Package 'GeneScape' reference manual

Title:	Simulate single cell and spatial transcriptomics data
Description:	Simulate single cell and spatial transcriptomics data with complicated heterogeneity structure with or without reference dataset.
Authors:	Qi Gao
Maintainer:	Qi Gao <[email protected]>
License:	GPL (>= 3)
Version:	1.2
Built:	2025-03-07 04:49:35 UTC
Source:	https://github.com/qigao1/genescape

add_distort

Description

The function adds distortions to the input tissue type regions.

Usage

add_distort(old_tissue_region, sample_angle = pi/6, tissue_angle = 0,
  sigma = 0.01, scaleloc = FALSE, thres = 1)
add_distort(old_tissue_region, sample_angle = pi/6, tissue_angle = 0,
  sigma = 0.01, scaleloc = FALSE, thres = 1)

Arguments

`old_tissue_region`	An sf object of the tissue type region polygons.
`sample_angle`	a number of the angles for the whole sample.
`tissue_angle`	a number or a vector of the angles for each of the tissue regions. Tissue type regions are rotated clockwise by the input angle.
`sigma`	standard deviation of the normal error
`scaleloc`	whether to scale the location coordinates to [0,1]
`thres`	threshold parameter of the concave hull calculation method. Larger value results in simpler shapes

Details

The function adds distortions to the input tissue type regions by adding normal errors to the coordinates of the vertices of the tissue type region polygons.

Value

An sf object of the polygons of the updated tissue type regions.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

aggregate_expression

Description

The function aggregate the gene expression level in high resolution data to generate low resolution data.

Usage

aggregate_expression(counts, cell_position, spot_position,
  dis_thres_prop = 0.03, sigma_prop = 0.01, lib_size_mean = 5000,
  lib_size_dispersion = 3, cell_annotation = NULL)
aggregate_expression(counts, cell_position, spot_position,
  dis_thres_prop = 0.03, sigma_prop = 0.01, lib_size_mean = 5000,
  lib_size_dispersion = 3, cell_annotation = NULL)

Arguments

`counts`	read count matrix
`cell_position`	coordinates of the cells
`spot_position`	coordinates of the spots
`dis_thres_prop`	radius of the spot
`sigma_prop`	standard deviation of normal distribution. larger sigma implies that cells outside of the spot are more likely to contribute to the read observed in the spot
`lib_size_mean`	mean parameter of target library size (negative binomial distribution)
`lib_size_dispersion`	dispersion parameter of target library size (negative binomial distribution)
`cell_annotation`	cell type annotations

Details

This function aggregate the gene expression level in high resolution (cell level) data to generate low resolution (spot level) data. Cells contribute to the spots based on the distance between cell centers and spot centers, with the cells inside the spot contribute all their reads to the correponding spot. After obtaining the read pool, the funtion also downsample the reads to a target mean spot library size.

Value

A matrix of the gene (row) expression in each spot (column)

assign_cell_types

Description

The function chooses the cell type for each cell.

Usage

assign_cell_types(ttype, nttype, cell_type_proportion = NULL)
assign_cell_types(ttype, nttype, cell_type_proportion = NULL)

Arguments

`ttype`	tissue types of the cells
`nttype`	total number of possible tissue types. should be no less than the max value of ttype and the number of unique values in ttype. some tissue types may not exist in ttype
`cell_type_proportion`	matrix of cell type proportion, cell type (row) by tissue type (column)

Details

This function choose the cell type for each cell. Each tissue type could contains multiple types of cells. Based on the cell type proportion, this function randomly samples the cell type for each cell based on their corresponding tissue type.

Value

A vector of the assigned cell type

assign_tissue_types

Description

The function assigns to each cell the corresponding tissue type.

Usage

assign_tissue_types(tissue_region, cell_spot_position, nneighbor = 3)
assign_tissue_types(tissue_region, cell_spot_position, nneighbor = 3)

Arguments

`tissue_region`	sf object of tissue region polygons.
`cell_spot_position`	coordinates for the cells
`nneighbor`	number of nearest tissue region taken into account

Details

This function assigns the cells to a tissue type if the cell is located in the tissue region polygon

Value

A vector of the tissue region type

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

cell_position_simulation

Description

The function simulates the coordinates of the cells within the sample region.

