ggrastr - Rasterize Layers for 'ggplot2'
Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
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13.89 score 236 stars 74 dependents 3.0k scripts 25k downloadspagoda2 - Single Cell Analysis and Differential Expression
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
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scrna-seqsingle-cellsingle-cell-rna-seqtranscriptomicsopenblascppopenmp
9.60 score 253 stars 2 dependents 334 scripts 1.2k downloadsscde - Single Cell Differential Expression
The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
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immunooncologyrnaseqstatisticalmethoddifferentialexpressionbayesiantranscriptionsoftwareanalysisbioinformaticsheterogenityngssingle-celltranscriptomicsopenblascppopenmp
7.64 score 180 stars 174 scripts 858 downloadsconos - Clustering on Network of Samples
Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.
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batch-correctionscrna-seqsingle-cell-rna-seqopenblascppopenmp
7.64 score 225 stars 276 scripts 862 downloadssccore - Core Utilities for Single-Cell RNA-Seq
Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
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cppopenmp
7.44 score 13 stars 11 dependents 47 scripts 4.6k downloadsRMTstat - Distributions, Statistics and Tests Derived from Random Matrix Theory
Functions for working with the Tracy-Widom laws and other distributions related to the eigenvalues of large Wishart matrices. The tables for computing the Tracy-Widom densities and distribution functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.
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5.84 score 6 stars 13 dependents 38 scripts 1.6k downloadsRook - HTTP Web Server for R
An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.
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5.72 score 1 stars 4 dependents 117 scripts 1.2k downloadsN2R - Fast and Scalable Approximate k-Nearest Neighbor Search Methods using 'N2' Library
Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <doi:10.48550/arXiv.1603.09320>.
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cpp
5.56 score 10 stars 3 dependents 3 scripts 873 downloadsgapmap - 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.
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4.64 score 2 stars 22 scripts 592 downloads