| Title: | Single Cell Entropy Analysis of Gene Heterogeneity in Cell Populations |
| Version: | 0.0.1 |
| Description: | Analyse single cell RNA sequencing data using entropy to calculate heterogeneity and homogeneity of genes amongst the cell population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia and Ben D. MacArthur. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | entropy, tibble |
| Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2021-09-29 12:47:02 UTC; hwarden |
| Author: | Hugh Warden  [aut,
cre] |
| Maintainer: | Hugh Warden <hugh.warden@outlook.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-09-30 08:30:04 UTC |
Find the Heterogeneity of a Gene Within a Population
Description
Find the Heterogeneity of a Gene Within a Population
Usage
gene_het(expr, unit = "log2", normalise = TRUE, transpose = FALSE)
Arguments
expr |
A vector or matrix of gene expressions. For the matrix, genes should be represented as rows and cells as columns. |
unit |
The units to be parsed to the entropy function. |
normalise |
A logical value representing whether the gene frequencies should be normalised into a distribution. |
transpose |
A logical value representing whether the matrix should be transposed before any calculations are performed. |
Value
A vector of the information gained from the gene distribution compared to the uniform distribution. The higher the value more heterogeneous the cell is within the population.
Examples
#Creating Data
gene1 <- c(0,0,0,0,1,2,3)
gene2 <- c(5,5,3,2,0,0,0)
gene3 <- c(2,0,2,1,3,0,1)
gene4 <- c(3,3,3,3,3,3,3)
gene5 <- c(0,0,0,0,5,0,0)
gene_counts <- matrix(c(gene1,gene2,gene3,gene4,gene5), ncol = 5)
rownames(gene_counts) <- paste0("cell",1:7)
colnames(gene_counts) <- paste0("gene",1:5)
#Calculating Heterogeneity For Each Gene
gene_het(gene1)
gene_het(gene2)
gene_het(gene3)
gene_het(gene4)
gene_het(gene5)
#Calculating Heterogeneity For a Matrix
gene_het(gene_counts)
Find the Homogeneity of a Gene Within a Population
Description
Find the Homogeneity of a Gene Within a Population
Usage
gene_hom(expr, unit = "log2", normalise = TRUE, transpose = FALSE)
Arguments
expr |
A vector or matrix of gene expressions. For the matrix, genes should be represented as rows and cells as columns. |
unit |
The units to be parsed to the entropy function. |
normalise |
A logical value representing whether the gene frequencies should be normalised into a distribution. |
transpose |
A legical value representing whether the matrix should be transposed before any calculations are performed. |
Value
A vector of the information contained in the distribution of each gene. The higher this is, the more homogeneous the gene is within the cell population.
Examples
#Creating Data
gene1 <- c(0,0,0,0,1,2,3)
gene2 <- c(5,5,3,2,0,0,0)
gene3 <- c(2,0,2,1,3,0,1)
gene4 <- c(3,3,3,3,3,3,3)
gene5 <- c(0,0,0,0,5,0,0)
gene_counts <- matrix(c(gene1,gene2,gene3,gene4,gene5), ncol = 5)
rownames(gene_counts) <- paste0("cell",1:7)
colnames(gene_counts) <- paste0("gene",1:5)
#Calculating Homogeneity For Each Gene
gene_hom(gene1)
gene_hom(gene2)
gene_hom(gene3)
gene_hom(gene4)
gene_hom(gene5)
#Calculating Homogeneity For a Matrix
gene_hom(gene_counts)
Normalise Counts into a Distribution
Description
A function that takes frequency count data and normalises it into a probability distribution. Only available internally within SCEnt.
Usage
normalise(dist)
Arguments
dist |
A vector of a frequency distribution. |
Value
A vector of a probability distribution relative to the frequencies.
Feature Selection by Gene Heterogeneity
Description
Feature Selection by Gene Heterogeneity
Usage
scent_select(
expr,
bit_threshold = NULL,
count_threshold = NULL,
perc_threshold = NULL,
unit = "log2",
normalise = TRUE,
transpose = FALSE
)
Arguments
expr |
A matrix of gene expression data. Cells should be represented as rows and genes should be represented as columns. |
bit_threshold |
The threshold for the amount of bits of information a gene must add to be selected as a feature. Only one threshold can be used at a time. |
count_threshold |
A number represented how many of the most heterogeneous cells should be selected. Only one threshold can be used at a time. |
perc_threshold |
The percentile of the hetergeneity distribution above which a gene should be to be selected as a feature. |
unit |
The units to be used when calculating entropy. |
normalise |
A logical value representing whether the gene counts should be normalised into a probability distribution. |
transpose |
A logical value representing whether the matrix should be transposed before having any operations computed on it. |
Value
A matrix of gene expression values where genes with low heterogeneity have been removed.
Examples
#Creating Data
gene1 <- c(0,0,0,0,1,2,3)
gene2 <- c(5,5,3,2,0,0,0)
gene3 <- c(2,0,2,1,3,0,1)
gene4 <- c(3,3,3,3,3,3,3)
gene5 <- c(0,0,0,0,5,0,0)
gene_counts <- matrix(c(gene1,gene2,gene3,gene4,gene5), ncol = 5)
rownames(gene_counts) <- paste0("cell",1:7)
colnames(gene_counts) <- paste0("gene",1:5)
#Performing Feature Selection
scent_select(gene_counts, bit_threshold = 0.85)
scent_select(gene_counts, count_threshold = 2)
scent_select(gene_counts, perc_threshold = 0.25)
A Tidy Wrapper for Feature Selection by Heterogeneity
Description
A Tidy Wrapper for Feature Selection by Heterogeneity
Usage
scent_select_tidy(
expr,
bit_threshold = NULL,
count_threshold = NULL,
perc_threshold = NULL,
unit = "log2",
normalise = TRUE,
transpose = FALSE
)
Arguments
expr |
A tibble of gene expression data. Cells should be represented as rows and genes should be represented as columns. |
bit_threshold |
The threshold for the amount of bits of information a gene must add to be selected as a feature. Only one threshold can be used at a time. |
count_threshold |
A number represented how many of the most heterogeneous cells should be selected. Only one threshold can be used at a time. |
perc_threshold |
The percentile of the hetergeneity distribution above which a gene should be to be selected as a feature. |
unit |
The units to be used when calculating entropy. |
normalise |
A logical value representing whether the gene counts should be normalised into a probability distribution. |
transpose |
A logical value representing whether the matrix should be transposed before having any operations computed on it. |
Value
A tibble of gene expression values where genes with low heterogeneity have been removed.
Examples
#Creating Data
library(tibble)
gene1 <- c(0,0,0,0,1,2,3)
gene2 <- c(5,5,3,2,0,0,0)
gene3 <- c(2,0,2,1,3,0,1)
gene4 <- c(3,3,3,3,3,3,3)
gene5 <- c(0,0,0,0,5,0,0)
gene_counts <- matrix(c(gene1,gene2,gene3,gene4,gene5), ncol = 5)
rownames(gene_counts) <- paste0("cell",1:7)
colnames(gene_counts) <- paste0("gene",1:5)
gene_counts <- as_tibble(gene_counts)
#Performing Feature Selection
scent_select_tidy(gene_counts, bit_threshold = 0.85)
scent_select_tidy(gene_counts, count_threshold = 2)
scent_select_tidy(gene_counts, perc_threshold = 0.25)