Usage

cell_position_simulation(sample_vertices, ncells = 10000)
cell_position_simulation(sample_vertices, ncells = 10000)

Arguments

`sample_vertices`	coordinates for the vertices of sample polygon. Each row for a vertex.
`ncells`	number of cells to simulate

Details

This function simulates the location of the cells within the sample region

Value

A matrix of the centers, 1st column as x coordinate and 2nd column for the y coordinate

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

changeLibSize This function modifies the simulated counts so that the total read count in each spot is the same as the example data.

Description

changeLibSize This function modifies the simulated counts so that the total read count in each spot is the same as the example data.

Usage

changeLibSize(count, nreadspot, weight)
changeLibSize(count, nreadspot, weight)

Arguments

`count`	simulated matrix of read count, rows as genes and columns as spots
`nreadspot`	number of total read count in each spot in the example data
`weight`	a matrix of probability weights for the read count of each gene in each cell being changed.

Value

a matrix of the updated read count

Author(s)

Qi Gao

cumsum_cpp This function calculates the cumulative sum (partial sum) of a vector.

Description

cumsum_cpp This function calculates the cumulative sum (partial sum) of a vector.

Usage

cumsum_cpp(x)
cumsum_cpp(x)

Arguments

`x`	vector to be summed.

Value

vector of the cumulative sum (partial sum)

Author(s)

Qi Gao

downSampleRead This function uses down-sampling to obtain the reads in each spot.

Description

downSampleRead This function uses down-sampling to obtain the reads in each spot.

Usage

downSampleRead(count, nread)
downSampleRead(count, nread)

Arguments

`count`	gene (row) by cell (column) matrix. Gene read count in each cell.
`nread`	cell (row) by spot (column) matrix. Target number of reads sampled from each cell in each spot.

Value

gene (row) by spot (column) matrix. Gene expression level in each spot.

Author(s)

Qi Gao

est_bound

Description

The function estimates tissue type regions using cell/spot locations and the corresponding tissue type.

Usage

est_bound(sploc, ttype, nttype, thres = 1)
est_bound(sploc, ttype, nttype, thres = 1)

Arguments

`sploc`	coordinates for the cells.
`ttype`	tissue types of the cells. should be integers starting from 1.
`nttype`	total number of possible tissue types. should be no less than the max value of ttype and the number of unique values in ttype. some tissue types may not exist in ttype
`thres`	threshold parameter of the concave hull calculation method. Larger value results in simpler shapes

Details

The function estimates tissue type regions using cell/spot locations and the corresponding tissue type from reference data.

Value

A sf object of the polygons of the tissue type regions estimated from data.

References

Joel Gombin and Ramnath Vaidyanathan and Vladimir Agafonkin (2020). concaveman: A Very Fast 2D Concave Hull Algorithm. R package version 1.1.0. https://CRAN.R-project.org/package=concaveman

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

estimate_gamma This function estimates gamma distribution shape and rate parameters.

Description

estimate_gamma This function estimates gamma distribution shape and rate parameters.

Usage

estimate_gamma(x)
estimate_gamma(x)

Arguments

`x`	target data vector

Value

list of gamma distribution shape and rate parameters

Author(s)

Qi Gao

estimate_lognormal This function estimates estimate_lognormal distribution loc and scale parameters.

Description

estimate_lognormal This function estimates estimate_lognormal distribution loc and scale parameters.

Usage

estimate_lognormal(x)
estimate_lognormal(x)

Arguments

`x`	target data vector

Value

list of lognormal distribution loc and scale parameters

Author(s)

Qi Gao

estimate_nb This function estimates negative binomial distribution mean and dispersion parameters.

Description

estimate_nb This function estimates negative binomial distribution mean and dispersion parameters.

