Package 'countland'

Title: Analysis of Biological Count Data, Especially from Single-Cell RNA-Seq
Description: A set of functions for applying a restricted linear algebra to the analysis of count-based data. See the accompanying preprint manuscript: "Normalizing need not be the norm: count-based math for analyzing single-cell data" Church et al (2022) <doi:10.1101/2022.06.01.494334> This tool is specifically designed to analyze count matrices from single cell RNA sequencing assays. The tools implement several count-based approaches for standard steps in single-cell RNA-seq analysis, including scoring genes and cells, comparing cells and clustering, calculating differential gene expression, and several methods for rank reduction. There are many opportunities for further optimization that may prove useful in the analysis of other data. We provide the source code freely available at <https://github.com/shchurch/countland> and encourage users and developers to fork the code for their own purposes.
Authors: Church Samuel H. [aut, cre]
Maintainer: Church Samuel H. <[email protected]>
License: MIT + file LICENSE
Version: 0.1.2
Built: 2025-02-26 04:33:01 UTC
Source: https://github.com/cran/countland

Help Index

Recapitulate Seurat centering scaled and transformed data

Description

Recapitulate Seurat centering scaled and transformed data

Usage

Center(C)

Arguments

C

countland object

Value

countland object with slots centered_counts

Perform spectral clustering on dot products.

Description

Perform spectral clustering on dot products.

Usage

Cluster(C, n_clusters, n_components = NULL)

Arguments

C

countland object
n_clusters

number of clusters, integer
n_components

number of components from spectral embedding to use (default NULL, will be set to n_clusters), integer

Value

countland object with slot cluster_labels

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)
C <- Embed(C,n_components=5)
C <- Cluster(C,n_clusters=3)

Internal function for calculating count index.

Description

Internal function for calculating count index.

Usage

CountIndex(lm)

Arguments

lm

column vector

Value

count index = largest n where n cells have >= n counts

Initialize a countland object from a dgCMatrix

Description

Initialize a countland object from a dgCMatrix

Usage

countland(m, remove_empty = TRUE, verbose = TRUE)

Arguments

m

A matrix of counts (dense or sparse)
remove_empty

filter out cells and genes with no observed counts (default=TRUE)
verbose

show stderr message statements (default=TRUE)

Value

countland object

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)

An S4 class to represent a countland object

Description

An S4 class to represent a countland object

Slots

counts

A dgCMatrix with rows as cells, columns as genes.

names_genes

A character vector of column names.

names_cells

A character vector of row names.

raw_counts

The count dgCMatrix as originally loaded.

raw_names_genes

The gene name character vector as originally loaded.

raw_names_cells

The cell name character vector as originally loaded.

subsample

A dgCMatrix with row sums equal.

cell_scores

A data.frame of cell count measures.

gene_scores

A data.frame of gene expression measures.

dots

A similarity dgCMatrix of dot products.

eigenvals

An vector of eigenvalues from spectral embedding

embedding

An array of two columns (spectral embeddings).

cluster_labels

A numeric vector of cluster assignments of length n cells.

marker_full

A list of data.frames with genes ranked for each cluster.

marker_genes

A data.frame of top ten marker genes per cluster.

matrixU

A dgCMatrix of dimensions cells x features.

matrixV

A dgCMatrix of dimensions genes x features.

matrixLambda

A diagonal dgCMatrix of scaling factors.

sharedcounts

A similarity dgCMatrix of shared counts between genes.

sum_sharedcounts

A dgCMatrix with counts summed within gene clusters.

sum_sharedcounts_all

A dgCMatrix with counts summed and including all genes not present in any cluster.

norm_factor

A numeric vector of cell normalization factors.

norm_counts

A dgCMatrix of normalized counts.

log_counts

A dgCMatrix of log transformed counts.

scaled_counts

A dgCMatrix of counts scaled by gene unit variance.

centered_counts

A dgCMatrix of counts centered at zero.

verbose

A T/F object for suppressing messages

Calculate pairwise dot products of counts between all cells.

Description

Calculate pairwise dot products of counts between all cells.

Usage

Dot(C, subsample = FALSE)

Arguments

C

countland object
subsample

if TRUE, use subsampled counts, otherwise use counts (default=FALSE)

Value

countland object with slot dots

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)

Perform spectral embedding on dot products.

Description

Perform spectral embedding on dot products.

Usage

Embed(C, n_components = 10)

Arguments

C

countland object
n_components

number of components, integer (default=10)

Value

countland object with slot embedding, eigenvals

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)
C <- Embed(C,n_components=5)

run integer matrix approximation

Description

run integer matrix approximation

Usage

IMA(X, params)

Arguments

X

observed data matrix
params

parameter object

Value

U, V, and Lambda matrix factors

rescale if max val is above upper bound

Description

rescale if max val is above upper bound

Usage

IMA_Compute_Init_Scaled(h, l_bound, u_bound)

Arguments

h

matrix to be rescaled
l_bound

lower bound
u_bound

upper bound

Value

rescaled matrix

function to initialize U, V, and Lambda

Description

function to initialize U, V, and Lambda

Usage

IMA_init(X, params)

Arguments

X

observed data matrix
params

parameter object

Value

initialized U, V, and Lambda matrices

Parameter class for IMA

Description

Parameter class for IMA

Usage

IMA_params(
  rank,
  u_bounds,
  l_bounds = c(0, 0),
  maxiter = 1e+06,
  stop_crit = 1e-04
)

Arguments

rank

target number of features in final matrices
u_bounds

upper bounds on integers
l_bounds

lower bounds on integers (default = c(0,0))
maxiter

maximum number of iterations (default = 1000000)
stop_crit

criterion of difference at which to stop (default = 0.0001)

Value

parameter object

Update factor matrix - see SUSTain code

Description

Update factor matrix - see SUSTain code

Usage

IMA_Update_Factor(M, coeff, mkrp, mode, lambda_, params)

Arguments

M

matrix to be updated (either U or V)
coeff

matrix used in updating algorithm
mkrp

matrix used in updating algorithm
mode

whether update U or V
lambda_

scaling matrix
params

parameter object

Value

updated matrix and scaling factors

Split dgCMatrix into column vectors.

Description

Split dgCMatrix into column vectors.

Usage

listCols(m)

Arguments

m

dgCMatrix

Value

list of column vectors, numeric

Recapitulate Seurat log transformation

Description

Recapitulate Seurat log transformation

Usage

Log(C)

Arguments

C

countland object

Value

countland object with slots log_counts

Recapitulate Seurat normalization

Description

Recapitulate Seurat normalization

Usage

Normalize(C)

Arguments

C

countland object

Value

countland object with slots norm_factor, norm_counts

Plots eigenvalues to investigate the optimal number of clusters

Description

Plots eigenvalues to investigate the optimal number of clusters

Usage

PlotEigengap(C)

Arguments

C

countland object

Value

generates plot of eigenvalues by number of components

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)
C <- Embed(C,n_components=5)
PlotEigengap(C)

Plot cells using spectral embedding of dot products.

Description

Plot cells using spectral embedding of dot products.

Usage

PlotEmbedding(C, colors = color_palette)

Arguments

C

countland object
colors

color palette for ggplot2, default=palette of 11 colors

Value

generates plot of cells in two spectral embedding dimensions

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)
C <- Embed(C,n_components=5)
C <- Cluster(C,n_clusters=3)
PlotEmbedding(C)

Generate a strip plot for counts across selected genes

Description

Generate a strip plot for counts across selected genes

Usage

PlotGeneCounts(C, gene_indices, colors = color_palette)

Arguments

C

countland object
gene_indices

vector of gene index values
colors

color palette for ggplot2, default=palette of 11 colors

Value

generates plot of gene count distributions

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
PlotGeneCounts(C,gene_indices=1:10)

