R-universe search: tsne

jdonaldson

tsne:T-Distributed Stochastic Neighbor Embedding for R (t-SNE)

A "pure R" implementation of the t-SNE algorithm.

Maintained by Justin Donaldson. Last updated 6 years ago.

60.2 match 58 stars 9.35 score 656 scripts 13 dependents

stemangiola

tidyseurat:Brings Seurat to the Tidyverse

It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.

Maintained by Stefano Mangiola. Last updated 8 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics dplyr ggplot2 pca purrr sct seurat single-cell single-cell-rna-seq tibble tidyr tidyverse transcripts tsne umap

11.0 match 158 stars 9.66 score 398 scripts 1 dependents

bioc

tidybulk:Brings transcriptomics to the tidyverse

This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics bioconductor bulk-transcriptional-analyses deseq2 differential-expression edger ensembl-ids entrez gene-symbols gsea mds-dimensions pca pipe redundancy tibble tidy tidy-data tidyverse transcripts tsne

11.0 match 168 stars 9.48 score 172 scripts 1 dependents

bioc

clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters

Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.

Maintained by Rui Fu. Last updated 5 months ago.

singlecell annotation sequencing microarray geneexpression assign-identities clusters marker-genes rna-seq single-cell-rna-seq

8.8 match 119 stars 9.63 score 296 scripts

jayanilakshika

cardinalR:Collection of Data Structures

A collection of simple simulation datasets designed for generating Nonlinear Dimension Reduction representations techniques such as t-distributed Stochastic Neighbor Embedding, and Uniform Manifold Approximation and Projection. These datasets serve as a valuable resource for understanding the reliability of Nonlinear Dimension Reduction representations in various contexts.

Maintained by Jayani P.G. Lakshika. Last updated 11 days ago.

16.0 match 4.54 score

gfellerlab

SuperCell:Simplification of scRNA-seq data by merging together similar cells

Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.

Maintained by The package maintainer. Last updated 8 months ago.

software coarse-graining scrna-seq-analysis scrna-seq-data

8.9 match 72 stars 8.08 score 93 scripts

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 23 days ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

6.9 match 181 stars 10.16 score 252 scripts

rezakj

iCellR:Analyzing High-Throughput Single Cell Sequencing Data

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

Maintained by Alireza Khodadadi-Jamayran. Last updated 8 months ago.

10xgenomics 3d batch-normalization cell-type-classification cite-seq clustering clustering-algorithm diffusion-maps dropout icellr imputation intractive-graph normalization pseudotime scrna-seq scvdj-seq singel-cell-sequencing umap cpp

9.2 match 121 stars 5.56 score 7 scripts 1 dependents

psychbruce

PsychWordVec:Word Embedding Research Framework for Psychological Science

An integrative toolbox of word embedding research that provides: (1) a collection of 'pre-trained' static word vectors in the '.RData' compressed format <https://psychbruce.github.io/WordVector_RData.pdf>; (2) a series of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test <doi:10.1126/science.aal4230> and the Relative Norm Distance <doi:10.1073/pnas.1720347115>, with permutation test of significance; (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' <arXiv:1301.3781>, 'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <arXiv:1607.04606>; (5) a group of functions to download 'pre-trained' language models (e.g., 'GPT', 'BERT') and extract contextualized (dynamic) word vectors (based on the R package 'text').

Maintained by Han-Wu-Shuang Bao. Last updated 1 years ago.

11.0 match 22 stars 4.04 score 10 scripts

bioc

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.

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

7.1 match 14 stars 5.83 score 16 scripts

bioc

structToolbox:Data processing & analysis tools for Metabolomics and other omics

An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.

Maintained by Gavin Rhys Lloyd. Last updated 25 days ago.

workflowstep metabolomics bioconductor-package dims lc-ms machine-learning multivariate-analysis statistics univariate

6.6 match 10 stars 6.26 score 12 scripts

bioc

scGPS:A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

Maintained by Quan Nguyen. Last updated 5 months ago.

singlecell clustering dataimport sequencing coverage openblas cpp

7.7 match 4 stars 5.20 score 7 scripts

ocbe-uio

DIscBIO:A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.

