Other packages > Find by keyword >

singleCellHaystack  

A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
View on CRAN: Click here


Download and install singleCellHaystack package within the R console
Install from CRAN:
install.packages("singleCellHaystack")

Install from Github:
library("remotes")
install_github("cran/singleCellHaystack")

Install by package version:
library("remotes")
install_version("singleCellHaystack", "1.0.3")



Attach the package and use:
library("singleCellHaystack")
Maintained by
Alexis Vandenbon
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-07-01
Latest Update: 2024-01-11
Description:
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>.
How to cite:
Alexis Vandenbon (2020). singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data. R package version 1.0.3, https://cran.r-project.org/web/packages/singleCellHaystack. Accessed 17 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:04), 0.3.2 (2020-07-01 13:10), 0.3.3 (2020-09-18 16:40), 0.3.4 (2021-03-28 03:50), 1.0.0 (2022-12-20 11:00), 1.0.2 (2024-01-11 11:00)
Other packages that cited singleCellHaystack R package
View singleCellHaystack citation profile
Other R packages that singleCellHaystack depends, imports, suggests or enhances
Complete documentation for singleCellHaystack
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

VEwaningVariant  
Vaccine Efficacy Over Time - Variant Aware
Implements methods for inference on potential waning of vaccine efficacy and for estimation of vacci ...
Download / Learn more Package Citations See dependency  
alabama  
Constrained Nonlinear Optimization
Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear object ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
rt.test  
Robustified t-Test
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/ ...
Download / Learn more Package Citations See dependency  
FastKNN  
Fast k-Nearest Neighbors
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast wa ...
Download / Learn more Package Citations See dependency  
VFS  
Vegetated Filter Strip and Erosion Model
Empirical models for runoff, erosion, and phosphorus loss across a vegetated filter strip, given sl ...
Download / Learn more Package Citations See dependency  

27,806

R Packages

239,283

Dependencies

74,019

Author Associations

27,807

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA