updated 5 months ago.
immunooncologybiologicalquestionstatisticalmethodmrnamicroarraymicroarraygeneticsrnaseqcpp
14.0 match 10 stars 6.05 score 14 scriptsbioc
GeoTcgaData:Processing Various Types of Data on GEO and TCGA
Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.
Maintained by Erqiang Hu. Last updated 5 months ago.
geneexpressiondifferentialexpressionrnaseqcopynumbervariationmicroarraysoftwarednamethylationdifferentialmethylationsnpatacseqmethylationarray
14.4 match 25 stars 5.85 score 19 scriptsbioc
ceRNAnetsim:Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA)
This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables.
Maintained by Selcen Ari Yuka. Last updated 5 months ago.
networkinferencesystemsbiologynetworkgraphandnetworktranscriptomicscernamirnanetwork-biologynetwork-simulatortcgatidygraphtidyverse
14.3 match 4 stars 5.76 score 12 scriptsbioc
iNETgrate:Integrates DNA methylation data with gene expression in a single gene network
The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.
Maintained by Habil Zare. Last updated 5 months ago.
geneexpressionrnaseqdnamethylationnetworkinferencenetworkgraphandnetworkbiomedicalinformaticssystemsbiologytranscriptomicsclassificationclusteringdimensionreductionprincipalcomponentmrnamicroarraynormalizationgenepredictionkeggsurvivalcore-services
12.6 match 74 stars 6.21 score 1 scriptsbioc
MoonlightR:Identify oncogenes and tumor suppressor genes from omics data
Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.
Maintained by Matteo Tiberti. Last updated 5 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
11.0 match 17 stars 6.57 scorebioc
RTCGAToolbox:A new tool for exporting TCGA Firehose data
Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis.
Maintained by Marcel Ramos. Last updated 3 months ago.
differentialexpressiongeneexpressionsequencing
7.3 match 18 stars 9.75 score 76 scripts 5 dependentsbioc
singscore:Rank-based single-sample gene set scoring method
A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.
Maintained by Malvika Kharbanda. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichmentbioinformatics
6.5 match 41 stars 10.03 score 124 scripts 4 dependentsbioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 5 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
4.7 match 182 stars 13.71 score 1.3k scripts 22 dependentsbioc
survClust:Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning
survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types).
Maintained by Arshi Arora. Last updated 5 months ago.
softwareclusteringsurvivalclassificationcpp
11.9 match 16 stars 4.74 score 17 scriptsbioc
NoRCE:NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.
Maintained by Gulden Olgun. Last updated 5 months ago.
biologicalquestiondifferentialexpressiongenomeannotationgenesetenrichmentgenetargetgenomeassemblygo
12.2 match 1 stars 4.60 score 6 scriptsbioc
MOSClip:Multi Omics Survival Clip
Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.
Maintained by Paolo Martini. Last updated 5 months ago.
softwarestatisticalmethodgraphandnetworksurvivalregressiondimensionreductionpathwaysreactome
10.4 match 5.34 score 5 scriptsbioc
MethReg:Assessing the regulatory potential of DNA methylation regions or sites on gene transcription
Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.
Maintained by Tiago Silva. Last updated 5 months ago.
methylationarrayregressiongeneexpressionepigeneticsgenetargettranscription
9.7 match 5 stars 5.45 score 19 scriptsbioc
SCANVIS:SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions
SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control).
Maintained by Phaedra Agius. Last updated 5 months ago.
softwareresearchfieldtranscriptomicsworkflowstepannotationvisualization
13.0 match 4.00 score 2 scriptsbioc
CaDrA:Candidate Driver Analysis
Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events.
Maintained by Reina Chau. Last updated 5 months ago.
microarrayrnaseqgeneexpressionsoftwarefeatureextraction
7.0 match 24 stars 7.19 score 12 scripts
person-c
easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.
Maintained by Wei Cui. Last updated 15 days ago.
limmageoqueryedgerfgseabioinformaticscellmarker2gsearna-seqsingle-cell
6.9 match 10 stars 6.62 score 35 scripts
md-anderson-bioinformatics
NGCHMDemoData:Demo Data for the NGCHM R Package
Package of demo data for NGCHM vignettes.
