Convert the coordinates of set of genes into vectors.

Usage

create_genesets(
  x,
  name_lst,
  cluster_info,
  sample_names,
  bin_type,
  bin_param,
  w_x,
  w_y,
  use_cm = FALSE,
  n_cores = 1
)

Arguments

x: a SingleCellExperiment or SpatialExperiment or SpatialFeatureExperiment object
name_lst: A named list of strings giving the name of features that are treated as background.
cluster_info: A dataframe/matrix containing the centroid coordinates, cluster and sample label for each cell.The column names must include "x" (x coordinate), "y" (y coordinate), "cluster" (cluster label) and "sample" (sample).
sample_names: a vector of strings giving the sample names
bin_type: A string indicating which bin shape is to be used for vectorization. One of "square" (default), "rectangle", or "hexagon".
bin_param: A numeric vector indicating the size of the bin. If the bin_type is "square" or "rectangle", this will be a vector of length two giving the numbers of rectangular quadrats in the x and y directions. If the bin_type is "hexagonal", this will be a number giving the side length of hexagons. Positive numbers only.
w_x: A numeric vector of length two specifying the x coordinate limits of enclosing box.
w_y: A numeric vector of length two specifying the y coordinate limits of enclosing box.
use_cm: A boolean value that specifies whether to create spatial vectors for genes using the count matrix and cell coordinates instead of the transcript coordinates when both types of information are available. The default setting is FALSE.
n_cores: A positive number specifying number of cores used for parallelizing permutation testing. Default is one core (sequential processing).

Value

a list of two matrices with the following components

gene_mt: contains the transcript count in each grid. Each row refers to a grid, and each column refers to a gene.
cluster_mt: contains the number of cells in a specific cluster in each grid. Each row refers to a grid, and each column refers to a cluster.

The row order of gene_mt matches the row order of cluster_mt.

A matrix contains the sum count in each grid. Each row refers to a grid, each column refers to a set in name_lst. The column name will match the names in name_lst.

Examples

library(SpatialExperiment)
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following object is masked from ‘package:SpatialFeatureExperiment’:
#> 
#>     saveRDS
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
set.seed(15)
trans = as.data.frame(rbind(cbind(x = runif(10, min=1, max=10),
                         y = runif(10, min=1, max=10),
                                feature_name="A"),
                         cbind(x = runif(5, min=10, max=24),
                               y = runif(5, min=1, max=10),
                               feature_name="B"),
                         cbind(x = runif(10, min=10, max=24),
                               y = runif(10, min=10, max=24),
                               feature_name="C")))
trans$x = as.numeric(trans$x)
trans$y = as.numeric(trans$y)
trans$cell = sample(c("cell1","cell2","cell2"),replace=TRUE,
                        size=nrow(trans))
# create SpatialExperiment object
trans_mol <- BumpyMatrix::splitAsBumpyMatrix(
    trans[, c("x", "y")], 
    row = trans$feature_name, col = trans$cell )
rep1_spe<- SpatialExperiment(
     assays = list(molecules = trans_mol),sample_id ="sample1" )
geneset_res <- create_genesets(x=rep1_spe, sample=c("sample1"),
                             name_lst=list(dummy_A=c("A","C"),
                                             dummy_B=c("A","B","C")),
                             bin_type="square",
                             bin_param=c(2,2),
                             w_x=c(0,25), w_y=c(0,25), cluster_info=NULL)