{ "_id": "6a1ea6a9b25058d4daad8b97", "Package": "singleCellHaystack", "Type": "Package", "Title": "A Universal Differential Expression Prediction Tool for\nSingle-Cell and Spatial Genomics Data", "Version": "1.0.3", "Authors@R": "c(\nperson(\"Alexis\", \"Vandenbon\", email = \"alexis.vandenbon@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2180-5732\")),\nperson(\"Diego\", \"Diez\", email = \"diego10ruiz@gmail.com\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-2325-4893\"))\n)", "Description": "One key exploratory analysis step in single-cell genomics\ndata analysis is the prediction of features with different\nactivity levels. For example, we want to predict differentially\nexpressed genes (DEGs) in single-cell RNA-seq data, spatial\nDEGs in spatial transcriptomics data, or differentially\naccessible regions (DARs) in single-cell ATAC-seq data.\n'singleCellHaystack' predicts differentially active features in\nsingle cell omics datasets without relying on the clustering of\ncells into arbitrary clusters. 'singleCellHaystack' uses\nKullback-Leibler divergence to find features (e.g., genes,\ngenomic regions, etc) that are active in subsets of cells that\nare non-randomly positioned inside an input space (such as 1D\ntrajectories, 2D tissue sections, multi-dimensional embeddings,\netc). For the theoretical background of 'singleCellHaystack' we\nrefer to our original paper Vandenbon and Diez (Nature\nCommunications, 2020) and our\nupdate Vandenbon and Diez (Scientific Reports, 2023)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "URL": "https://alexisvdb.github.io/singleCellHaystack/,\nhttps://github.com/alexisvdb/singleCellHaystack", "BugReports": "https://github.com/alexisvdb/singleCellHaystack/issues", "LazyData": "true", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://alexisvdb.r-universe.dev", "Date/Publication": "2025-12-04 09:57:34 UTC", "RemoteUrl": "https://github.com/alexisvdb/singlecellhaystack", "RemoteRef": "HEAD", "RemoteSha": "652e5d681dd23603b305c13884db050ba7f2a398", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 09:43:15 UTC", "User": "root" }, "Author": "Alexis Vandenbon [aut, cre] (ORCID:\n),\nDiego Diez [aut] (ORCID: )", "Maintainer": "Alexis Vandenbon ", "MD5sum": "131393fedd07272e6f69599334d29eed", "_user": "alexisvdb", "_type": "src", "_file": "singleCellHaystack_1.0.3.tar.gz", "_fileid": "af4b21a2f2e939cade2125ec7804340032c9e87e241220cb67cd5183fcb50c32", "_filesize": 2420821, "_sha256": "af4b21a2f2e939cade2125ec7804340032c9e87e241220cb67cd5183fcb50c32", "_created": "2026-06-02T09:43:15.000Z", "_published": "2026-06-02T09:47:21.331Z", "_distro": "noble", "_jobs": [ { "job": 79043254128, "time": 198, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7354367201" }, { "job": 79043254151, "time": 189, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7354367768" }, { "job": 79043254315, "time": 195, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7354356887" }, { "job": 79043254326, "time": 184, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7354350854" }, { "job": 79042646763, "time": 202, "config": "source", "r": "4.6.0", "check": 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No changes were made to this function.", "topics": [ "default_bandwidth.nrd" ] }, { "page": "extract_row_dgRMatrix", "title": "Returns a row of a sparse matrix of class dgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)", "topics": [ "extract_row_dgRMatrix" ] }, { "page": "extract_row_lgRMatrix", "title": "Returns a row of a sparse matrix of class lgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)", "topics": [ "extract_row_lgRMatrix" ] }, { "page": "get_D_KL", "title": "Calculates the Kullback-Leibler divergence between distributions.", "topics": [ "get_D_KL" ] }, { "page": "get_D_KL_continuous_highD", "title": "Calculates the Kullback-Leibler divergence between distributions for the high-dimensional continuous version of haystack.", "topics": [ "get_D_KL_continuous_highD" ] }, { "page": "get_D_KL_highD", "title": "Calculates the Kullback-Leibler divergence between distributions for the high-dimensional version of haystack().", "topics": [ "get_D_KL_highD" ] }, { "page": "get_density", "title": "Function to get the density of points with value TRUE in the (x,y) plot", "topics": [ "get_density" ] }, { "page": "get_dist_two_sets", "title": "Calculate the pairwise Euclidean distances between the rows of 2 matrices.", "topics": [ "get_dist_two_sets" ] }, { "page": "get_euclidean_distance", "title": "Calculate the Euclidean distance between x and y.", "topics": [ "get_euclidean_distance" ] }, { "page": "get_grid_points", "title": "A function to decide grid points in a