Maintained by Waldir Leoncio. Last updated 1 years ago.

biomarker-discovery jupyter-notebook scrna-seq single-cell-analysis transcriptomics openjdk

8.3 match 12 stars 4.38 score 5 scripts

bioc

M3C:Monte Carlo Reference-based Consensus Clustering

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

Maintained by Christopher John. Last updated 5 months ago.

clustering geneexpression transcription rnaseq sequencing immunooncology

5.2 match 6.59 score 174 scripts 1 dependents

bioc

Sconify:A toolkit for performing KNN-based statistics for flow and mass cytometry data

This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

Maintained by Tyler J Burns. Last updated 5 months ago.

immunooncology singlecell flowcytometry software multiplecomparison visualization

6.1 match 4.74 score 11 scripts

alexisvdb

singleCellHaystack:A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

Maintained by Alexis Vandenbon. Last updated 1 years ago.

bioinformatics cite-seq pseudotime scatac-seq single-cell spatial-proteomics spatial-transcriptomics transcriptomics

3.8 match 81 stars 6.71 score 64 scripts

kisungyou

Rdimtools:Dimension Reduction and Estimation Methods

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

Maintained by Kisung You. Last updated 2 years ago.

dimension-estimation dimension-reduction manifold-learning subspace-learning openblas cpp openmp

3.0 match 52 stars 8.37 score 186 scripts 8 dependents

adrientaudiere

MiscMetabar:Miscellaneous Functions for Metabarcoding Analysis

Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' R packages. It helps to build reproducible and robust bioinformatics pipelines in R. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.

Maintained by Adrien Taudière. Last updated 25 days ago.

sequencing microbiome metagenomics clustering classification visualization amplicon amplicon-sequencing biodiversity-informatics ecology illumina metabarcoding ngs-analysis

3.5 match 17 stars 6.44 score 23 scripts

bioc

CATALYST:Cytometry dATa anALYSis Tools

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

Maintained by Helena L. Crowell. Last updated 4 months ago.

clustering dataimport differentialexpression experimentaldesign flowcytometry immunooncology massspectrometry normalization preprocessing singlecell software statisticalmethod visualization

1.9 match 67 stars 11.06 score 362 scripts 2 dependents

dvrbts

labdsv:Ordination and Multivariate Analysis for Ecology

A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses.

Maintained by David W. Roberts. Last updated 2 years ago.

fortran

3.3 match 3 stars 6.08 score 452 scripts 13 dependents

bioc

MatrixQCvis:Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Maintained by Thomas Naake. Last updated 5 months ago.

visualization shinyapps gui qualitycontrol dimensionreduction metabolomics proteomics transcriptomics

3.9 match 4.74 score 4 scripts

immunogenomics

harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data

Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.

Maintained by Ilya Korsunsky. Last updated 4 months ago.

algorithm data-integration scrna-seq openblas cpp

1.3 match 554 stars 13.74 score 5.5k scripts 8 dependents

kisungyou

maotai:Tools for Matrix Algebra, Optimization and Inference

Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

Maintained by Kisung You. Last updated 4 days ago.

openblas cpp openmp

3.3 match 8 stars 5.51 score 15 scripts 9 dependents

mthrun

ProjectionBasedClustering:Projection Based Clustering

A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.

Maintained by Michael Thrun. Last updated 3 years ago.

cpp

3.3 match 7 stars 5.33 score 34 scripts 3 dependents

immunomind

immunarch:Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.

Maintained by Vadim I. Nazarov. Last updated 12 months ago.

airr-analysis b-cell-receptor bcr bcr-repertoire bioinformatics ig ig-repertoire immune-repertoire immune-repertoire-analysis immune-repertoire-data immunoglobulin immunoinformatics immunology rep-seq repertoire-analysis single-cell single-cell-analysis t-cell-receptor tcr tcr-repertoire cpp

1.7 match 315 stars 9.49 score 203 scripts

tkcaccia

KODAMA:Knowledge Discovery by Accuracy Maximization

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Maintained by Stefano Cacciatore. Last updated 3 months ago.

openblas cpp

2.3 match 1 stars 6.98 score 63 scripts 1 dependents

bioc

Linnorm:Linear model and normality based normalization and transformation method (Linnorm)

Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.

Maintained by Shun Hang Yip. Last updated 5 months ago.

immunooncology sequencing chipseq rnaseq differentialexpression geneexpression genetics normalization software transcription batcheffect peakdetection clustering network singlecell cpp

2.3 match 6.26 score 61 scripts 5 dependents

dynverse

dyndimred:Dimensionality Reduction Methods in a Common Format

Provides a common interface for applying dimensionality reduction methods, such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), and Uniform Manifold Approximation and Projection ('UMAP'). Has built-in support for sparse matrices.

Maintained by Robrecht Cannoodt. Last updated 4 years ago.

3.3 match 7 stars 4.21 score 23 scripts

bioc

DepecheR:Determination of essential phenotypic elements of clusters in high-dimensional entities

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.

Maintained by Jakob Theorell. Last updated 5 months ago.

software cellbasedassays transcription differentialexpression datarepresentation immunooncology transcriptomics classification clustering dimensionreduction featureextraction flowcytometry rnaseq singlecell visualization cpp

2.7 match 5.18 score 15 scripts

fatore

mp:Multidimensional Projection Techniques

Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets.

