| Type: | Package |
| Title: | A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data |
| Version: | 1.0.2 |
| 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>. |
| Imports: | methods, Matrix, splines, ggplot2, reshape2 |
| Suggests: | knitr, rmarkdown, testthat, SummarizedExperiment, SingleCellExperiment, SeuratObject, cowplot, wrswoR, sparseMatrixStats, ComplexHeatmap, patchwork |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| URL: | https://alexisvdb.github.io/singleCellHaystack/, https://github.com/alexisvdb/singleCellHaystack |
| BugReports: | https://github.com/alexisvdb/singleCellHaystack/issues |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-01-11 09:29:58 UTC; alex |
| Author: | Alexis Vandenbon 
[aut, cre],
Diego Diez [aut] |
| Maintainer: | Alexis Vandenbon <alexis.vandenbon@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-01-11 10:00:05 UTC |
singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
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.
Author(s)
Maintainer: Alexis Vandenbon alexis.vandenbon@gmail.com (ORCID)
Authors:
Diego Diez diego10ruiz@gmail.com (ORCID)
See Also
Useful links:
Report bugs at https://github.com/alexisvdb/singleCellHaystack/issues
Single cell RNA-seq dataset.
Description
Single cell RNA-seq dataset.
Single cell tSNE coordingates.
Description
Single cell tSNE coordingates.
Default function given by function bandwidth.nrd in MASS. No changes were made to this function.
Description
Default function given by function bandwidth.nrd in MASS. No changes were made to this function.
Usage
default_bandwidth.nrd(x)
Arguments
x |
A numeric vector |
Value
A suitable bandwith.
Returns a row of a sparse matrix of class dgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)
Description
Returns a row of a sparse matrix of class dgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)
Usage
extract_row_dgRMatrix(m, i = 1)
Arguments
m |
a sparse matrix of class dgRMatrix |
i |
the index of the row to return |
Value
A row (numerical vector) of the sparse matrix
Returns a row of a sparse matrix of class lgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)
Description
Returns a row of a sparse matrix of class lgRMatrix. Function made by Ben Bolker and Ott Toomet (see https://stackoverflow.com/questions/47997184/)
Usage
extract_row_lgRMatrix(m, i = 1)
Arguments
m |
a sparse matrix of class lgRMatrix |
i |
the index of the row to return |
Value
A row (logical vector) of the sparse matrix
Calculates the Kullback-Leibler divergence between distributions.
Description
Calculates the Kullback-Leibler divergence between distributions.
Usage
get_D_KL(classes, parameters, reference.prob, pseudo)
Arguments
classes |
A logical vector. Values are T is the gene is expressed in a cell, F is not. |
parameters |
Parameters of the analysis, as set by function 'get_parameters_haystack' |
reference.prob |
A reference distribution to calculate the divergence against. |
pseudo |
A pseudocount, used to avoid log(0) problems. |
Value
A numerical value, the Kullback-Leibler divergence
Calculates the Kullback-Leibler divergence between distributions for the high-dimensional continuous version of haystack.
Description
Calculates the Kullback-Leibler divergence between distributions for the high-dimensional continuous version of haystack.
Usage
get_D_KL_continuous_highD(
weights,
density.contributions,
reference.prob,
pseudo = 0
)
Arguments
weights |
A numerical vector with expression values of a gene. |
density.contributions |
A matrix of density contributions of each cell (rows) to each center point (columns). |
reference.prob |
A reference distribution to calculate the divergence against. |
pseudo |
A pseudocount, used to avoid log(0) problems. |
Value
A numerical value, the Kullback-Leibler divergence
Calculates the Kullback-Leibler divergence between distributions for the high-dimensional version of haystack().
Description
Calculates the Kullback-Leibler divergence between distributions for the high-dimensional version of haystack().
Usage
get_D_KL_highD(classes, density.contributions, reference.prob, pseudo = 0)
Arguments
classes |
A logical vector. Values are T is the gene is expressed in a cell, F is not. |
density.contributions |
A matrix of density contributions of each cell (rows) to each center point (columns). |
reference.prob |
A reference distribution to calculate the divergence against. |
pseudo |
A pseudocount, used to avoid log(0) problems. |
Value
A numerical value, the Kullback-Leibler divergence
Function to get the density of points with value TRUE in the (x,y) plot
Description
Function to get the density of points with value TRUE in the (x,y) plot
Usage
get_density(
x,
y,
detection,
rows.subset = 1:nrow(detection),
high.resolution = FALSE
)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
detection |
A logical matrix or dgRMatrix showing which gens (rows) are detected in which cells (columns) |
rows.subset |
Indices of the rows of 'detection' for which to get the densities. Default: all. |
high.resolution |
Logical: should high resolution be used? Default is FALSE. |
Value
A 3-dimensional array (dim 1: genes/rows of expression, dim 2 and 3: x and y grid points) with density data
Calculate the pairwise Euclidean distances between the rows of 2 matrices.
Description
Calculate the pairwise Euclidean distances between the rows of 2 matrices.
Usage
get_dist_two_sets(set1, set2)
Arguments
set1 |
A numerical matrix. |
set2 |
A numerical matrix. |
Value
A matrix of pairwise distances between the rows of 2 matrices.
Calculate the Euclidean distance between x and y.
Description
Calculate the Euclidean distance between x and y.
Usage
get_euclidean_distance(x, y)
Arguments
x |
A numerical vector. |
y |
A numerical vector. |
Value
A numerical value, the Euclidean distance.
A function to decide grid points in a higher-dimensional space
Description
A function to decide grid points in a higher-dimensional space
Usage
get_grid_points(input, method = "centroid", grid.points = 100)
Arguments
input |
A numerical matrix with higher-dimensional coordinates (columns) of points (rows) |
method |
The method to decide grid points. Should be "centroid" (default) or "seeding". |
grid.points |
The number of grid points to return. Default is 100. |
Value
Coordinates of grid points in the higher-dimensonal space.
Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations.
Description
Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations.
Usage
get_log_p_D_KL(T.counts, D_KL.observed, D_KL.randomized, output.dir = NULL)
Arguments
T.counts |
The number of cells in which a gene is detected. |
D_KL.observed |
A vector of observed Kullback-Leibler divergences. |
D_KL.randomized |
A matrix of Kullback-Leibler divergences of randomized datasets. |
output.dir |
Optional parameter. Default is NULL. If not NULL, some files will be written to this directory. |
Value
A vector of log10 p values, not corrected for multiple testing using the Bonferroni correction.
Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations for the continuous version of haystack.
Description
Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations for the continuous version of haystack.
Usage
get_log_p_D_KL_continuous(
D_KL.observed,
D_KL.randomized,
all.coeffVar,
train.coeffVar,
output.dir = NULL,
spline.method = "ns"
)
Arguments
D_KL.observed |
A vector of observed Kullback-Leibler divergences. |
D_KL.randomized |
A matrix of Kullback-Leibler divergences of randomized datasets. |
all.coeffVar |
Coefficients of variation of all genes. Used for fitting the Kullback-Leibler divergences. |
train.coeffVar |
Coefficients of variation of genes that will be used for fitting the Kullback-Leibler divergences. |
output.dir |
Optional parameter. Default is NULL. If not NULL, some files will be written to this directory. |
spline.method |
Method to use for fitting splines "ns" (default): natural splines, "bs": B-splines. |
Value
A vector of log10 p values, not corrected for multiple testing using the Bonferroni correction.
Function that decides most of the parameters that will be used during the "Haystack" analysis.
Description
Function that decides most of the parameters that will be used during the "Haystack" analysis.
Usage
get_parameters_haystack(x, y, high.resolution = FALSE)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
high.resolution |
Logical: should high resolution be used? Default is FALSE. |
Value
A list containing various parameters to use in the analysis.
Get reference distribution
Description
Get reference distribution
Usage
get_reference(param, use.advanced.sampling = NULL)
Arguments
param |
Parameters of the analysis, as set by function 'get_parameters_haystack' |
use.advanced.sampling |
If NULL naive sampling is used. If a vector is given (of length = no. of cells) sampling is done according to the values in the vector. |
Value
A list with two components, Q for the reference distribution and pseudo.
The main Haystack function
Description
The main Haystack function
Usage
haystack(x, ...)
## S3 method for class 'matrix'
haystack(
x,
expression,
weights.advanced.Q = NULL,
dir.randomization = NULL,
scale = TRUE,
grid.points = 100,
grid.method = "centroid",
...
)
## S3 method for class 'data.frame'
haystack(
x,
expression,
weights.advanced.Q = NULL,
dir.randomization = NULL,
scale = TRUE,
grid.points = 100,
grid.method = "centroid",
...