Maintained by Mary A Rohrdanz. Last updated 9 months ago.
20.5 match 2.20 score 16 scriptsbioc
CTdata:Data companion to CTexploreR
Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics.
Maintained by Laurent Gatto. Last updated 5 months ago.
transcriptomicsepigeneticsgeneexpressiondataimportexperimenthubsoftware
7.5 match 1 stars 5.69 score 1 scripts 1 dependentsbioc
GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.
Maintained by Ruidong Li. Last updated 5 months ago.
immunooncologygeneexpressiondifferentialexpressiongeneregulationgenetargetnetworkinferencesurvivalvisualizationgenesetenrichmentnetworkenrichmentnetworkrnaseqgokegg
7.2 match 5.64 score 44 scripts
blasseigne
ProliferativeIndex:Calculates and Analyzes the Proliferative Index
Provides functions for calculating and analyzing the proliferative index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, et al. bioRxiv, 2016 <doi:10.1101/063057>.
Maintained by Brittany Lasseigne. Last updated 7 years ago.
cancercancer-genomicsgene-expressiongenomicsindexmetagene
10.6 match 3.70 score 10 scriptsbioc
GenomicSuperSignature:Interpretation of RNA-seq experiments through robust, efficient comparison to public databases
This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package.
Maintained by Sehyun Oh. Last updated 5 months ago.
transcriptomicssystemsbiologyprincipalcomponentrnaseqsequencingpathwaysclusteringbioconductor-packageexploratory-data-analysisgseameshprincipal-component-analysisrna-sequencing-profilestransferlearning
5.6 match 16 stars 6.97 score 59 scriptsbioc
sevenbridges:Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R
R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms.
Maintained by Phil Webster. Last updated 5 months ago.
softwaredataimportthirdpartyclientapi-clientbioconductorbioinformaticscloudcommon-workflow-languagesevenbridges
4.8 match 35 stars 7.40 score 24 scriptsbioc
CTexploreR:Explores Cancer Testis Genes
The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues.
Maintained by Axelle Loriot. Last updated 5 months ago.
transcriptomicsepigeneticsdifferentialexpressiongeneexpressiondnamethylationexperimenthubsoftwaredataimportbioconductor
7.0 match 5.02 score 2 scripts
evanbiederstedt
dendsort:Modular Leaf Ordering Methods for Dendrogram Nodes
An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 <doi:10.12688/f1000research.4784.1>.
Maintained by Evan Biederstedt. Last updated 4 years ago.
4.7 match 4 stars 7.01 score 472 scripts 3 dependentsbioc
MethylMix:MethylMix: Identifying methylation driven cancer genes
MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8.
Maintained by Olivier Gevaert. Last updated 5 months ago.
dnamethylationstatisticalmethoddifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetwork
6.7 match 4.72 score 26 scriptsbioc
BOBaFIT:Refitting diploid region profiles using a clustering procedure
This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster.
Maintained by Gaia Mazzocchetti. Last updated 5 months ago.
copynumbervariationclusteringvisualizationnormalizationsoftware
7.8 match 3.90 score 3 scriptsevanbiederstedt
gapmap:Drawing Gapped Cluster Heatmaps with 'ggplot2'
The gap encodes the distance between clusters and improves interpretation of cluster heatmaps. The gaps can be of the same distance based on a height threshold to cut the dendrogram. Another option is to vary the size of gaps based on the distance between clusters.
Maintained by Evan Biederstedt. Last updated 1 years ago.
6.6 match 2 stars 4.62 score 21 scriptsropensci
cRegulome:Obtain and Visualize Regulome-Gene Expression Correlations in Cancer
Builds a 'SQLite' database file of pre-calculated transcription factor/microRNA-gene correlations (co-expression) in cancer from the Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 'miRCancerdb' databases (in press). Provides custom classes and functions to query, tidy and plot the correlation data.