Usage

estimate_nb(datavec)
estimate_nb(datavec)

Arguments

datavec

target data vector

Value

vector of negative binomial distribution mean (1st element), dispersion (2nd element) parameters and 0 (3rd element).

Author(s)

Qi Gao

estimate_zinb This function estimates the mean, dispersion and zero inflation parameters in zero-inflated negative binomial distribution.

Description

estimate_zinb This function estimates the mean, dispersion and zero inflation parameters in zero-inflated negative binomial distribution.

Usage

estimate_zinb(data, maxiter = 400)
estimate_zinb(data, maxiter = 400)

Arguments

`data`	target data matrix, rows as genes and columns as spots
`maxiter`	maximum number of iteration for R optim

Value

matrix of negative binomial distribution mean (1st column), dispersion (2nd column) and zero-inflation (3rd column) parameters.

Author(s)

Qi Gao

estimate_zip

Description

This function estimate zero-inflated poisson distribution parameters from data vector

Usage

estimate_zip(datavec)
estimate_zip(datavec)

Arguments

datavec

target data vector

Value

a vector of zero-inflated poisson distribution parameters. first element is mean parameter, while second element is zero-inflation parameter.

References

S. Dencks, M. Piepenbrock and G. Schmitz, "Assessing Vessel Reconstruction in Ultrasound Localization Microscopy by Maximum Likelihood Estimation of a Zero-Inflated Poisson Model," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 67, no. 8, pp. 1603-1612, Aug. 2020, doi: 10.1109/TUFFC.2020.2980063.

fcsim

Description

This function similate differential expression fold change level

Usage

fcsim(n.gene, de.id, fc.mean, fc.sd)
fcsim(n.gene, de.id, fc.mean, fc.sd)

Arguments

`n.gene`	total number of genes
`de.id`	index of differentially expressed genes
`fc.mean`	location parameter for fold change (normal distribution)
`fc.sd`	scale parameter for fold change (normal distribution)

References

Zappia, L., Phipson, B., & Oshlack, A. (2017). Splatter: Simulation of single-cell RNA sequencing data. Genome Biology, 18(1). https://doi.org/10.1186/s13059-017-1305-0

GeneScape_est

Description

This function estimate zero-inflated poisson distribution parameters from data

Usage

GeneScape_est(data, groups = NULL)
GeneScape_est(data, groups = NULL)

Arguments

`data`	target gene (row) by cell (column) data matrix
`groups`	group of the cells

Value

list of two gene (row) by group (column) matrices. first matrix contains the mean parameter, while second matrix contains zero inflation parameters.

GeneScape_par

Description

This function simulate parameters for single cell RNAseq data simulation with complicated differential expression.

Usage

GeneScape_par(nCells = 6000, nGroups = NULL, groups = NULL,
  lib.size = NULL, lib.size.mean = 10000, lib.size.sd = 2000,
  de.fc.mat = NULL, nGenes = 5000, gene.mean.shape = 0.3,
  gene.mean.rate = 0.15, gene.means = NULL, zero.inflat = NULL,
  add.zero.inflat = TRUE, zero.inflat.times = 0.5, de.n = 50,
  de.share = NULL, de.id = NULL, de.fc.mean = 1, de.fc.sd = 0.2,
  add.sub = FALSE, sub.major = NULL, sub.prop = 0.1, sub.group = NULL,
  sub.de.n = 20, sub.de.id = NULL, sub.de.common = FALSE,
  sub.de.fc.mean = 1, sub.de.fc.sd = 0.2)
GeneScape_par(nCells = 6000, nGroups = NULL, groups = NULL,
  lib.size = NULL, lib.size.mean = 10000, lib.size.sd = 2000,
  de.fc.mat = NULL, nGenes = 5000, gene.mean.shape = 0.3,
  gene.mean.rate = 0.15, gene.means = NULL, zero.inflat = NULL,
  add.zero.inflat = TRUE, zero.inflat.times = 0.5, de.n = 50,
  de.share = NULL, de.id = NULL, de.fc.mean = 1, de.fc.sd = 0.2,
  add.sub = FALSE, sub.major = NULL, sub.prop = 0.1, sub.group = NULL,
  sub.de.n = 20, sub.de.id = NULL, sub.de.common = FALSE,
  sub.de.fc.mean = 1, sub.de.fc.sd = 0.2)

