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r-dabestr
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public |
Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.
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2025-03-25 |
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r-seuratwrappers
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public |
SeuratWrappers is a collection of community-provided methods and extensions for Seurat, curated by the Satija Lab at NYGC. These methods comprise functionality not presently found in Seurat, and are able to be updated much more frequently.
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2025-03-25 |
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r-trendsceek
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public |
Identify genes with spatial expression trends in single-cell gene expression data using marked point processes.
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2025-03-25 |
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r-conos
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public |
Wire together large collections of single-cell RNA-seq datasets. The package focuses on uniform mapping of homologous cell types across heterogeneous sample collections. For instance, a collection of dozens of peripheral blood samples from cancer patients, combined with dozens of controls. And perhaps also including samples of a related tissue, such as lymph nodes.
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2025-03-25 |
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r-busparse
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public |
The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices.
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2025-03-25 |
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r-schex
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public |
Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment and SeuratObject. The ideas used in this package are based on the excellence work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.
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2025-03-25 |
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r-singlecellexperiment
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public |
Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.
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2025-03-25 |
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r-s4vectors
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public |
The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).
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2025-03-25 |
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r-biocgenerics
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public |
The package defines S4 generic functions used in Bioconductor.
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2025-03-25 |
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r-harmony
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public |
Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi.org/10.1101/461954>. Package includes a standalone Harmony function and interfaces to external frameworks.
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2025-03-25 |
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r-singler
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public |
A computational method for unbiased cell type recognition of scRNA-seq. SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently. SingleR’s annotations combined with Seurat, a processing and analysis package designed for scRNA-seq, provide a powerful tool for the investigation of scRNA-seq data
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2025-03-25 |
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r-velocyto.r
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public |
Provides basic routines for estimation of gene-specific transcriptional derivatives and visualization of the resulting velocity patterns.
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2025-03-25 |
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r-pagoda2
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public |
Single-cell RNA-seq Analysis
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2025-03-25 |
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r-pheatmap
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public |
Implementation of heatmaps that offers more control over dimensions and appearance.
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2025-03-25 |
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r-outliers
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public |
A collection of some tests commonly used for identifying outliers.
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2025-03-25 |
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r-dofuture
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public |
Provides a '%dopar%' adaptor such that any type of futures can be used as backends for the 'foreach' framework.
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2025-03-25 |
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r-future
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public |
The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.
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2025-03-25 |
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r-pbmcapply
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public |
A light-weight package helps you track and visualize the progress of parallel version of vectorized R functions (mc*apply). Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to the lack of fork() functionality, which is essential for mc*apply, on Windows.
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2025-03-25 |
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r-liger
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public |
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors
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2025-03-25 |
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r-dosnow
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public |
Provides a parallel backend for the %dopar% function using the snow package of Tierney, Rossini, Li, and Sevcikova.
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2025-03-25 |
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r-snow
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public |
Support for simple parallel computing in R.
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2025-03-25 |
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r-riverplot
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public |
Sankey plots are a type of diagram that is convenient to illustrate how flow of information, resources etc. separates and joins, much like observing how rivers split and merge. For example, they can be used to compare different clusterings.
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2025-03-25 |
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r-mclust
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public |
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
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2025-03-25 |
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r-irlba
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public |
Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices.
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2025-03-25 |
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r-ica
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public |
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
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2025-03-25 |