Maintained by Mahmoud Ahmed. Last updated 5 years ago.
cancer-genomicsdatabasedatasciencemicrornapeer-reviewedtcga-datatranscription-factors
7.5 match 3 stars 3.69 score 54 scripts
alinetalhouk
diceR:Diverse Cluster Ensemble in R
Performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.
Maintained by Derek Chiu. Last updated 1 months ago.
3.4 match 37 stars 8.13 score 60 scripts 3 dependents
l0ka
TPES:Tumor Purity Estimation using SNVs
A bioinformatics tool for the estimation of the tumor purity from sequencing data. It uses the set of putative clonal somatic single nucleotide variants within copy number neutral segments to call tumor cellularity.
Maintained by Alessio Locallo. Last updated 6 years ago.
20.9 match 2 stars 1.30 score 10 scriptsbioc
GSALightning:Fast Permutation-based Gene Set Analysis
GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation.
Maintained by Billy Heung Wing Chang. Last updated 5 months ago.
softwarebiologicalquestiongenesetenrichmentdifferentialexpressiongeneexpressiontranscription
6.8 match 5 stars 4.00 score 4 scripts
dadongz
OncoSubtype:Predict Cancer Subtypes Based on TCGA Data using Machine Learning Method
Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.
Maintained by Dadong Zhang. Last updated 12 months ago.
7.1 match 1 stars 3.70 score 1 scriptsbioc
SpatialDecon:Deconvolution of mixed cells from spatial and/or bulk gene expression data
Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.
Maintained by Maddy Griswold. Last updated 5 months ago.
immunooncologyfeatureextractiongeneexpressiontranscriptomicsspatial
3.5 match 36 stars 7.40 score 58 scriptsbioc
roastgsa:Rotation based gene set analysis
This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations.
Maintained by Adria Caballe. Last updated 5 months ago.
microarraypreprocessingnormalizationgeneexpressionsurvivaltranscriptionsequencingtranscriptomicsbayesianclusteringregressionrnaseqmicrornaarraymrnamicroarrayfunctionalgenomicssystemsbiologyimmunooncologydifferentialexpressiongenesetenrichmentbatcheffectmultiplecomparisonqualitycontroltimecoursemetabolomicsproteomicsepigeneticscheminformaticsexonarrayonechanneltwochannelproprietaryplatformscellbiologybiomedicalinformaticsalternativesplicingdifferentialsplicingdataimportpathways
11.3 match 2.30 scorebioc
terraTCGAdata:OpenAccess TCGA Data on Terra as MultiAssayExperiment
Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra.
Maintained by Marcel Ramos. Last updated 5 months ago.
softwareinfrastructuredataimportbioconductor-package
5.6 match 4.60 score 4 scriptsbioc
oncomix:Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data
This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes.
Maintained by Daniel Pique. Last updated 5 months ago.
6.6 match 3.78 score 4 scripts
eonurk
seAMLess:A Single Cell Transcriptomics Based Deconvolution Pipeline for Leukemia
Given a bulk transcriptomic (RNA-seq) sample of an Myeloid Leukemia patient calculates immune composition and drug resistance for different small-molecule inhibitors. Published in <https://www.nature.com/articles/s41698-024-00596-9>.
Maintained by E Onur Karakaslar. Last updated 3 months ago.
6.8 match 2 stars 3.60 score 7 scripts
dami82
TCGAretriever:Retrieve Genomic and Clinical Data from CBioPortal Including TCGA Data
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <https://www.cbioportal.org/> for more information).
Maintained by Damiano Fantini. Last updated 1 years ago.
5.5 match 4.11 score 26 scripts
uclouvain-cbio
rWSBIM1207:Companion Package for WSBIM1207 Course
Companion package for the WSBIM1207 course, distributing data and general documentation, and making course administration easier.
Maintained by Laurent Gatto. Last updated 9 months ago.
11.3 match 2.00 score 7 scriptsbioc
minfi:Analyze Illumina Infinium DNA methylation arrays
Tools to analyze & visualize Illumina Infinium methylation arrays.