)
## S3 method for class 'Seurat'
haystack(
x,
coord,
assay = "RNA",
slot = "data",
dims = NULL,
cutoff = 1,
method = NULL,
weights.advanced.Q = NULL,
...
)
## S3 method for class 'SingleCellExperiment'
haystack(
x,
assay = "counts",
coord = "TSNE",
dims = NULL,
cutoff = 1,
method = NULL,
weights.advanced.Q = NULL,
...
)
Arguments
x |
a matrix or other object from which coordinates of cells can be extracted. |
... |
further parameters passed down to methods. |
expression |
a matrix with expression data of genes (rows) in cells (columns) |
weights.advanced.Q |
If NULL naive sampling is used. If a vector is given (of length = no. of cells) sampling is done according to the values in the vector. |
dir.randomization |
If NULL, no output is made about the random sampling step. If not NULL, files related to the randomizations are printed to this directory. |
scale |
Logical (default=TRUE) indicating whether input coordinates in x should be scaled to mean 0 and standard deviation 1. |
grid.points |
An integer specifying the number of centers (gridpoints) to be used for estimating the density distributions of cells. Default is set to 100. |
grid.method |
The method to decide grid points for estimating the density in the high-dimensional space. Should be "centroid" (default) or "seeding". |
coord |
name of coordinates slot for specific methods. |
assay |
name of assay data for Seurat method. |
slot |
name of slot for assay data for Seurat method. |
dims |
dimensions from coord to use. By default, all. |
cutoff |
cutoff for detection. |
method |
choose between highD (default) and 2D haystack. |
Value
An object of class "haystack"
The main Haystack function, for 2-dimensional spaces.
Description
The main Haystack function, for 2-dimensional spaces.
Usage
haystack_2D(
x,
y,
detection,
use.advanced.sampling = NULL,
dir.randomization = NULL
)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
use.advanced.sampling |
If NULL naive sampling is used. If a vector is given (of length = no. of cells) sampling is done according to the values in the vector. |
dir.randomization |
If NULL, no output is made about the random sampling step. If not NULL, files related to the randomizations are printed to this directory. |
Value
An object of class "haystack"
The main Haystack function, for higher-dimensional spaces and continuous expression levels.
Description
The main Haystack function, for higher-dimensional spaces and continuous expression levels.
Usage
haystack_continuous_highD(
x,
expression,
grid.points = 100,
weights.advanced.Q = NULL,
dir.randomization = NULL,
scale = TRUE,
grid.method = "centroid",
randomization.count = 100,
n.genes.to.randomize = 100,
selection.method.genes.to.randomize = "heavytails",
grid.coord = NULL,
spline.method = "ns"
)
Arguments
x |
Coordinates of cells in a 2D or higher-dimensional space. Rows represent cells, columns the dimensions of the space. |
expression |
a matrix with expression data of genes (rows) in cells (columns) |
grid.points |
An integer specifying the number of centers (grid points) to be used for estimating the density distributions of cells. Default is set to 100. |
weights.advanced.Q |
(Default: NULL) Optional weights of cells for calculating a weighted distribution of expression. |
dir.randomization |
If NULL, no output is made about the random sampling step. If not NULL, files related to the randomizations are printed to this directory. |
scale |
Logical (default=TRUE) indicating whether input coordinates in x should be scaled to mean 0 and standard deviation 1. |
grid.method |
The method to decide grid points for estimating the density in the high-dimensional space. Should be "centroid" (default) or "seeding". |
randomization.count |
Number of randomizations to use. Default: 100 |
n.genes.to.randomize |
Number of genes to use in randomizations. Default: 100 |
selection.method.genes.to.randomize |
Method used to select genes for randomization. |
grid.coord |
matrix of grid coordinates. |
spline.method |
Method to use for fitting splines "ns" (default): natural splines, "bs": B-splines. |
Value
An object of class "haystack", including the results of the analysis, and the coordinates of the grid points used to estimate densities.
Examples
# using the toy example of the singleCellHaystack package
# running haystack
res <- haystack(dat.tsne, dat.expression)
# list top 10 biased genes
show_result_haystack(res, n=10)
The main Haystack function, for higher-dimensional spaces.
Description
The main Haystack function, for higher-dimensional spaces.
