A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
The main Haystack function, for higher-dimensional spaces.
Function for hierarchical clustering of genes according to their expre...
Function for hierarchical clustering of genes according to their distr...
Function for hierarchical clustering of genes according to their distr...
Based on the MASS kde2d() function, but heavily simplified; it's just ...
Function for k-means clustering of genes according to their expression...
Function for k-means clustering of genes according to their distributi...
Function for k-means clustering of genes according to their distributi...
plot_compare_ranks
Visualizing the detection/expression of a gene in a 2D plot
Visualizing the detection/expression of a gene in a 2D plot
Visualizing the detection/expression of a set of genes in a 2D plot
Visualizing the detection/expression of a set of genes in a 2D plot
plot_rand_fit
plot_rand_KLD
Function to read haystack results from file.
Single cell RNA-seq dataset.
Single cell tSNE coordingates.
Default function given by function bandwidth.nrd in MASS. No changes w...
Returns a row of a sparse matrix of class dgRMatrix. Function made by ...
Returns a row of a sparse matrix of class lgRMatrix. Function made by ...
Calculates the Kullback-Leibler divergence between distributions.
Calculates the Kullback-Leibler divergence between distributions for t...
Calculates the Kullback-Leibler divergence between distributions for t...
Function to get the density of points with value TRUE in the (x,y) plo...
Calculate the pairwise Euclidean distances between the rows of 2 matri...
Calculate the Euclidean distance between x and y.
A function to decide grid points in a higher-dimensional space
Estimates the significance of the observed Kullback-Leibler divergence...
Estimates the significance of the observed Kullback-Leibler divergence...
Function that decides most of the parameters that will be used during ...
Get reference distribution
The main Haystack function
The main Haystack function, for 2-dimensional spaces.
The main Haystack function, for higher-dimensional spaces and continuo...
show_result_haystack
singleCellHaystack: A Universal Differential Expression Prediction Too...
Function to write haystack result data to file.
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>.
Useful links