tidyHeatmap - A Tidy Implementation of Heatmap
This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.
Last updated 5 days ago
brewercomplexheatmapcustom-palettedplyrgraphvizheatmapmtcarsplottingrstudioscaletibbletidytidy-data-frametidybulktidyverseviridis
296 stars 6.28 score 59 dependencies 1 dependentstidyseurat - 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.
Last updated 2 months ago
dplyrggplot2normalizationpcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
152 stars 5.08 score 147 dependencies 1 dependentssccomp - Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data
A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Last updated 4 months ago
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecellclustering
86 stars 4.07 score 109 dependencies
CuratedAtlasQueryR - Queries the Human Cell Atlas
Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.
Last updated 4 months ago
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
88 stars 3.91 score 171 dependencies
tidyomics - Easily install and load the tidyomics ecosystem
The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command.
Last updated 4 months ago
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
55 stars 3.26 score 197 dependenciestidySingleCellExperiment - Brings SingleCellExperiment to the Tidyverse
'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).
Last updated 4 months ago
assaydomaininfrastructurernaseqdifferentialexpressionsinglecellgeneexpressionnormalizationclusteringqualitycontrolsequencing
35 stars 3.14 score 91 dependencies 2 dependentstidygate - Interactively Gate Points
Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid (who is also an author of this package).
Last updated 2 months ago
clusteringdatavisdatavizdplyrdrawingfacsgateggplot2interactivepipeprogrammaticseuratsingle-cellsingle-cell-rna-seqtibbletidy-datatidyverse
20 stars 2.43 score 78 dependencies 1 dependents
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.
Last updated 4 months ago
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
2.18 score 99 dependencies 1 dependentsttservice - A Service for Tidy Transcriptomics Software Suite
It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by 'tidySingleCellExperiment' and 'tidyseurat'.
Last updated 3 months ago
2.11 score 70 dependencies 6 dependentstidySummarizedExperiment - Brings SummarizedExperiment to the Tidyverse
The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis.
Last updated 4 months ago
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
1.64 score 90 dependencies 1 dependentsppcseq - Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models
Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.
Last updated 4 months ago
rnaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
6 stars 1.38 score 83 dependencies