Usage
haystack_highD(
x,
detection,
grid.points = 100,
use.advanced.sampling = NULL,
dir.randomization = NULL,
scale = TRUE,
grid.method = "centroid"
)
Arguments
x |
Coordinates of cells in a 2D or higher-dimensional space. Rows represent cells, columns the dimensions of the space. |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
grid.points |
An integer specifying the number of centers (grid points) to be used for estimating the density distributions of cells. Default is set to 100. |
use.advanced.sampling |
If NULL naive sampling is used. If a vector is given (of length = no. of cells) sampling is done according to the values in the vector. |
dir.randomization |
If NULL, no output is made about the random sampling step. If not NULL, files related to the randomizations are printed to this directory. |
scale |
Logical (default=TRUE) indicating whether input coordinates in x should be scaled to mean 0 and standard deviation 1. |
grid.method |
The method to decide grid points for estimating the density in the high-dimensional space. Should be "centroid" (default) or "seeding". |
Value
An object of class "haystack", including the results of the analysis, and the coordinates of the grid points used to estimate densities.
Examples
# I need to add some examples.
# A toy example will be added too.
Function for hierarchical clustering of genes according to their expression distribution in 2D or multi-dimensional space
Description
Function for hierarchical clustering of genes according to their expression distribution in 2D or multi-dimensional space
Usage
hclust_haystack(
x,
expression,
grid.coordinates,
hclust.method = "ward.D",
cor.method = "spearman",
...
)
## S3 method for class 'matrix'
hclust_haystack(
x,
expression,
grid.coordinates,
hclust.method = "ward.D",
cor.method = "spearman",
...
)
## S3 method for class 'data.frame'
hclust_haystack(
x,
expression,
grid.coordinates,
hclust.method = "ward.D",
cor.method = "spearman",
...
)
Arguments
x |
a matrix or other object from which coordinates of cells can be extracted. |
expression |
expression matrix. |
grid.coordinates |
coordinates of the grid points. |
hclust.method |
method used with hclust. |
cor.method |
method used with cor. |
... |
further parameters passed down to methods. |
Function for hierarchical clustering of genes according to their distribution in a higher-dimensional space.
Description
Function for hierarchical clustering of genes according to their distribution in a higher-dimensional space.
Usage
hclust_haystack_highD(
x,
detection,
genes,
method = "ward.D",
grid.coordinates = NULL,
scale = TRUE
)
Arguments
x |
Coordinates of cells in a 2D or higher-dimensional space. Rows represent cells, columns the dimensions of the space. |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
genes |
A set of genes (of the 'detection' data) which will be clustered. |
method |
The method to use for hierarchical clustering. See '?hclust' for more information. Default: "ward.D". |
grid.coordinates |
Coordinates of grid points in the same space as 'x', to be used to estimate densities for clustering. |
scale |
whether to scale data. |
Value
An object of class hclust, describing a hierarchical clustering tree.
Examples
# to be added
Function for hierarchical clustering of genes according to their distribution on a 2D plot.
Description
Function for hierarchical clustering of genes according to their distribution on a 2D plot.
Usage
hclust_haystack_raw(x, y, detection, genes, method = "ward.D")
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
genes |
A set of genes (of the 'detection' data) which will be clustered. |
method |
The method to use for hierarchical clustering. See '?hclust' for more information. Default: "ward.D". |
Value
An object of class hclust, describing a hierarchical clustering tree.
Based on the MASS kde2d() function, but heavily simplified; it's just tcrossprod() now.
Description
Based on the MASS kde2d() function, but heavily simplified; it's just tcrossprod() now.
Usage
kde2d_faster(dens.x, dens.y)
Arguments
dens.x |
Contribution of all cells to densities of the x-axis grid points. |
dens.y |
Contribution of all cells to densities of the y-axis grid points. |
Function for k-means clustering of genes according to their expression distribution in 2D or multi-dimensional space
Description
Function for k-means clustering of genes according to their expression distribution in 2D or multi-dimensional space
Usage
kmeans_haystack(x, expression, grid.coordinates, k, ...)
## S3 method for class 'matrix'
kmeans_haystack(x, expression, grid.coordinates, k, ...)
## S3 method for class 'data.frame'
kmeans_haystack(x, expression, grid.coordinates, k, ...)
Arguments
x |
a matrix or other object from which coordinates of cells can be extracted. |
expression |
expression matrix. |
grid.coordinates |
coordinates of the grid points. |
k |
number of clusters. |
... |
further parameters passed down to methods. |
Function for k-means clustering of genes according to their distribution in a higher-dimensional space.