Plot cells using integer matrix approximation

Description

Plot cells using integer matrix approximation

Usage

PlotIMA(C, x = 1, y = 2, colors = color_palette, subsample = TRUE)

Arguments

C

countland object
x

feature on x-axis, integer (default=1)
y

feature on y-axis, integer (default=2)
colors

color palette for ggplot2, default=palette of 11 colors
subsample

if TRUE, use subsampled counts (default), otherwise use counts

Value

generates plot of cells using integer matrix approximation

Plot the difference between the observed and reconstructed count matrix using integer matrix approximation and a series of total features.

Description

Plot the difference between the observed and reconstructed count matrix using integer matrix approximation and a series of total features.

Usage

PlotIMAElbow(C, max_features, u_bounds, subsample = TRUE)

Arguments

C

countland object
max_features

maximum number of features to assess, integer
u_bounds

upper bounds for U and V matrices, vector of length 2
subsample

if TRUE, use subsampled counts (default), otherwise use counts

Value

generates elbow plot for the difference between observed and reconstructed matrices as number of features increases

Plot cell using spectral embedding and display counts in a given gene.

Description

Plot cell using spectral embedding and display counts in a given gene.

Usage

PlotMarker(C, gene_index, colors = color_palette)

Arguments

C

countland object
gene_index

index value for gene to visualize
colors

color palette for ggplot2, default=palette of 11 colors

Value

generates plot of cells with spectral embedding, colored by marker gene counts

Plot cells using matrix of counts summed by clusters of genes.

Description

Plot cells using matrix of counts summed by clusters of genes.

Usage

PlotSharedCounts(C, x = 1, y = 2, colors = color_palette)

Arguments

C

countland object
x

gene cluster to plot on x-axis, integer (default=1)
y

gene cluster to plot on y-axis, integer (default=2)
colors

color palette for ggplot2, default=palette of 11 colors

Value

generates plot of cells using shared counts

Restore count matrix to original state

Description

Restore count matrix to original state

Usage

PrintGeneNumber(C)

Arguments

C

countland object

Value

countland object

Rank the top marker genes for each cluster from spectral clustering.

Description

Rank the top marker genes for each cluster from spectral clustering.

Usage

RankMarkerGenes(C, method = "prop-zero", subsample = FALSE)

Arguments

C

countland object
method

prop-zero to rank by proportion of cells that are non-zero (default), or rank-sums to rank using Wilcoxon rank-sums test
subsample

if TRUE, use subsampled counts, otherwise use counts (default=FALSE)

Value

countland object with slots marker_genes and marker_full

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Dot(C)
C <- Embed(C,n_components=5)
C <- Cluster(C,n_clusters=3)
C <- RankMarkerGenes(C,method='prop-zero',subsample=FALSE)

Internal function to remove empty columns and rows

Description

Internal function to remove empty columns and rows

Usage

RemoveEmpty(C)

Arguments

C

countland object

Value

countland object, count matrix updated

Recapitulate Seurat scaling to unit variance

Description

Recapitulate Seurat scaling to unit variance

Usage

RescaleVariance(C)

Arguments

C

countland object

Value

countland object with slots scaled_counts

Restore count matrix to original state

Description

Restore count matrix to original state

Usage

RestoreCounts(C)

Arguments

C

countland object

Value

countland object

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- SubsetGenes(C,gene_indices=1:200)
C <- SubsetCells(C,cell_indices=1:50)
C <- RestoreCounts(C)

Perform integer matrix approximation on count matrix.

Description

Perform integer matrix approximation on count matrix.