Maintained by Kasper Daniel Hansen. Last updated 4 months ago.
immunooncologydnamethylationdifferentialmethylationepigeneticsmicroarraymethylationarraymultichanneltwochanneldataimportnormalizationpreprocessingqualitycontrol
1.8 match 60 stars 12.83 score 996 scripts 26 dependentsbioc
padma:Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis
Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles.
Maintained by Andrea Rau. Last updated 5 months ago.
softwarestatisticalmethodprincipalcomponentgeneexpressionpathwaysrnaseqbiocartamethylseq
4.4 match 3 stars 4.95 score 2 scriptsbioc
Linnorm:Linear model and normality based normalization and transformation method (Linnorm)
Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.
Maintained by Shun Hang Yip. Last updated 5 months ago.
immunooncologysequencingchipseqrnaseqdifferentialexpressiongeneexpressiongeneticsnormalizationsoftwaretranscriptionbatcheffectpeakdetectionclusteringnetworksinglecellcpp
3.3 match 6.26 score 61 scripts 5 dependents
vonnwalter23
MVisAGe:Compute and Visualize Bivariate Associations
Pearson and Spearman correlation coefficients are commonly used to quantify the strength of bivariate associations of genomic variables. For example, correlations of gene-level DNA copy number and gene expression measurements may be used to assess the impact of DNA copy number changes on gene expression in tumor tissue. 'MVisAGe' enables users to quickly compute and visualize the correlations in order to assess the effect of regional genomic events such as changes in DNA copy number or DNA methylation level. Please see Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research <doi:10.1158/0008-5472.CAN-17-3464>.
Maintained by Vonn Walter. Last updated 7 years ago.
7.5 match 2.74 score 11 scripts
cran
iDINGO:Integrative Differential Network Analysis in Genomics
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Maintained by Caleb A. Class. Last updated 5 years ago.
7.3 match 3 stars 2.78 score
qianli10000
GMSimpute:Generalized Mass Spectrum Missing Peaks Abundance Imputation
GMSimpute implements the Two-Step Lasso (TS-Lasso) and compound minimum to recover the abundance of missing peaks in mass spectrum analysis. TS-Lasso is a label-free imputation method that handles various types of missing peaks simultaneously. This package provides the procedure to generate missing peaks (or data) for simulation study, as well as a tool to estimate and visualize the proportion of missing at random.
Maintained by Qian Li. Last updated 6 years ago.
6.6 match 3.06 score 23 scripts
ccicb
CRUX:Easily explore patterns of somatic variation in cancer using 'CRUX'
Shiny app for exploring somatic variation in cancer. Powered by maftools.
Maintained by Sam El-Kamand. Last updated 1 years ago.
9.8 match 2 stars 2.00 score 5 scriptsbioc
supersigs:Supervised mutational signatures
Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).
Maintained by Albert Kuo. Last updated 5 months ago.
featureextractionclassificationregressionsequencingwholegenomesomaticmutation
4.0 match 3 stars 4.78 score 3 scripts
oconnellmj
r.jive:Perform JIVE Decomposition for Multi-Source Data
Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Maintained by Michael J. OConnell. Last updated 4 years ago.
6.0 match 2 stars 3.18 score 75 scriptsbioc
SplicingFactory:Splicing Diversity Analysis for Transcriptome Data
The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions.
Maintained by Endre Sebestyen. Last updated 5 months ago.
transcriptomicsrnaseqdifferentialsplicingalternativesplicingtranscriptomevariantgini-indexrna-seqshannon-entropysimpson-indexsplicing
3.6 match 4 stars 5.20 score 1 scriptsbioc
SPONGE:Sparse Partial Correlations On Gene Expression
This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Maintained by Markus List. Last updated 5 months ago.
geneexpressiontranscriptiongeneregulationnetworkinferencetranscriptomicssystemsbiologyregressionrandomforestmachinelearning
3.5 match 5.36 score 38 scripts 1 dependentsbioc
EpiMix:EpiMix: an integrative tool for the population-level analysis of DNA methylation
EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs.
