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
assaydomaininfrastructurebrewercomplexheatmapcustom-palettedplyrgraphvizheatmapmtcarsplottingrstudioscaletibbletidytidy-data-frametidybulktidyverseviridis
10.44 score 361 stars 1 dependents 321 scripts 1.1k downloadstidybulk - 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
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbioconductorbulk-transcriptional-analysesdeseq2differential-expressionedgerensembl-idsentrezgene-symbolsgseamds-dimensionspcapiperedundancytibbletidytidy-datatidyversetranscriptstsne
10.31 score 180 stars 1 dependents 208 scripts 621 downloadstidyseurat - 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
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
10.16 score 166 stars 1 dependents 542 scripts 569 downloadstidySingleCellExperiment - 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
assaydomaininfrastructurernaseqdifferentialexpressionsinglecellgeneexpressionnormalizationclusteringqualitycontrolsequencingbioconductordplyrggplot2plotlysingle-cell-rna-seqsingle-cell-sequencingsinglecellexperimenttibbletidyrtidyverse
8.96 score 37 stars 2 dependents 185 scripts 678 downloadstidySummarizedExperiment - 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
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbioconductorgenomicssummarizedexperimenttidyverse
8.81 score 30 stars 1 dependents 240 scripts 601 downloads
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
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicscytometrygenomicstidyverse
7.00 score 75 stars 22 scripts 260 downloadstidygate - 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
assaydomaininfrastructureclusteringdatavisdatavizdplyrdrawingfacsgateggplot2interactivepipeprogrammaticseuratsingle-cellsingle-cell-rna-seqtibbletidy-datatidyverse
6.73 score 25 stars 2 dependents 18 scripts 323 downloadsppcseq - 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
rnaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbayesian-inferencedeseq2edgernegative-binomialoutlierstancpp
5.71 score 8 stars 16 scripts 339 downloads