Arguments

`nCells`	number of cells
`nGroups`	number of cell groups
`groups`	group information for cells
`lib.size`	library size for cells
`lib.size.mean`	location parameter for library size (log-normal distribution)
`lib.size.sd`	scale parameter for library size (log-normal distribution)
`de.fc.mat`	differential expression fold change matrix, could be generated by this function
`nGenes`	number of genes
`gene.mean.shape`	shape parameter for mean expression level (Gamma distribution)
`gene.mean.rate`	rate parameter for mean expression level (Gamma distribution)
`gene.means`	mean gene expression levels
`zero.inflat`	vector of zero inflation paramters
`add.zero.inflat`	whether to add zero inflation
`zero.inflat.times`	the zero inflation paramter is simulated as zero.inflat.times / (1 + exp(gene.means))
`de.n`	number of differentially expressed genes in each cell type. Should be a integer or a vector of length nGroups
`de.share`	number of shared DE genes between neighbor cell types. Should be a vector of length (nGroups - 1)
`de.id`	the index of genes that are DE across cell types. Should be a list of vectors. Each vector corresponds to a cell type. With non-null value of de.id, de.n and de.share would be ignored.
`de.fc.mean`	the location parameter for the fold change of DE genes. Should be a number, a vector of length nGroups
`de.fc.sd`	the scale parameter for fold change (log-normal distribution). Should be a number or a vector of length nGroups
`add.sub`	whether to add sub-cell-types
`sub.major`	the major cell types correspond to the sub-cell-types
`sub.prop`	proportion of sub-cell-types in the corresponding major cell type
`sub.group`	cell index for sub-cell-types. With non-null sub.group specified, sub.prop would be ignored.
`sub.de.n`	number of differentially expressed genes in each sub-cell-type compared to the corresponding major cell type. Should be a integer or a vector of length sub.major
`sub.de.id`	the index of additional differentially expressed genes between sub-cell-types and the corresponding major cell types
`sub.de.common`	whether the additional differential expression structure should be same for all sub-cell-types
`sub.de.fc.mean`	similar to de.fc.mean, but for addtional differentially expressed genes in sub-cell-types
`sub.de.fc.sd`	similar to de.fc.sd, but for addtional differentially expressed genes in sub-cell-types

Value

A list of simulation parameters: vector of cell groups, vector of cell library size, gene expression parameter (zero-inflated poisson model), and matrix of differentially expression fold changes.

References

Zappia, L., Phipson, B., & Oshlack, A. (2017). Splatter: Simulation of single-cell RNA sequencing data. Genome Biology, 18(1). https://doi.org/10.1186/s13059-017-1305-0

Examples

set.seed(1)
para <- GeneScape_par()
set.seed(1)
para <- GeneScape_par()

GeneScape_sim

Description

This function generate single cell RNAseq data using simulation parameter.

Usage

GeneScape_sim(para, add.path = FALSE, path.n = 4, path.size = 20,
  path.cor = 0.7, path.id = NULL, band.width = 10, add.hub = FALSE,
  hub.n = 10, hub.size = 20, hub.cor = 0.4, hub.id = NULL,
  hub.fix = NULL)
GeneScape_sim(para, add.path = FALSE, path.n = 4, path.size = 20,
  path.cor = 0.7, path.id = NULL, band.width = 10, add.hub = FALSE,
  hub.n = 10, hub.size = 20, hub.cor = 0.4, hub.id = NULL,
  hub.fix = NULL)

