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.

Last updated 4 months ago

assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap

9.91 score 157 stars 1 packages 310 scripts 541 downloads

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 24 days ago

assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbioconductorbulk-transcriptional-analysesdeseq2differential-expressionedgerensembl-idsentrezgene-symbolsgseamds-dimensionspcapiperedundancytibbletidytidy-datatidyversetranscriptstsne

9.53 score 165 stars 1 packages 163 scripts 546 downloads

tidygate - 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

assaydomaininfrastructureclusteringdatavisdatavizdplyrdrawingfacsgateggplot2interactivepipeprogrammaticseuratsingle-cellsingle-cell-rna-seqtibbletidy-datatidyverse

6.87 score 22 stars 1 packages 14 scripts 286 downloads

ppcseq - 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 24 days ago

rnaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbayesian-inferencedeseq2edgernegative-binomialoutlierstan

5.65 score 7 stars 16 scripts 175 downloads