Maintained by Yuanning Zheng. Last updated 5 months ago.
softwareepigeneticspreprocessingdnamethylationgeneexpressiondifferentialmethylation
4.0 match 1 stars 4.48 score 7 scripts 1 dependentsbioc
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
3.4 match 7 stars 5.25 score 17 scriptsbioc
epivizr:R Interface to epiviz web app
This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well.
Maintained by Hector Corrada Bravo. Last updated 5 months ago.
visualizationinfrastructuregui
3.3 match 5.24 score 29 scripts 2 dependents
shixiangwang
IDConverter:Convert Identifiers in Biological Databases
Identifiers in biological databases connect different levels of metadata, phenotype data or genotype data. This tool is designed to easily convert identifiers within or between different biological databases (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557>).
Maintained by Shixiang Wang. Last updated 2 years ago.
5.8 match 9 stars 3.00 score 22 scriptsbioc
messina:Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems
Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.
Maintained by Mark Pinese. Last updated 5 months ago.
geneexpressiondifferentialexpressionbiomedicalinformaticsclassificationsurvivalcpp
5.1 match 3.30 score 1 scriptsbioc
methylumi:Handle Illumina methylation data
This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included.
Maintained by Sean Davis. Last updated 5 months ago.
dnamethylationtwochannelpreprocessingqualitycontrolcpgisland
1.7 match 9 stars 9.90 score 89 scripts 9 dependentsbioc
shinyMethyl:Interactive visualization for Illumina methylation arrays
Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported.
Maintained by Jean-Philippe Fortin. Last updated 5 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrolmethylationarray
2.3 match 5 stars 7.34 score 42 scriptsbioc
iPath:iPath pipeline for detecting perturbed pathways at individual level
iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes.
Maintained by Kenong Su. Last updated 5 months ago.
pathwayssoftwaregeneexpressionsurvivalcpp
3.5 match 2 stars 4.60 score 3 scripts
sahirbhatnagar
eclust:Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data
Companion package to the paper: An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
Maintained by Sahir Rai Bhatnagar. Last updated 8 years ago.
3.4 match 2 stars 4.62 score 14 scriptsccicb
TCGAgistic:Easily access TCGA gistic data
Streams TCGA GISTIC2 copynumber data into the R session.
Maintained by Sam El-Kamand. Last updated 2 years ago.
5.4 match 5 stars 2.88 score 8 scripts 1 dependentsbioc
epivizrChart:R interface to epiviz web components
This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer.
Maintained by Hector Corrada Bravo. Last updated 5 months ago.
3.3 match 4.68 score 12 scriptsbioc
gINTomics:Multi-Omics data integration
gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app.
Maintained by Angelo Velle. Last updated 5 months ago.
geneexpressionrnaseqmicroarrayvisualizationcopynumbervariationgenetargetquarto
3.1 match 3 stars 5.08 score 3 scriptsbioc
RESOLVE:RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes
Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures.
Maintained by Luca De Sano. Last updated 5 months ago.
biomedicalinformaticssomaticmutation
3.3 match 1 stars 4.60 score 3 scriptsbioc
CPSM:CPSM: Cancer patient survival model
The CPSM package provides a comprehensive computational pipeline for predicting the survival probability of cancer patients. It offers a series of steps including data processing, splitting data into training and test subsets, and normalization of data. The package enables the selection of significant features based on univariate survival analysis and generates a LASSO prognostic index score. It supports the development of predictive models for survival probability using various features and provides visualization tools to draw survival curves based on predicted survival probabilities. Additionally, SPM includes functionalities for generating bar plots that depict the predicted mean and median survival times of patients, making it a versatile tool for survival analysis in cancer research.
Maintained by Harpreet Kaur. Last updated 7 days ago.
geneexpressionnormalizationsurvival
3.6 match 3.90 scorebioc
netboost:Network Analysis Supported by Boosting
Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein.