higher-dimensional space", "topics": [ "get_grid_points" ] }, { "page": "get_log_p_D_KL", "title": "Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations.", "topics": [ "get_log_p_D_KL" ] }, { "page": "get_log_p_D_KL_continuous", "title": "Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations for the continuous version of haystack.", "topics": [ "get_log_p_D_KL_continuous" ] }, { "page": "get_parameters_haystack", "title": "Function that decides most of the parameters that will be used during the \"Haystack\" analysis.", "topics": [ "get_parameters_haystack" ] }, { "page": "get_reference", "title": "Get reference distribution", "topics": [ "get_reference" ] }, { "page": "haystack", "title": "The main Haystack function", "topics": [ "haystack", "haystack.data.frame", "haystack.matrix", "haystack.Seurat", "haystack.SingleCellExperiment" ] }, { "page": "haystack_2D", "title": "The main Haystack function, for 2-dimensional spaces.", "topics": [ "haystack_2D" ] }, { "page": "haystack_continuous_highD", "title": "The main Haystack function, for higher-dimensional spaces and continuous expression levels.", "topics": [ "haystack_continuous_highD" ] }, { "page": "haystack_highD", "title": "The main Haystack function, for higher-dimensional spaces.", "topics": [ "haystack_highD" ] }, { "page": "hclust_haystack", "title": "Function for hierarchical clustering of genes according to their expression distribution in 2D or multi-dimensional space", "topics": [ "hclust_haystack", "hclust_haystack.data.frame", "hclust_haystack.matrix" ] }, { "page": "hclust_haystack_highD", "title": "Function for hierarchical clustering of genes according to their distribution in a higher-dimensional space.", "topics": [ "hclust_haystack_highD" ] }, { "page": "hclust_haystack_raw", "title": "Function for hierarchical clustering of genes according to their distribution on a 2D plot.", "topics": [ "hclust_haystack_raw" ] }, { "page": "kde2d_faster", "title": "Based on the MASS kde2d() function, but heavily simplified; it's just tcrossprod() now.", "topics": [ "kde2d_faster" ] }, { "page": "kmeans_haystack", "title": "Function for k-means clustering of genes according to their expression distribution in 2D or multi-dimensional space", "topics": [ "kmeans_haystack", "kmeans_haystack.data.frame", "kmeans_haystack.matrix" ] }, { "page": "kmeans_haystack_highD", "title": "Function for k-means clustering of genes according to their distribution in a higher-dimensional space.", "topics": [ "kmeans_haystack_highD" ] }, { "page": "kmeans_haystack_raw", "title": "Function for k-means clustering of genes according to their distribution on a 2D plot.", "topics": [ "kmeans_haystack_raw" ] }, { "page": "plot_compare_ranks", "title": "plot_compare_ranks", "topics": [ "plot_compare_ranks" ] }, { "page": "plot_gene_haystack", "title": "Visualizing the detection/expression of a gene in a 2D plot", "topics": [ "plot_gene_haystack", "plot_gene_haystack.data.frame", "plot_gene_haystack.matrix", "plot_gene_haystack.Seurat", "plot_gene_haystack.SingleCellExperiment" ] }, { "page": "plot_gene_haystack_raw", "title": "Visualizing the detection/expression of a gene in a 2D plot", "topics": [ "plot_gene_haystack_raw" ] }, { "page": "plot_gene_set_haystack", "title": "Visualizing the detection/expression of a set of genes in a 2D plot", "topics": [ "plot_gene_set_haystack", "plot_gene_set_haystack.data.frame", "plot_gene_set_haystack.matrix", "plot_gene_set_haystack.Seurat", "plot_gene_set_haystack.SingleCellExperiment" ] }, { "page": "plot_gene_set_haystack_raw", "title": "Visualizing the detection/expression of a set of genes in a 2D plot", "topics": [ "plot_gene_set_haystack_raw" ] }, { "page": "plot_rand_fit", "title": "plot_rand_fit", "topics": [ "plot_rand_fit", "plot_rand_fit.haystack" ] }, { "page": "plot_rand_KLD", "title": "plot_rand_KLD", "topics": [ "plot_rand_KLD" ] }, { "page": "read_haystack", "title": "Function to read haystack results from file.", "topics": [ "read_haystack" ] }, { "page": "show_result_haystack", "title": "show_result_haystack", "topics": [ "show_result_haystack", "show_result_haystack.haystack" ] }, { "page": "write_haystack", "title": "Function to write haystack result data to file.", "topics": [ "write_haystack" ] } ], "_readme": "https://github.com/alexisvdb/singlecellhaystack/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "farver", "ggplot2", "glue", "gtable", "isoband", "labeling", "lattice", "lifecycle", "magrittr", "Matrix", "plyr", "R6", "RColorBrewer", "Rcpp", "reshape2", "rlang", "S7", "scales", "stringi", "stringr", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": 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