Showing 16 of total 16 results (show query)
bioc
PharmacoGx:Analysis of Large-Scale Pharmacogenomic Data
Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line.
Maintained by Benjamin Haibe-Kains. Last updated 3 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationdatasetspharmacogenomicpharmacogxcpp
68 stars 11.39 score 442 scripts 3 dependentsbioc
scDesign3:A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
Maintained by Dongyuan Song. Last updated 28 days ago.
softwaresinglecellsequencinggeneexpressionspatial
89 stars 7.59 score 25 scriptsbioc
gDRimport:Package for handling the import of dose-response data
The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats.
Maintained by Arkadiusz Gladki. Last updated 3 days ago.
softwareinfrastructuredataimport
3 stars 7.32 score 5 scripts 1 dependentsbioc
cliqueMS:Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data
Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite.
Maintained by Oriol Senan Campos. Last updated 5 months ago.
metabolomicsmassspectrometrynetworknetworkinferencecpp
12 stars 6.91 score 25 scripts
philboileau
cvCovEst:Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.
Maintained by Philippe Boileau. Last updated 1 years ago.
covariance-matrix-estimationcross-validationhigh-dimensional-statisticsnonparametric-statistics
13 stars 6.78 score 26 scripts 2 dependents
asalavaty
influential:Identification and Classification of the Most Influential Nodes
Contains functions for the classification and ranking of top candidate features, reconstruction of networks from adjacency matrices and data frames, analysis of the topology of the network and calculation of centrality measures, and identification of the most influential nodes. Also, a function is provided for running SIRIR model, which is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment of dependence and correlation of two network centrality measures as well as the conditional probability of deviation from their corresponding means in opposite direction. Fred Viole and David Nawrocki (2013, ISBN:1490523995). Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695. Adopted algorithms and sources are referenced in function document.
Maintained by Adrian Salavaty. Last updated 6 months ago.
centrality-measuresclassification-modelinfluence-rankingnetwork-analysispriaritization-model
27 stars 6.54 score 43 scripts 1 dependentsbioc
Xeva:Analysis of patient-derived xenograft (PDX) data
The Xeva package provides efficient and powerful functions for patient-drived xenograft (PDX) based pharmacogenomic data analysis. This package contains a set of functions to perform analysis of patient-derived xenograft data. This package was developed by the BHKLab, for further information please see our documentation.
Maintained by Benjamin Haibe-Kains. Last updated 9 days ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassification
11 stars 6.48 score 17 scriptsbioc
scPCA:Sparse Contrastive Principal Component Analysis
A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.
Maintained by Philippe Boileau. Last updated 2 months ago.
principalcomponentgeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqbioconductorcontrastive-learningdimensionality-reduction
12 stars 5.94 score 29 scriptsbioc
ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App
ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.
Maintained by Pacome Prompsy. Last updated 5 months ago.
shinyappssoftwaresinglecellchipseqatacseqmethylseqclassificationclusteringepigeneticsprincipalcomponentannotationbatcheffectmultiplecomparisonnormalizationpathwayspreprocessingqualitycontrolreportwritingvisualizationgenesetenrichmentdifferentialpeakcallingepigenomicsshinysingle-cellcpp
14 stars 5.83 score 16 scriptsbioc
gDR:Umbrella package for R packages in the gDR suite
Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.
Maintained by Arkadiusz Gladki. Last updated 5 months ago.
1 stars 5.20 score 7 scripts
dgrun
RaceID:Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).
Maintained by Dominic Grün. Last updated 4 months ago.
4.74 score 110 scriptsbioc
FuseSOM:A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets
A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays.
Maintained by Elijah Willie. Last updated 5 months ago.
singlecellcellbasedassaysclusteringspatial
1 stars 4.71 score 17 scripts
xudonghan-bio
scapGNN:Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis
It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.
Maintained by Xudong Han. Last updated 2 years ago.
2.00 score 7 scripts
cran
PPLasso:Prognostic Predictive Lasso for Biomarker Selection
We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
Maintained by Wencan Zhu. Last updated 2 years ago.
2.00 score
clavie3009
WLogit:Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
Maintained by Wencan Zhu. Last updated 2 years ago.
2.00 score 3 scripts
johnnyzhz
networksem:Network Structural Equation Modeling
Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.
Maintained by Zhiyong Zhang. Last updated 9 days ago.
1.30 score