Usage

RunIMA(
  C,
  features,
  u_bounds,
  l_bounds = c(0, 0),
  maxiter = 1e+06,
  stop_crit = 1e-04,
  subsample = TRUE
)

Arguments

C

countland object
features

target number of features, integer
u_bounds

upper bounds for U and V matrices, vector of length 2
l_bounds

lower bounds for U and V matrices, vector of length 2 (default=c(0,0))
maxiter

maximum number of iterations, integer (default=1000000)
stop_crit

criterion for stopping based on difference between iterations, numeric (default=0.0001)
subsample

if TRUE, use subsampled counts (default), otherwise use counts

Value

countland object with slots matrixU, matrixV, matrixLambda

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- RunIMA(C,features=10,u_bounds=c(10,10),subsample=FALSE)

Recapitulate scikit.manifold.spectral_embedding from python.

Description

Recapitulate scikit.manifold.spectral_embedding from python.

Usage

ScikitManifoldSpectralEmbedding(A, n_components)

Arguments

A

similarity matrix, dgCMatrix
n_components

number of eigenvectors to retain, integer

Value

matrix of eigenvectors

Calculate several scores for counts across cells

Description

Calculate several scores for counts across cells

Usage

ScoreCells(C, gene_string = NULL)

Arguments

C

countland object
gene_string

string with regular expression expression matching gene names of interest (default=NULL)

Value

countland object with slot cell_scores

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- ScoreCells(C,gene_string="*149932$")

Calculate several scores for count-based gene expression.

Description

Calculate several scores for count-based gene expression.

Usage

ScoreGenes(C, subsample = FALSE)

Arguments

C

countland object
subsample

if TRUE, use subsampled counts, otherwise use counts (default=FALSE)

Value

countland object with slot gene_scores

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- ScoreGenes(C)

Combine groups of genes with similar counts by clustering and summing.

Description

Combine groups of genes with similar counts by clustering and summing.

Usage

SharedCounts(C, n_clusters, n_cells = 100, subsample = TRUE)

Arguments

C

countland object
n_clusters

number of clusters
n_cells

number of cells to sample for gene clustering
subsample

if TRUE, use subsampled counts (default), otherwise use counts

Value

countland object with slots shared_counts, sum_sharedcounts, sum_sharedcounts_all

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- SharedCounts(C,n_clusters=10,subsample=FALSE)

Subsample cells to a standard number of counts by randomly sampling observations without replacement.

Description

Subsample cells to a standard number of counts by randomly sampling observations without replacement.

Usage

Subsample(C, gene_counts = NA, cell_counts = NA)

Arguments

C

countland object
gene_counts

maximum total counts for genes
cell_counts

sequencing depth for all cells, or "min" to use the minimum cell total

Value

countland object with slot subsample

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- Subsample(C,gene_counts=250,cell_counts=100)

Internal function for subsampling a column from a sparse matrix.

Description

Internal function for subsampling a column from a sparse matrix.

Usage

SubsampleCol(lm, li, j, n_counts)

Arguments

lm

column vector
li

row positions
j

column index
n_counts

count to sample

Value

subsampled column as dgTMatrix components

Subsets cells using a vector of cell indices

Description

Subsets cells using a vector of cell indices

Usage

SubsetCells(C, cell_indices, remove_empty = TRUE)

Arguments

C

countland object
cell_indices

vector of cell index values
remove_empty

filter out cells and genes with no observed counts (default=TRUE)

Value

countland object, count matrix updated

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- SubsetCells(C,cell_indices=1:50)

Subsets genes using a vector of gene indices

Description

Subsets genes using a vector of gene indices

Usage

SubsetGenes(C, gene_indices, remove_empty = TRUE)

Arguments

C

countland object
gene_indices

vector of gene index values
remove_empty

filter out cells and genes with no observed counts (default=TRUE)

Value

countland object, count matrix updated

Examples

gold_path <- system.file("testdata", package = "countland", mustWork = TRUE)
gold.data <- Seurat::Read10X(data.dir = gold_path)
C <- countland(gold.data)
C <- SubsetGenes(C,gene_indices=1:200)