Showing 12 of total 12 results (show query)
bioc
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
7.42 score 193 scripts 3 dependentsbioc
signifinder:Collection and implementation of public transcriptional cancer signatures
signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.
Maintained by Stefania Pirrotta. Last updated 3 months ago.
geneexpressiongenetargetimmunooncologybiomedicalinformaticsrnaseqmicroarrayreportwritingvisualizationsinglecellspatialgenesignaling
7 stars 6.28 score 15 scriptsbioc
metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms
Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.
Maintained by Panagiotis Moulos. Last updated 21 days ago.
softwaregeneexpressiondifferentialexpressionworkflowsteppreprocessingqualitycontrolnormalizationreportwritingrnaseqtranscriptionsequencingtranscriptomicsbayesianclusteringcellbiologybiomedicalinformaticsfunctionalgenomicssystemsbiologyimmunooncologyalternativesplicingdifferentialsplicingmultiplecomparisontimecoursedataimportatacseqepigeneticsregressionproprietaryplatformsgenesetenrichmentbatcheffectchipseq
7 stars 6.05 score 3 scriptsbioc
consensusOV:Gene expression-based subtype classification for high-grade serous ovarian cancer
This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
classificationclusteringdifferentialexpressiongeneexpressionmicroarraytranscriptomicscancer-datacancer-genomicscancer-researchexpression-databaseovarian-cancer
3 stars 5.98 score 15 scripts 1 dependents
vdblab
FLORAL:Fit Log-Ratio Lasso Regression for Compositional Data
Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.
Maintained by Teng Fei. Last updated 1 months ago.
12 stars 5.85 score 13 scripts
fbertran
plsRcox:Partial Least Squares Regression for Cox Models and Related Techniques
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Maintained by Frederic Bertrand. Last updated 2 years ago.
4 stars 5.13 score 56 scripts 2 dependents
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 2 months ago.
4.97 score 13 scriptsbioc
PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit
Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
1 stars 4.31 score 17 scripts
bhaibeka
SIGN:Similarity Identification in Gene Expression
Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
Maintained by Benjamin Haibe-Kains. Last updated 6 years ago.
geneexpressionclassificationclusteringsurvival
2.70 score 3 scripts
m-signo
pencal:Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival
Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. PRC is described in Signorelli (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021) <doi:10.1002/sim.9178>.
Maintained by Mirko Signorelli. Last updated 23 days ago.
2.60 score 10 scripts
stefanmichielsgr
biospear:Biomarker Selection in Penalized Regression Models
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
Maintained by Stefan Michiels. Last updated 6 years ago.
1.48 score 3 scripts