Description
Function for k-means clustering of genes according to their distribution in a higher-dimensional space.
Usage
kmeans_haystack_highD(
x,
detection,
genes,
grid.coordinates = NULL,
k,
scale = TRUE,
...
)
Arguments
x |
Coordinates of cells in a 2D or higher-dimensional space. Rows represent cells, columns the dimensions of the space. |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
genes |
A set of genes (of the 'detection' data) which will be clustered. |
grid.coordinates |
Coordinates of grid points in the same space as 'x', to be used to estimate densities for clustering. |
k |
The number of clusters to return. |
scale |
whether to scale data. |
... |
Additional parameters which will be passed on to the kmeans function. |
Value
An object of class kmeans, describing a clustering into 'k' clusters
Examples
# to be added
Function for k-means clustering of genes according to their distribution on a 2D plot.
Description
Function for k-means clustering of genes according to their distribution on a 2D plot.
Usage
kmeans_haystack_raw(x, y, detection, genes, k, ...)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
detection |
A logical matrix showing which genes (rows) are detected in which cells (columns) |
genes |
A set of genes (of the 'detection' data) which will be clustered. |
k |
The number of clusters to return. |
... |
Additional parameters which will be passed on to the kmeans function. |
Value
An object of class kmeans, describing a clustering into 'k' clusters
plot_compare_ranks
Description
plot_compare_ranks
Usage
plot_compare_ranks(res1, res2, sort_by = "log.p.vals")
Arguments
res1 |
haystack result. |
res2 |
haystack result. |
sort_by |
column to sort results (default: log.p.vals). |
Visualizing the detection/expression of a gene in a 2D plot
Description
Visualizing the detection/expression of a gene in a 2D plot
Usage
plot_gene_haystack(x, ...)
## S3 method for class 'matrix'
plot_gene_haystack(x, dim1 = 1, dim2 = 2, ...)
## S3 method for class 'data.frame'
plot_gene_haystack(x, dim1 = 1, dim2 = 2, ...)
## S3 method for class 'SingleCellExperiment'
plot_gene_haystack(
x,
dim1 = 1,
dim2 = 2,
assay = "counts",
coord = "TSNE",
...
)
## S3 method for class 'Seurat'
plot_gene_haystack(
x,
dim1 = 1,
dim2 = 2,
assay = "RNA",
slot = "data",
coord = "tsne",
...
)
Arguments
x |
a matrix or other object from which coordinates of cells can be extracted. |
... |
further parameters passed to plot_gene_haystack_raw(). |
dim1 |
column index or name of matrix for x-axis coordinates. |
dim2 |
column index or name of matrix for y-axis coordinates. |
assay |
name of assay data for Seurat method. |
coord |
name of coordinates slot for specific methods. |
slot |
name of slot for assay data for Seurat method. |
Visualizing the detection/expression of a gene in a 2D plot
Description
Visualizing the detection/expression of a gene in a 2D plot
Usage
plot_gene_haystack_raw(
x,
y,
gene,
expression,
detection = NULL,
high.resolution = FALSE,
point.size = 1,
order.by.signal = FALSE
)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
gene |
name of a gene that is present in the input expression data, or a numerical index |
expression |
a logical/numerical matrix showing detection/expression of genes (rows) in cells (columns) |
detection |
an optional logical matrix showing detection of genes (rows) in cells (columns). If left as NULL, the density distribution of the gene is not plotted. |
high.resolution |
logical (default: FALSE). If set to TRUE, the density plot will be of a higher resolution |
point.size |
numerical value to set size of points in plot. Default is 1. |
order.by.signal |
If TRUE, cells with higher signal will be put on the foreground in the plot. Default is FALSE. |
Value
A plot
Visualizing the detection/expression of a set of genes in a 2D plot
Description
Visualizing the detection/expression of a set of genes in a 2D plot
Usage
plot_gene_set_haystack(x, ...)
## S3 method for class 'matrix'
plot_gene_set_haystack(x, dim1 = 1, dim2 = 2, ...)
## S3 method for class 'data.frame'
plot_gene_set_haystack(x, dim1 = 1, dim2 = 2, ...)
## S3 method for class 'SingleCellExperiment'
plot_gene_set_haystack(
x,
dim1 = 1,
dim2 = 2,
assay = "counts",
coord = "TSNE",
...