Arguments

`para`	simulation parameter estimated by GeneScape_est or simulated by GeneScape_par
`add.path`	whether to add pathways (correlated genes)
`path.n`	number of pathways included. Should be a integer.
`path.size`	number of correlated genes (length of pathway). Should be a number or a vector of length path.n
`path.cor`	correlation parameters
`path.id`	gene index of correlated (pathway) genes. Should be a list of vectors, with each vector represents a pathway. With non-null value of path.id, path.n would be ignored.
`band.width`	No correlation exists if distance of 2 genes are further than band_width in a pathway
`add.hub`	whether to add hub genes
`hub.n`	number of hub genes included. Should be a integer.
`hub.size`	number of genes correlated to the hub gene. Should be a number or a vector of length hub.n
`hub.cor`	correlation parameters between hub genes and their correlated genes
`hub.id`	gene index of hub genes. Should be a list of vectors. With non-null value of hub.id, hub.n would be ignored.
`hub.fix`	user defined genes correlated to hub genes (others are randomly selected). Should be a list of vectors of length hub.n or same as hub.id.

Value

A list of read count data, cell groups, cell library size, gene mean expression, gene differential expression rate, pathway genes and hub gene indices.

References

Zappia, L., Phipson, B., & Oshlack, A. (2017). Splatter: Simulation of single-cell RNA sequencing data. Genome Biology, 18(1). https://doi.org/10.1186/s13059-017-1305-0

Examples

set.seed(1)
para <- GeneScape_par()
data <- GeneScape_sim(para)
set.seed(1)
para <- GeneScape_par()
data <- GeneScape_sim(para)

GeneScapeS_est This function estimates distribution parameters from an example data.

Description

GeneScapeS_est This function estimates distribution parameters from an example data.

Usage

GeneScapeS_est(data, ttype)
GeneScapeS_est(data, ttype)

Arguments

`data`	Example data matrix
`ttype`	Original tissue type of each spot

Value

list of matrices of negative binomial distribution mean (1st column), dispersion (2nd column) and zero-inflation (3rd column) parameters. one matrix for each tissue type.

Author(s)

Qi Gao

GeneScapeS_sim This function generates simulated data based on distribution parameters estimated from an example data.

Description

GeneScapeS_sim This function generates simulated data based on distribution parameters estimated from an example data.

Usage

GeneScapeS_sim(para, newttype, rank = TRUE, data = NULL, ttype = NULL)
GeneScapeS_sim(para, newttype, rank = TRUE, data = NULL, ttype = NULL)

Arguments

`para`	Example data matrix
`newttype`	output tissue type of each spot
`rank`	whether to rank the expression level in each tissue type based on the example
`data`	original reference dataset used for create para
`ttype`	Original tissue type of each spot

Value

matrix of simulated data

Author(s)

Qi Gao

inverse_logit This function is the inverse logit function.

Description

inverse_logit This function is the inverse logit function.

Usage

inverse_logit(x)
inverse_logit(x)

Arguments

x

number

Value

inverse logit function value with input x

Author(s)

Qi Gao

knn_tissue_update

Description

The function updates the tissue type for each cell.

Usage

knn_tissue_update(cell_spot_position, old_tissue_type, k)
knn_tissue_update(cell_spot_position, old_tissue_type, k)

Arguments

`cell_spot_position`	coordinates for the cells
`old_tissue_type`	previously assigned tissue type for each cell. should be integers start from one
`k`	number of nearest neighbor

Details

This function assigns to the each cell to a new tissue type by sampling the tissue types of its k nearest neighbors.

Value

A vector of the updated tissue type

knnassign This function uses KNN sampling to simulate gradually changed tissue types.

Description

knnassign This function uses KNN sampling to simulate gradually changed tissue types.

Usage

knnassign(distmat, ttype, nttype, k)
knnassign(distmat, ttype, nttype, k)

Arguments

`distmat`	Spot distance matrix
`ttype`	Input tissue type of each spot
`nttype`	total number of possible tissue types. should be no less than the max value of ttype and the number of unique values in ttype. some tissue types may not exist in ttype
`k`	parameter for KNN sampling

Value

List of tissue type weight and updated tissue type with gradual change

Author(s)

Qi Gao

rotate_pg

Description

The function rotate polygons clockwise.

