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ggseqlogo - A 'ggplot2' Extension for Drawing Publication-Ready Sequence Logos

The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.

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11.71 score 231 stars 12 dependents 1.3k scripts 4.8k downloads

margins - Marginal Effects for Model Objects

An R port of Stata's 'margins' command, which can be used to calculate marginal (or partial) effects from model objects.

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glmlinear-modelsmarginal-effectspartial-effectsregressionstata-command

11.44 score 265 stars 7 dependents 1.3k scripts 12k downloads

RcppRoll - Efficient Rolling / Windowed Operations

Provides fast and efficient routines for common rolling / windowed operations. Routines for the efficient computation of windowed mean, median, sum, product, minimum, maximum, standard deviation and variance are provided.

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cppopenmp

11.23 score 83 stars 72 dependents 1.5k scripts 32k downloads

dotwhisker - Dot-and-Whisker Plots of Regression Results

Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the 'parameters' package). It returns 'ggplot' objects that can be further customized using tools from the 'ggplot2' package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.

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graphicsplotregression-models

10.56 score 59 stars 2 dependents 858 scripts 6.6k downloads

SeuratWrappers - Community-Provided Methods and Extensions for the Seurat Object

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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communitysingle-cell-analysissingle-cell-genomics

8.89 score 372 stars 4.1k scripts

rsparse - Statistical Learning on Sparse Matrices

Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <doi:10.48550/arXiv.1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://aclanthology.org/D14-1162/>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.

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collaborative-filteringfactorization-machinesmatrix-completionmatrix-factorizationrecommender-systemsparse-matricessvdopenblascppopenmp

8.89 score 180 stars 26 dependents 55 scripts 4.0k downloads

CellChat - Inference and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics data

an open source R tool that infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq and spatially resolved transcriptomics data.

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cell-cell-communicationcell-cell-interactionmicroenvironmentsingle-cell-analysisspatial-transcriptomicscpp

8.36 score 676 stars 3.4k scripts

SeuratDisk - Interfaces for HDF5-Based Single Cell File Formats

The h5Seurat file format is specifically designed for the storage and analysis of multi-modal single-cell and spatially-resolved expression experiments, for example, from CITE-seq or 10X Visium technologies. It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Scanpy.

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hdf5-formatsingle-cell-genomicssingle-cell-rna-seq

7.78 score 177 stars 1 dependents 5.6k scripts

ArchR - Analyzing single-cell regulatory chromatin in R.

This package is designed to streamline scATAC analyses in R.

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cpp

7.72 score 465 stars 4.5k scripts

SeuratData - Install and Manage Seurat Datasets

Single cell RNA sequencing datasets can be large, consisting of matrices that contain expression data for several thousand features across several thousand cells. This package is designed to easily install, manage, and learn about various single-cell datasets, provided Seurat objects and distributed as independent packages.

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datasetssingle-cellsingle-cell-genomics

7.47 score 160 stars 4.1k scripts

speedglm - Fitting Linear and Generalized Linear Models to Large Data Sets

Fitting linear models and generalized linear models to large data sets by updating algorithms.

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7.15 score 7 stars 11 dependents 556 scripts 11k downloads

sceasy - A package to help convert different single-cell data formats to each other

Convert Seurat, SingleCellExperiment, Loom object to AnnData object. Convert SingleCellExperiment object to and from Loom object.

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7.10 score 470 stars 884 scripts

penalized - L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model

Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.

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openblascpp

6.96 score 4 stars 17 dependents 297 scripts 3.0k downloads

RcisTarget - RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions

RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).

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generegulationmotifannotationtranscriptomicstranscriptiongenesetenrichmentgenetarget

6.92 score 50 stars 278 scripts

Azimuth - A Shiny App Demonstrating a Query-Reference Mapping Algorithm for Single-Cell Data

Azimuth uses an annotated reference dataset to automate the processing, analysis, and interpretation of a new single-cell RNA-seq or ATAC-seq experiment. Azimuth leverages a 'reference-based mapping' pipeline that inputs a counts matrix and performs normalization, visualization, cell annotation, and differential expression (biomarker discovery).

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shiny-appsingle-cell-genomicssingle-cell-rna-seqcpp

6.87 score 151 stars 886 scripts

loupeR - Converts Seurat objects to 10x Genomics Loupe files

Converts Seurat objects to 10x Genomics Loupe files. This is a second line to make the package checker not complain.

