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singleCellHaystack
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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", "1.0.2")
Attach the package and use:
library("singleCellHaystack")
Maintained by
Alexis Vandenbon
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[Scholar Profile | Author Map]
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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 1.0.2, https://cran.r-project.org/web/packages/singleCellHaystack
Previous versions and publish date:
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Functions, R codes and Examples using
the singleCellHaystack R package
Some associated functions: dat.expression . dat.tsne . default_bandwidth.nrd . extract_row_dgRMatrix . extract_row_lgRMatrix . get_D_KL . get_D_KL_continuous_highD . get_D_KL_highD . get_density . get_dist_two_sets . get_euclidean_distance . get_grid_points . get_log_p_D_KL . get_log_p_D_KL_continuous . get_parameters_haystack . get_reference . haystack . haystack_2D . haystack_continuous_highD . haystack_highD . hclust_haystack . hclust_haystack_highD . hclust_haystack_raw . kde2d_faster . kmeans_haystack . kmeans_haystack_highD . kmeans_haystack_raw . plot_compare_ranks . plot_gene_haystack . plot_gene_haystack_raw . plot_gene_set_haystack . plot_gene_set_haystack_raw . plot_rand_KLD . plot_rand_fit . read_haystack . show_result_haystack . singleCellHaystack-package . write_haystack .
Some associated R codes: data.R . debug.R . haystack.R . haystack_IO.R . haystack_clustering.R . haystack_clustering_highD.R . haystack_continuous.R . haystack_highD.R . haystack_visualization.R . randomization.R . s3.R . singleCellHaystack-package.R . sparse.R . Full singleCellHaystack package functions and examples
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