Showing 34 of total 34 results (show query)
ropensci
medrxivr:Access and Search MedRxiv and BioRxiv Preprint Data
An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv <https://www.medrxiv.org/> and bioRxiv <https://www.biorxiv.org/>, both of which are operated by the Cold Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 'Cold Spring Harbour Laboratory (CSHL)' API <https://api.biorxiv.org/>, allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. 'medrxivr' also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria.
Maintained by Yaoxiang Li. Last updated 1 months ago.
bibliographic-databasebiorxivevidence-synthesismedrxiv-datapeer-reviewedpreprint-recordssystematic-reviews
19.7 match 56 stars 7.17 score 44 scripts
bioc
YAPSA:Yet Another Package for Signature Analysis
This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.
Maintained by Zuguang Gu. Last updated 5 months ago.
sequencingdnaseqsomaticmutationvisualizationclusteringgenomicvariationstatisticalmethodbiologicalquestion
16.1 match 6.41 score 57 scripts
jsilve24
fido:Bayesian Multinomial Logistic Normal Regression
Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'.
Maintained by Justin Silverman. Last updated 18 days ago.
8.8 match 20 stars 8.31 score 103 scripts
stephenturner
biorecap:Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama
Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama.
Maintained by Stephen Turner. Last updated 6 months ago.
13.3 match 64 stars 4.20 score 5 scripts
satijalab
Seurat:Tools for Single Cell Genomics
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
Maintained by Paul Hoffman. Last updated 1 years ago.
human-cell-atlassingle-cell-genomicssingle-cell-rna-seqcpp
1.6 match 2.4k stars 16.86 score 50k scripts 73 dependents
bodkan
admixr:An Interface for Running 'ADMIXTOOLS' Analyses
An interface for performing all stages of 'ADMIXTOOLS' analyses (<https://reich.hms.harvard.edu/software>) entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run 'ADMIXTOOLS' programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the 'EIGENSTRAT' format is also provided.
Maintained by Martin Petr. Last updated 26 days ago.
bioinformaticspopgenpopulation-genetics
1.6 match 29 stars 7.42 score 91 scripts
dcnorris
DTAT:Dose Titration Algorithm Tuning
Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a '3+3/PC' dose-finding study. Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and Norris (2017c) <doi:10.1101/240846>.
Maintained by David C. Norris. Last updated 10 months ago.
3.3 match 2.90 score 20 scripts
uclouvain-cbio
scpdata:Single-Cell Proteomics Data Package
The package disseminates mass spectrometry (MS)-based single-cell proteomics (SCP) datasets. The data were collected from published work and formatted using the `scp` data structure. The data sets contain quantitative information at spectrum, peptide and/or protein level for single cells or minute sample amounts.
Maintained by Christophe Vanderaa. Last updated 10 days ago.
experimentdataexpressiondataexperimenthubreproducibleresearchmassspectrometrydataproteomesinglecelldatapackagetypedata
1.7 match 6 stars 5.58 score 16 scriptsbioc
sva:Surrogate Variable Analysis
The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).
Maintained by Jeffrey T. Leek. Last updated 5 months ago.
immunooncologymicroarraystatisticalmethodpreprocessingmultiplecomparisonsequencingrnaseqbatcheffectnormalization
0.5 match 10.05 score 3.2k scripts 50 dependents
plantandfoodresearch
hidecan:Create HIDECAN Plots for Visualising Genome-Wide Association Studies and Differential Expression Results
Generates HIDECAN plots that summarise and combine the results of genome-wide association studies (GWAS) and transcriptomics differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot is presented in: Angelin-Bonnet, O., Vignes, M., Biggs, P. J., Baldwin, S., & Thomson, S. (2023). Visual integration of GWAS and differential expression results with the hidecan R package. bioRxiv, 2023-03.
Maintained by Olivia Angelin-Bonnet. Last updated 6 months ago.
0.5 match 6 stars 5.70 score 14 scripts 1 dependents
uscbiostats
hJAM:Hierarchical Joint Analysis of Marginal Summary Statistics
Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.
Maintained by Lai Jiang. Last updated 1 years ago.
0.5 match 9 stars 5.13 score 5 scripts
msesia
SNPknock:Knockoffs for Hidden Markov Models and Genetic Data
Generate knockoffs for genetic data and hidden Markov models. For more information, see the website below and the accompanying papers: "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>). "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).
Maintained by Matteo Sesia. Last updated 5 years ago.
0.5 match 2 stars 4.76 score 29 scripts
amishra-stats
nbfar:Negative Binomial Factor Regression Models ('nbfar')
We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y. With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.
Maintained by Aditya Mishra. Last updated 3 years ago.
0.5 match 6 stars 4.48 score 6 scripts
greifflab
immuneSIM:Tunable Simulation of B- And T-Cell Receptor Repertoires
Simulate full B-cell and T-cell receptor repertoires using an in silico recombination process that includes a wide variety of tunable parameters to introduce noise and biases. Additional post-simulation modification functions allow the user to implant motifs or codon biases as well as remodeling sequence similarity architecture. The output repertoires contain records of all relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions. Preprint is available at bioRxiv (Weber et al., 2019 <doi:10.1101/759795>).
Maintained by Cédric R. Weber. Last updated 1 years ago.
0.5 match 37 stars 4.44 score 15 scriptsbioc
saseR:Scalable Aberrant Splicing and Expression Retrieval
saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}.
Maintained by Alexandre Segers. Last updated 5 months ago.
differentialexpressiondifferentialsplicingregressiongeneexpressionalternativesplicingrnaseqsequencingsoftware
0.5 match 1 stars 4.40 score 1 scriptsbioc
DepInfeR:Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling
DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.
Maintained by Junyan Lu. Last updated 5 months ago.
softwareregressionpharmacogeneticspharmacogenomicsfunctionalgenomics
0.5 match 1 stars 4.36 score 23 scriptsbioc
BloodGen3Module:This R package for performing module repertoire analyses and generating fingerprint representations
The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows.
Maintained by Darawan Rinchai. Last updated 5 months ago.
softwarevisualizationgeneexpression
0.5 match 4.30 score 5 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
0.5 match 3.70 score 10 scripts
qiangxyz
habCluster:Detecting Spatial Clustering Based on Connection Cost Between Grids
Based on landscape connectivity, spatial boundaries were identified using community detection algorithm at grid level. Methods using raster as input and the value of each cell of the raster is the "smoothness" to indicate how easy the cell connecting with neighbor cells. Details about the 'habCluster' package methods can be found in Zhang et al. <bioRxiv:2022.05.06.490926>.
Maintained by Qiang Dai. Last updated 3 years ago.
0.5 match 1 stars 3.70 score 5 scripts
ingmbioinfo
combiroc:Selection and Ranking of Omics Biomarkers Combinations Made Easy
Provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) <doi:10.1038/srep45477> and further described in Bombaci & Rossi (2019) <doi:10.1007/978-1-4939-9164-8_16>, and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. <doi:10.1101/2022.01.17.476603>.
Maintained by Riccardo L. Rossi. Last updated 9 months ago.
0.5 match 5 stars 3.48 score 12 scripts
cran
PEIMAN2:Post-Translational Modification Enrichment, Integration, and Matching Analysis
Functions and mined database from 'UniProt' focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2022) <bioRxiv:10.1101/2022.11.09.515610>.
Maintained by Payman Nickchi. Last updated 2 years ago.
0.5 match 2.70 score
mfjoneill
gwrpvr:Genome-Wide Regression P-Value (Gwrpv)
Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. Finite-sample genome-wide regression p-values (Gwrpv) with a non-normally distributed phenotype (Gregory Connor and Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).
Maintained by Michael ONeill. Last updated 3 years ago.
0.5 match 2.70 score 2 scripts
denizakdemir
CovCombR:Combine Partial Covariance / Relationship Matrices
Combine partial covariance matrices using a Wishart-EM algorithm. Methods are described in the November 2019 article by Akdemir et al. <https://www.biorxiv.org/content/10.1101/857425v1>. It can be used to combine partially overlapping covariance matrices from independent trials, partially overlapping multi-view relationship data from genomic experiments, partially overlapping Gaussian graphs described by their covariance structures. High dimensional covariance estimation, multi-view data integration. high dimensional covariance graph estimation.
Maintained by Deniz Akdemir. Last updated 5 years ago.
0.5 match 2.70 score 5 scripts
pboutros
SeqKat:Detection of Kataegis
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
Maintained by Paul C. Boutros. Last updated 5 years ago.
0.5 match 2.11 score 13 scriptscran
valection:Sampler for Verification Studies
A binding for the 'valection' program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The 'valection' C library is required to use this package and can be downloaded from: <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
Maintained by Paul C. Boutros. Last updated 7 years ago.
0.5 match 2.00 score
hamed-ebi
SmoothWin:Soft Windowing on Linear Regression
The main function in the package utilizes a windowing function in the form of an exponential weighting function to linear models. The bandwidth and sharpness of the window are controlled by two parameters. Then, a series of tests are used to identify the right parameters of the window (see Hamed Haselimashhadi et al (2019) <https://www.biorxiv.org/content/10.1101/656678v1>).
Maintained by Hamed Haselimashhadi. Last updated 6 years ago.
0.5 match 1.48 score 4 scripts 1 dependents
altayg
Ac3net:Inferring Directional Conservative Causal Core Gene Networks
Infers directional conservative causal core (gene) networks. It is an advanced version of the algorithm C3NET by providing directional network. Gokmen Altay (2018) <doi:10.1101/271031>, bioRxiv.
Maintained by Gokmen Altay. Last updated 7 years ago.
0.5 match 1.08 score 12 scriptscran
JUMP:Replicability Analysis of High-Throughput Experiments
Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <https://www.biorxiv.org/content/10.1101/2023.02.13.528417v2>].
Maintained by Yan Li. Last updated 2 years ago.
0.5 match 1.00 scorecran
STAREG:An Empirical Bayes Approach for Replicability Analysis Across Two Studies
A robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2023), <https://www.biorxiv.org/content/10.1101/2023.05.30.542607v1>].
Maintained by Yan Li. Last updated 2 years ago.
0.5 match 1.00 scorecran
gomp:The gamma-OMP Feature Selection Algorithm
The gamma-Orthogonal Matching Pursuit (gamma-OMP) is a recently suggested modification of the OMP feature selection algorithm for a wide range of response variables. The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. <doi:10.1101/431734>. Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. <doi:10.1109/TCBB.2020.3029952>.
Maintained by Michail Tsagris. Last updated 2 months ago.
0.5 match 1.00 score 6 scripts