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10xgenomicssingle-cell-genomics

6.47 score 161 stars 1 dependents 152 scripts

DDRTree - Learning Principal Graphs with DDRTree

Project data into a reduced dimensional space and construct a principal graph from the reduced dimension.

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cpp

6.11 score 8 stars 3 dependents 51 scripts 3.5k downloads

DoubletFinder - DoubletFinder is a suite of tools for identifying doublets in single-cell RNA sequencing data.

DoubletFinder identifies doublets by generating artificial doublets from existing scRNA-seq data and defining which real cells preferentially co-localize with artificial doublets in gene expression space. Other DoubletFinder package functions are used for fitting DoubletFinder to different scRNA-seq datasets. For example, ideal DoubletFinder performance in real-world contexts requires (I) Optimal pK selection and (2) Homotypic doublet proportion estimation. pK selection is achieved using pN-pK parameter sweeps and maxima identification in mean-variance-normalized bimodality coefficient distributions. Homotypic doublet proportion estimation is achieved by finding the sum of squared cell annotation frequencies. For more information, see our Cell Sysmtes paper https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30073-0 and our github https://github.com/chris-mcginnis-ucsf/DoubletFinder

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5.36 score 1 dependents 3.0k scripts

tidyproteomics - An S3 data object and framework for common quantitative proteomic analyses

Creates a simple, universal S3 data structure for the post analysis of mass spectrometry based quantitative proteomic data. In addition, this package collects, adapts and organizes several useful algorithms and methods used in typical post analysis workflows.

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5.11 score 47 stars 136 scripts

rdoc - Colourised R Documentation

Extends tools::Rd2txt() by adding customisable text and colour formatting to R documentation contents. If used from a terminal, output will be displayed via file.show() otherwise contents will be printed in sections. Also provides stand-in replacements for ?() and help().

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clicolorizationcrayondocumentationpretty-print

4.16 score 48 stars 60 scripts 23 downloads

DAseq - Detecting regions of differential abundance between scRNA-seq datasets

DA-seq is a multiscale approach for detecting DA subpopulations. In contrast to clustering based approaches, our method can detect DA subpopulations that do not form well separated clusters. For each cell, we compute a multiscale differential abundance score measure. These scores are based on the k nearest neighbors in the transcriptome space for various values of k.

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4.13 score 44 stars 61 scripts

GOsummaries - Word cloud summaries of GO enrichment analysis

A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data.

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geneexpressionclusteringgovisualizationcpp

3.88 score 11 stars 46 scripts 10 downloads

datascience.curriculum - Blaser Lab Data Science Online Course

What the package does (one paragraph).

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3.86 score 1 stars 12 scripts

SummarizedHeatmap - Heatmaps for SummarizedExperiment Objects

Represents a heatmap as a SummarizedExperiment-derived object, holding a single assay together with its row and column annotations and any hierarchical clustering. Assay values, annotations, display order, and dendrograms stay synchronized as the object is subsetted or reclustered, so a heatmap's components cannot drift out of step with each other. Each visual component (the main heatmap, annotation strips, dendrograms) is returned as an ordinary ggplot2 plot, and these can be composed, laid out, and customized using patchwork.

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softwareclusteringvisualization

3.18 score 3 scripts

RforMassSpectrometry - R for MassSpectrometry meta-package

The RforMassSpectrometry meta-package loads and manages the core packages of the R for Mass Spectrometry initiative, that provide efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry assays.

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4.72 score 21 stars 1 scripts

blaseRtemplates - Generates a structured R project with custom templates.

This package generates an R project with customized templates. It also packages bash scripts for blaserlab collaborative git workflow.

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2.65 score 1 dependents 5 scripts

mhurdle - Multiple Hurdle Tobit Models

Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.

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glibc

4.37 score 1 stars 16 scripts 2.9k downloads

muRtools - Mueller's R tools

Fabian's custom plotting functions and utilities

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1.70 score 1 stars 2 scripts

TxDb.Drerio.UCSC.danRer11.ensGene - Annotation package for TxDb object(s)

Exposes an annotation databases generated from UCSC by exposing these as TxDb objects

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annotationdatageneticstxdbdanio_rerio

1.70 score

muLogR - muLogR

Logging and code structuring for R. Based on code from the RnBeads package.

Last updated

1.48 score 30 scripts