Maintained by Pascal Schlosser. Last updated 5 months ago.
softwarestatisticalmethodgraphandnetworknetworkclusteringdimensionreductionbiomedicalinformaticsepigeneticsmetabolomicstranscriptomicscpp
3.3 match 4.18 score 1 scripts
vishaloza
CINmetrics:Calculate Chromosomal Instability Metrics
Implement various chromosomal instability metrics. 'CINmetrics' (Chromosomal INstability metrics) provides functions to calculate various chromosomal instability metrics on masked Copy Number Variation(CNV) data at individual sample level. The chromosomal instability metrics have been implemented as described in the following studies: Baumbusch LO et al. 2013 <doi:10.1371/journal.pone.0054356>, Davidson JM et al. 2014 <doi:10.1371/journal.pone.0079079>, Chin SF et al. 2007 <doi:10.1186/gb-2007-8-10-r215>.
Maintained by Vishal H. Oza. Last updated 4 years ago.
4.8 match 2.70 score 8 scriptsbioc
genefu:Computation of Gene Expression-Based Signatures in Breast Cancer
This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.
Maintained by Benjamin Haibe-Kains. Last updated 4 months ago.
differentialexpressiongeneexpressionvisualizationclusteringclassification
1.7 match 7.42 score 193 scripts 3 dependents
parklab
Nozzle.R1:Nozzle Reports
The Nozzle package provides an API to generate HTML reports with dynamic user interface elements based on JavaScript and CSS (Cascading Style Sheets). Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big data sets. The package can be applied to any project where user-friendly reports need to be created.
Maintained by Nils Gehlenborg. Last updated 10 years ago.
gehlenborglabhtml-reportreproducible-research
2.3 match 68 stars 5.31 score 10 scripts 2 dependentsbioc
CNViz:Copy Number Visualization
CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.
Maintained by Rebecca Greenblatt. Last updated 5 months ago.
visualizationcopynumbervariationsequencingdnaseq
3.6 match 3.30 score 1 scriptscran
whitening:Whitening and High-Dimensional Canonical Correlation Analysis
Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>, as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).
Maintained by Korbinian Strimmer. Last updated 3 years ago.
4.5 match 2.59 score 65 scripts 2 dependentsbioc
MOMA:Multi Omic Master Regulator Analysis
This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions.
Maintained by Sunny Jones. Last updated 5 months ago.
softwarenetworkenrichmentnetworkinferencenetworkfeatureextractionclusteringfunctionalgenomicstranscriptomicssystemsbiology
1.6 match 6 stars 6.19 score 13 scriptsbioc
OutSplice:Comparison of Splicing Events between Tumor and Normal Samples
An easy to use tool that can compare splicing events in tumor and normal tissue samples using either a user generated matrix, or data from The Cancer Genome Atlas (TCGA). This package generates a matrix of splicing outliers that are significantly over or underexpressed in tumors samples compared to normal denoted by chromosome location. The package also will calculate the splicing burden in each tumor and characterize the types of splicing events that occur.
Maintained by Theresa Guo. Last updated 1 months ago.
alternativesplicingdifferentialexpressiondifferentialsplicinggeneexpressionrnaseqsoftwarevariantannotation
2.3 match 1 stars 4.30 score 4 scriptsbioc
SurfR:Surface Protein Prediction and Identification
Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.
Maintained by Aurora Maurizio. Last updated 4 days ago.
softwaresequencingrnaseqgeneexpressiontranscriptiondifferentialexpressionprincipalcomponentgenesetenrichmentpathwaysbatcheffectfunctionalgenomicsvisualizationdataimportfunctionalpredictiongenepredictiongodgeenrichment-analysismetaanalysisplotsproteinspublic-datasurfacesurfaceome
1.7 match 3 stars 5.43 score 3 scriptsbioc
M3C:Monte Carlo Reference-based Consensus Clustering
M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.
Maintained by Christopher John. Last updated 5 months ago.
clusteringgeneexpressiontranscriptionrnaseqsequencingimmunooncology
1.3 match 6.59 score 174 scripts 1 dependentsbioc
TDbasedUFEadv:Advanced package of tensor decomposition based unsupervised feature extraction
This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.
