Outputs a circular bullseye plot for a gene pair. The central circle is gene B in cells expressing gene A. Rings indicate neighbors with gene B, where the first ring is the first neighbor.

Description

Outputs a circular bullseye plot for a gene pair. The central circle is gene B in cells expressing gene A. Rings indicate neighbors with gene B, where the first ring is the first neighbor.

Usage

bullseye_plot(scores)

Arguments

Value

Returns a circular bullseye plot.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = bullseye_scores(data = expression, locations = locations)
results    = as.numeric(results[1, 3:ncol(results)]) # choose gene pair of interest (row index)
bullseye_plot(results)

Calculates bullseye statistics for ALL gene pairs. Counts the number of cells with gene B in those with gene A And in their neighbors Neighbor scores are cumulative and normalized by window size.

Description

Calculates bullseye statistics for ALL gene pairs. Counts the number of cells with gene B in those with gene A And in their neighbors Neighbor scores are cumulative and normalized by window size.

Usage

bullseye_scores(
  data,
  locations,
  window_sizes = 1:5,
  ratio_to_co = FALSE,
  log_2 = FALSE,
  output_matrix = FALSE
)

Arguments

Value

Returns a dataframe with each row representing a gene pair and each column representing the bullseye score for that pair in each window size. output_matrix = TRUE presents a matrix for each window size.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = bullseye_scores(data = expression, locations = locations)

Computes Pearson's correlation between pairs of columns. If one matrix is provided, the output is the pairwise correlations between its columns. If two matrices are provided, the output is the pairwise correlations between their columns.

Description

Computes Pearson's correlation between pairs of columns. If one matrix is provided, the output is the pairwise correlations between its columns. If two matrices are provided, the output is the pairwise correlations between their columns.

Usage

correlation(matrix1, matrix2 = NULL)

Arguments

Value

Returns a correlation matrix.

Computes cross-expression and co-expression p-values between all gene pairs.

Description

Computes cross-expression and co-expression p-values between all gene pairs.

Usage

cross_expression(
  data,
  locations,
  neighbor = 1,
  alpha_cross = 0.05,
  alpha_co = 0,
  output_matrix = FALSE
)

Arguments

Value

Returns a list containing gene pairs with co-expression and cross-expression p-values before and after false discovery rate (FDR) correction.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = cross_expression(data = expression, locations = locations)

Computes gene-gene correlations between cross-expressing cell-neighbor pairs. Cell and neighbor masks are used to consider mutually exclusive expression per gene pair.

Description

Computes gene-gene correlations between cross-expressing cell-neighbor pairs. Cell and neighbor masks are used to consider mutually exclusive expression per gene pair.

Usage

cross_expression_correlation(
  data,
  locations,
  neighbor = 1,
  output_matrix = FALSE
)

Arguments

Value

Returns a gene list with cross-expression correlation for each gene pair.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = cross_expression_correlation(data = expression, locations = locations)

Example gene expression matrix

Description

Gene expression matrix for one tissue slice.

Usage

data(expression)

Format

A matrix with cells as rows and genes as columns.

Source

Example slice obtained from https://data.mendeley.com/datasets/6drcm3hy2h/2

Calculates the number of elements common between columns of two matrices. This function performs a simple dot product when binarize = FALSE.

Description

Calculates the number of elements common between columns of two matrices. This function performs a simple dot product when binarize = FALSE.

Usage

get_cooccurrence_stats(matrix1, matrix2, sparse = TRUE, binarize = TRUE)

Arguments

Value

Returns a column by column matrix, where entries represent the number of common elements.

Example cell location matrix

Description

Spatial coordinates for each cell.

Usage

data(locations)

Format

A matrix with cells as rows and xy-coordinates as columns.

Source

Example slice obtained from https://data.mendeley.com/datasets/6drcm3hy2h/2

This function takes x and y coordinates and rotates them counterclockwise by the specified number of degrees, and mean centers the points.

Description

This function takes x and y coordinates and rotates them counterclockwise by the specified number of degrees, and mean centers the points.

Usage

rotate_coordinates(
  x,
  y,
  n_degrees = 0,
  center = FALSE,
  scale = FALSE,
  flip_x = FALSE,
  flip_y = FALSE
)

Arguments

Value

Returns a data frame containing the new x and y coordinates.

Examples

data("locations")
locations = as.data.frame(as.matrix(locations))
ggplot2::ggplot(data = locations) + ggplot2::aes(x = pos_x, y = pos_y) +
  ggplot2::geom_point(size = 0) + ggplot2::theme_classic()

locations = rotate_coordinates(x = locations$pos_x, y = locations$pos_y, n_degrees = 45)
ggplot2::ggplot(data = locations) + ggplot2::aes(x = pos_x, y = pos_y) +
  ggplot2::geom_point(size = 0) + ggplot2::theme_classic()

Smooths cells' gene expression by averaging its expression by the nearest neighbors. Optionally computes genes by genes Pearson's correlation matrix between cells by genes and neighbors by genes matrices.

Description

Smooths cells' gene expression by averaging its expression by the nearest neighbors. Optionally computes genes by genes Pearson's correlation matrix between cells by genes and neighbors by genes matrices.

Usage

smooth_cells(data, locations, neighbors_smoothed = 1, corr = TRUE)

Arguments

Value

Returns a smoothed gene expression matrix. If corr = TRUE, additionally returns a gene-gene correlation matrix.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = smooth_cells(data = expression, locations = locations)

Determines whether the supplied genes show spatial enrichment in cross-expression. Spatial enrichment can be interpreted as delineating anatomical boundaries.

Description

Determines whether the supplied genes show spatial enrichment in cross-expression. Spatial enrichment can be interpreted as delineating anatomical boundaries.

Usage

spatial_enrichment(
  data,
  locations,
  gene1,
  gene2,
  neighbor = 1,
  max_pairs = 20000
)

Arguments

Value

Returns a p-value and distance distributions between cross-expressing cells and cross-expressing and random cells.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
results    = spatial_enrichment(data = expression, locations = locations,
                                gene1 = "Calb1", gene2 = "Rasgrf2", max_pairs = 100)

Plots gene expression and cross-expression on tissue by coloring cells.

Description

Plots gene expression and cross-expression on tissue by coloring cells.

Usage

tissue_expression_plot(
  data,
  locations,
  gene1,
  gene2,
  cross_expression = TRUE,
  neighbor = 1,
  point_size = 0,
  scale_bar = 0
)

Arguments

Value

Returns a plot with cells shown as points and color indicating the genes it expresses.

Examples

data("locations")
data("expression")
locations  = as.matrix(locations)
expression = as.matrix(expression)
expression = expression[,1:5]
tissue_expression_plot(data = expression, locations = locations, gene1 = "Calb1", gene2 = "Rasgrf2")