Maintained by Francisco M. Fatore. Last updated 7 years ago.

openblas cpp

3.3 match 5 stars 4.15 score 14 scripts

bioc

iasva:Iteratively Adjusted Surrogate Variable Analysis

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.

Maintained by Donghyung Lee. Last updated 5 months ago.

preprocessing qualitycontrol batcheffect rnaseq software statisticalmethod featureextraction immunooncology

2.9 match 4.65 score 45 scripts

bioc

dittoSeq:User Friendly Single-Cell and Bulk RNA Sequencing Visualization

A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().

Maintained by Daniel Bunis. Last updated 5 months ago.

software visualization rnaseq singlecell geneexpression transcriptomics dataimport

1.7 match 7.56 score 760 scripts 2 dependents

kharchenkolab

pagoda2: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.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

1.6 match 222 stars 8.00 score 282 scripts

bioc

scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Maintained by Hechen Li. Last updated 5 months ago.

singlecell transcriptomics geneexpression sequencing experimentaldesign

1.8 match 23 stars 7.15 score 11 scripts

exaexa

EmbedSOM:Fast Embedding Guided by Self-Organizing Map

Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019) <doi:10.12688/f1000research.21642.1>.

Maintained by Mirek Kratochvil. Last updated 1 months ago.

cytometry som visualization cpp

1.8 match 26 stars 6.02 score 8 scripts

md-anderson-bioinformatics

NGCHM:Next Generation Clustered Heat Maps

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. 'NGCHM' allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

Maintained by Mary A Rohrdanz. Last updated 8 days ago.

heatmap nci-itcr ng-chm

1.8 match 9 stars 5.48 score 28 scripts

bioc

CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

Maintained by Akira Cortal. Last updated 5 months ago.

rnaseq singlecell dimensionreduction clustering genesetenrichment geneexpression atacseq openblas cpp openmp

2.0 match 4.85 score 70 scripts

bioc

tomoda:Tomo-seq data analysis

This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.

Maintained by Wendao Liu. Last updated 5 months ago.

geneexpression sequencing rnaseq transcriptomics spatial clustering visualization

2.3 match 4.00 score 2 scripts

bioc

lemur:Latent Embedding Multivariate Regression

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

transcriptomics differentialexpression singlecell dimensionreduction regression openblas cpp

1.1 match 87 stars 7.80 score 81 scripts

kisungyou

Riemann:Learning with Data on Riemannian Manifolds

We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.

Maintained by Kisung You. Last updated 2 years ago.

openblas cpp openmp

2.3 match 10 stars 3.70 score 8 scripts

marcohlmann

metanetwork:Handling and Representing Trophic Networks in Space and Time

A toolbox to handle and represent trophic networks in space or time across aggregation levels. This package contains a layout algorithm specifically designed for trophic networks, using dimension reduction on a diffusion graph kernel and trophic levels. Importantly, this package provides a layout method applicable for large trophic networks. The package also implements network diversity indices at different aggregation levels and connectance computation.

Maintained by Marc Ohlmann. Last updated 2 years ago.

1.8 match 2 stars 3.89 score 77 scripts

bioc

esetVis:Visualizations of expressionSet Bioconductor object

Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available.

Maintained by Laure Cougnaud. Last updated 5 months ago.

visualization datarepresentation dimensionreduction principalcomponent pathways

2.0 match 3.30 score 6 scripts

cran

CytobankAPI:Cytobank API Wrapper for R

Tools to interface with Cytobank's API via R, organized by endpoints that represent various areas of Cytobank functionality. Learn more about Cytobank at <https://www.beckman.com/flow-cytometry/software>.

Maintained by Stu Blair. Last updated 2 years ago.

2.0 match 3.00 score

igordot

scooter:Streamlined scRNA-Seq Analysis Pipeline

Streamlined scRNA-Seq analysis pipeline.

Maintained by Igor Dolgalev. Last updated 1 years ago.

2.3 match 4 stars 2.51 score 16 scripts

blansche

fdm2id:Data Mining and R Programming for Beginners

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Maintained by Alexandre Blansché. Last updated 2 years ago.

3.3 match 1 stars 1.62 score 42 scripts

bioc

snifter:R wrapper for the python openTSNE library

Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) <doi:10.1101/731877> and the algorithm described by Linderman et al. (2018) <doi:10.1038/s41592-018-0308-4>.

Maintained by Alan OCallaghan. Last updated 5 months ago.

dimensionreduction visualization software singlecell sequencing

0.5 match 3 stars 4.95 score 3 scripts