Usage

rotate_pg(pg, angle = pi/12, center = NULL)
rotate_pg(pg, angle = pi/12, center = NULL)

Arguments

`pg`	An sf object of the polygon.
`angle`	a number of the angle. The polygon is rotated clockwise by the input angle.
`center`	customized rotation center

Details

The function rotate polygons clockwise based on the input angle around the center (by default its centroid).

Value

An sf object of the rotated polygon.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

rotate_sample

Description

The function rotate polygons clockwise.

Usage

rotate_sample(tissue_region, angle = pi/12, thres = 1)
rotate_sample(tissue_region, angle = pi/12, thres = 1)

Arguments

`tissue_region`	Tissue region polygons.
`angle`	a number of the angle. The polygon is rotated clockwise by the input angle.
`thres`	threshold parameter of the concave hull calculation method. Larger value results in simpler shapes

Details

The function rotate tissue regions clockwise based on the input angle around the center (by default its centroid).

Value

An sf object of the rotated tissue regions

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

sample_int This function samples integers from 1 to an upper limit integer value with equal probability without replacement.

Description

sample_int This function samples integers from 1 to an upper limit integer value with equal probability without replacement.

Usage

sample_int(maxvalue, nsample)
sample_int(maxvalue, nsample)

Arguments

`maxvalue`	the max integer to be sampled.
`nsample`	the number of integer to be sampled.

Value

vector of the integers in the sample

Author(s)

Qi Gao

sample_region_simulation

Description

This function simulates the sample region.

Usage

sample_region_simulation(nvert = 13, sample_shape = c("square", "circle"),
  limitx = c(0, 1), limity = c(0, 1))
sample_region_simulation(nvert = 13, sample_shape = c("square", "circle"),
  limitx = c(0, 1), limity = c(0, 1))

Arguments

`nvert`	number of vertices of the sample polygon
`sample_shape`	shape of the capture area
`limitx`	min and max value of x coordinates
`limity`	min and max value of y coordinates

Details

This function simulates the sample region with the capture area as a polygon.

Value

A matrix of the coordinates of sample polygon vertices

References

Valtr, P. Probability that n random points are in convex position. Discrete Comput Geom 13, 637-643 (1995). https://doi.org/10.1007/BF02574070

scale_sample

Description

The function shifts the tissue type regions.

Usage

scale_sample(tissue_region, scale_prop = c(1, 1), thres = 1)
scale_sample(tissue_region, scale_prop = c(1, 1), thres = 1)

Arguments

`tissue_region`	An sf object of the tissue type region polygons.
`scale_prop`	a vector of length 2, for x and y correspondingly. Value greater than 1 would enlarge the tissue type region on the corresponding coordinates.
`thres`	threshold parameter of the concave hull calculation method. Larger value results in simpler shapes

Details

The function shifts tissue type regions by modifying the x and y coordinates of the vertices of the tissue type region polygons. The sizes of modifications equal to the input proportion times the difference between the maximum of x (y) coordinates and the minimum of x (y) coordinates

Value

An sf object of the polygons of the updated tissue type regions.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

shift_sample

Description

The function scales the tissue type regions.

Usage

shift_sample(tissue_region, shift_prop = c(0, 0))
shift_sample(tissue_region, shift_prop = c(0, 0))

Arguments

`tissue_region`	An sf object of the tissue type region polygons.
`shift_prop`	a vector of length 2, for x and y correspondingly. Large absolute value implies greater shift. Positive value implies positive change of x and y coordinates (shift up and right)

Details

The function scales the tissue type regions around the sample centroid

Value

An sf object of the polygons of the updated tissue type regions.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

sp_dist_euclidean_cpp This function calculates Euclidean distance between cell locations and spot locations.

Description

sp_dist_euclidean_cpp This function calculates Euclidean distance between cell locations and spot locations.

Usage

sp_dist_euclidean_cpp(cloc, sloc)
sp_dist_euclidean_cpp(cloc, sloc)

Arguments

`cloc`	matrix of location coordinates of the cells. Each row represents a cell.
`sloc`	matrix of location coordinates of the spots. Each row represents a spot.