)
## S3 method for class 'Seurat'
plot_gene_set_haystack(
x,
dim1 = 1,
dim2 = 2,
assay = "RNA",
slot = "data",
coord = "tsne",
...
)
Arguments
x |
a matrix or other object from which coordinates of cells can be extracted. |
... |
further parameters passed to plot_gene_haystack_raw(). |
dim1 |
column index or name of matrix for x-axis coordinates. |
dim2 |
column index or name of matrix for y-axis coordinates. |
assay |
name of assay data for Seurat method. |
coord |
name of coordinates slot for specific methods. |
slot |
name of slot for assay data for Seurat method. |
Visualizing the detection/expression of a set of genes in a 2D plot
Description
Visualizing the detection/expression of a set of genes in a 2D plot
Usage
plot_gene_set_haystack_raw(
x,
y,
genes = NA,
detection,
high.resolution = TRUE,
point.size = 1,
order.by.signal = FALSE
)
Arguments
x |
x-axis coordinates of cells in a 2D representation (e.g. resulting from PCA or t-SNE) |
y |
y-axis coordinates of cells in a 2D representation |
genes |
Gene names that are present in the input expression data, or a numerical indeces. If NA, all genes will be used. |
detection |
a logical matrix showing detection of genes (rows) in cells (columns) |
high.resolution |
logical (default: TRUE). If set to FALSE, the density plot will be of a lower resolution |
point.size |
numerical value to set size of points in plot. Default is 1. |
order.by.signal |
If TRUE, cells with higher signal will be put on the foreground in the plot. Default is FALSE. |
Value
A plot
plot_rand_KLD
Description
Plots the distribution of randomized KLD for each of the genes, together with the mean and standard deviation, the 0.95 quantile and the 0.95 quantile from a normal distribution with mean and standard deviations from the distribution of KLDs. The logCV is indicated in the subtitle of each plot.
Usage
plot_rand_KLD(x, n = 12, log = TRUE, tail = FALSE)
Arguments
x |
haystack result. |
n |
number of genes from randomization set to plot. |
log |
whether to use log of KLD. |
tail |
whether the genes are chosen from the tail of randomized genes. |
plot_rand_fit
Description
plot_rand_fit
Usage
plot_rand_fit(x, type = c("mean", "sd"))
## S3 method for class 'haystack'
plot_rand_fit(x, type = c("mean", "sd"))
Arguments
x |
haystack object. |
type |
whether to plot mean or sd. |
Function to read haystack results from file.
Description
Function to read haystack results from file.
Usage
read_haystack(file)
Arguments
file |
A file containing 'haystack' results to read |
Value
An object of class "haystack"
show_result_haystack
Description
Shows the results of the 'haystack' analysis in various ways, sorted by significance. Priority of params is genes > p.value.threshold > n.
Usage
show_result_haystack(
res.haystack,
n = NULL,
p.value.threshold = NULL,
gene = NULL
)
## S3 method for class 'haystack'
show_result_haystack(
res.haystack,
n = NULL,
p.value.threshold = NULL,
gene = NULL
)
Arguments
res.haystack |
A 'haystack' result object. |
n |
If defined, the top "n" significant genes will be returned. Default: NA, which shows all results. |
p.value.threshold |
If defined, genes passing this p-value threshold will be returned. |
gene |
If defined, the results of this (these) gene(s) will be returned. |
Details
The output is a data.frame with the following columns: * D_KL the calculated KL divergence. * log.p.vals log10 p.values calculated from randomization. * log.p.adj log10 p.values adjusted by Bonferroni correction.
Value
A data.frame with 'haystack' results sorted by log.p.vals.
Examples
# using the toy example of the singleCellHaystack package
# running haystack
res <- haystack(dat.tsne, dat.expression)
# below are variations for showing the results in a table
# 1. list top 10 biased genes
show_result_haystack(res.haystack = res, n =10)
# 2. list genes with p value below a certain threshold
show_result_haystack(res.haystack = res, p.value.threshold=1e-10)
# 3. list a set of specified genes
set <- c("gene_497","gene_386", "gene_275")
show_result_haystack(res.haystack = res, gene = set)
Function to write haystack result data to file.
Description
Function to write haystack result data to file.
Usage
write_haystack(res.haystack, file)
Arguments
res.haystack |
A 'haystack' result variable |
file |
A file to write to |