scater - Single-Cell Analysis Toolkit for Gene Expression Data in R

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

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immunooncologysinglecellrnaseqqualitycontrolpreprocessingnormalizationvisualizationdimensionreductiontranscriptomicsgeneexpressionsequencingsoftwaredataimportdatarepresentationinfrastructurecoverage

11.15 score 57 dependents 13k scripts 16k downloads

iheatmapr - Interactive, Complex Heatmaps

Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.

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heatmapplotlyinteractive-visualizationsdata-visualizationhtmlwidgetspeer-reviewed

9.50 score 270 stars 2 dependents 129 scripts 371 downloads

contrast - A Collection of Contrast Methods

One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.

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7.35 score 4 stars 1 dependents 185 scripts 551 downloads

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>.

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dimensionreductionvisualizationsoftwaresinglecellsequencing

5.26 score 3 stars 5 scripts 631 downloads

densvis - Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction

Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.

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dimensionreductionvisualizationsoftwaresinglecellsequencingcppopenmp

5.07 score 2 stars 22 scripts 1.3k downloads

BASiCStan - Stan implementation of BASiCS

Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries.

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immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecelldifferentialexpressionbayesiancellbiologysingle-cell-rna-seqcpp

3.78 score 1 scripts 309 downloads

bayefdr - Bayesian Estimation and Optimisation of Expected False Discovery Rate

Implements the Bayesian FDR control described by Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>. Allows optimisation and visualisation of expected error rates based on tail posterior probability tests. Based on code written by Catalina Vallejos for BASiCS, see Beyond comparisons of means: understanding changes in gene expression at the single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.

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2.70 score 1 scripts 261 downloads