Value

matrix of the Euclidean distance, number of cells (row) by number of spots (column)

Author(s)

Qi Gao

spot_simulate

Description

The function simulates the spot positions.

Usage

spot_simulate(dimension = c(78, 64), n = 5000, structure = c("interlaced",
  "uniform", "random"), error_prop = 0.04, limitx = c(0, 1), limity = c(0,
  1))
spot_simulate(dimension = c(78, 64), n = 5000, structure = c("interlaced",
  "uniform", "random"), error_prop = 0.04, limitx = c(0, 1), limity = c(0,
  1))

Arguments

`dimension`	dimension of the spots, only used for "interlaced" and "uniform" structure.
`n`	number of spots, only used for "random" structure
`structure`	structure of the spots
`error_prop`	size of error added to the coordinates of the spots
`limitx`	min and max value of x coordinates
`limity`	min and max value of y coordinates

Details

This function simulates the spot coordinates. The "interlaced" structure mimic the Visium sequencing data. The "uniform" structure generate spot positions that are located on the vertices of squares. And the "random" structure randomly sample the spot locations.

Value

A matrix of the spot coordinates

tissue_region_simulation

Description

The function simulates regions of different tissue types within the sample region.

Usage

tissue_region_simulation(sample_vertices, ntissuetype = 4, centers = NULL)
tissue_region_simulation(sample_vertices, ntissuetype = 4, centers = NULL)

Arguments

`sample_vertices`	coordinates for the vertices of sample polygon. Each row for a vertex.
`ntissuetype`	number of tissue types
`centers`	centers of the tissue types

Details

This function simulates the tissue type regions using voronoi polygons correponding to the simulated or input centers

Value

A sf object of the polygons of the tissue type regions.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

update_sample_region

Description

The function updates the sample region based on the regions of different tissue types.

Usage

update_sample_region(tissue_region, thres = 1)
update_sample_region(tissue_region, thres = 1)

Arguments

`tissue_region`	An sf object of the tissue type region polygons.
`thres`	threshold parameter of the concave hull calculation method. Larger value results in simpler shapes

Details

The function updates the sample region by finding the concave hull of the vertices of tissue type region polygons.

Value

An sf object of the polygons of the updated tissue type regions.

References

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

zinb_nll This function calculates the negative log likelihood of zero-inflated negative binomial distribution.

Description

zinb_nll This function calculates the negative log likelihood of zero-inflated negative binomial distribution.

Usage

zinb_nll(y, par)
zinb_nll(y, par)

Arguments

`y`	observations
`par`	vector of 3 elements. parameters (mean, dispersion, zero-inflation) for zero-inflated negative binomial distribution.

Value

negative log likelihood of zero-inflated negative binomial distribution

Author(s)

Qi Gao

Package 'GeneScape'

Help Index

add_distort

Description

Usage

Arguments

Details

Value

References

aggregate_expression

Description

Usage

Arguments

Details

Value

assign_cell_types

Description

Usage

Arguments

Details

Value

assign_tissue_types

Description

Usage

Arguments

Details

Value

References

cell_position_simulation

Description

Usage

Arguments

Details

Value

References

changeLibSize This function modifies the simulated counts so that the total read count in each spot is the same as the example data.

Description

Usage

Arguments

Value

Author(s)

cumsum_cpp This function calculates the cumulative sum (partial sum) of a vector.

Description

Usage

Arguments

Value

Author(s)

downSampleRead This function uses down-sampling to obtain the reads in each spot.

Description

Usage

Arguments

Value

Author(s)

est_bound

Description

Usage

Arguments

Details

Value

References

estimate_gamma This function estimates gamma distribution shape and rate parameters.

Description

Usage

Arguments

Value

Author(s)

estimate_lognormal This function estimates estimate_lognormal distribution loc and scale parameters.

Description

Usage

Arguments

Value

Author(s)

estimate_nb This function estimates negative binomial distribution mean and dispersion parameters.

Description

Usage

Arguments

Value

Author(s)

estimate_zinb This function estimates the mean, dispersion and zero inflation parameters in zero-inflated negative binomial distribution.

Description