Maintained by Y-h. Taguchi. Last updated 5 months ago.
geneexpressionfeatureextractionmethylationarraysinglecellsoftwarebioconductor-packagebioinformaticstensor-decomposition
1.8 match 4.48 score 4 scripts
r-forge
plasma:Partial LeAst Squares for Multiomic Analysis
Contains tools for supervised analyses of incomplete, overlapping multiomics datasets. Applies partial least squares in multiple steps to find models that predict survival outcomes. See Yamaguchi et al. (2023) <doi:10.1101/2023.03.10.532096>.
Maintained by Kevin R. Coombes. Last updated 1 months ago.
1.6 match 4.97 score 13 scripts
bioc
RTN:RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons
A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.
Maintained by Mauro Castro. Last updated 5 months ago.
transcriptionnetworknetworkinferencenetworkenrichmentgeneregulationgeneexpressiongraphandnetworkgenesetenrichmentgeneticvariability
1.3 match 5.80 score 53 scripts 2 dependentsbioc
CNVRanger:Summarization and expression/phenotype association of CNV ranges
The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
copynumbervariationdifferentialexpressiongeneexpressiongenomewideassociationgenomicvariationmicroarrayrnaseqsnpbioconductor-packageu24ca289073
1.2 match 7 stars 5.77 score 12 scripts
mikehellstern
netgsa:Network-Based Gene Set Analysis
Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>
Maintained by Michael Hellstern. Last updated 3 years ago.
1.8 match 4 stars 3.75 score 28 scripts
kaiaragaki
classifyBLCA:What the Package Does (One Line, Title Case)
What the package does (one paragraph).
Maintained by Kai Aragaki. Last updated 2 years ago.
3.8 match 1.70 scorebioc
DeMixT:Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms
DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.
Maintained by Ruonan Li. Last updated 5 months ago.
softwarestatisticalmethodclassificationgeneexpressionsequencingmicroarraytissuemicroarraycoveragecppopenmp
1.1 match 5.27 score 25 scripts
rajkumpismb
PCAPAM50:Enhanced 'PAM50' Subtyping of Breast Cancer
Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.
Maintained by Praveen-Kumar Raj-Kumar. Last updated 2 months ago.
2.3 match 2.48 score 3 scriptscran
TPAC:Tissue-Adjusted Pathway Analysis of Cancer (TPAC)
Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Maintained by H. Robert Frost. Last updated 1 years ago.
2.8 match 2.00 scorebioc
MatrixQCvis:Shiny-based interactive data-quality exploration for omics data
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.
Maintained by Thomas Naake. Last updated 5 months ago.
visualizationshinyappsguiqualitycontroldimensionreductionmetabolomicsproteomicstranscriptomics
1.2 match 4.74 score 4 scriptsbioc
ANF:Affinity Network Fusion for Complex Patient Clustering
This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion.
Maintained by Tianle Ma. Last updated 5 months ago.
clusteringgraphandnetworknetwork
1.2 match 4.30 score 9 scriptscran
TPACData:Human Protein Atlas Data for Tissue-Adjusted Pathway Analysis of Cancer (TPAC)
Contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the Tissue-Adjusted Pathway Analysis of cancer (TPAC) method. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Maintained by H. Robert Frost. Last updated 1 years ago.
3.1 match 1.48 score 1 dependents
yuande
signatureSurvival:Signature Survival Analysis
When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots.
Maintained by Yuan-De Tan. Last updated 2 years ago.
3.8 match 1 stars 1.00 scorebioc
easier:Estimate Systems Immune Response from RNA-seq data
This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy.
Maintained by Oscar Lapuente-Santana. Last updated 5 months ago.
geneexpressionsoftwaretranscriptionsystemsbiologypathwaysgenesetenrichmentimmunooncologyepigeneticsclassificationbiomedicalinformaticsregressionexperimenthubsoftware
0.5 match 4.20 score 16 scriptscran
InterSIM:Simulation of Inter-Related Genomic Datasets
Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.
Maintained by Prabhakar Chalise. Last updated 2 months ago.
0.5 match 2.26 score 2 dependents