SPHM: The SPHM class

In jianhaizhang/spatialHeatmap: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

The SPHM class

Description

The SPHM class is designed to store numeric data and image objects for plotting spatial heatmaps.

Usage

SPHM(svg = NULL, bulk = NULL, cell = NULL, match = list(), output = list())

Arguments

svg	An SVG object containing one or multiple aSVG instances (see SVG and read_svg). In the aSVGs, spatial features (tissues, organs, etc) having counterparts with the same identifiers in the 'bulk' data will be colored accoording to expression profiles of chosen biomolecules (genes, proteins, etc).
bulk	The bulk data in form of numeric vector, data.frame, or SummarizedExperiment. See the 'data' argument of the function filter_data.
cell	The single-cell data in form of SingleCellExperiment. In the 'colData' slot, a column that stores cell group labels is required.
match	The list for re-matching in SHMs (only bulk data) or matching between single-cell and bulk data in co-visualization. SHMs A named list for rematching spatial features between numeric data (ftA, ftB) and aSVGs (ftC, ftD, ftE). In each slot, the slot name is a spatial feature from the data and the corresponding element is one or multiple spatial features from the aSVG. E.g. list(ftA = c('ftC', 'ftD'), ftB = c('ftE')). Co-visualization plots Mapping cells to tissues: a named list, where cell group labels from colData(sce. dimred)[, 'cell.group'] are the name slots and aSVG features are the corresponding list elements. Mapping tissues to cells: a named list, where tissues are the name slots and cells from colData(sce.dimred)[, 'cell.group'] are the corresponding list elements. Applicable when cell grouping methods are annodation labels, marker genes, clustering, or manual assignments.
output	A list of outputs, which is automatically generated.

Value

An SPHM object.

Author(s)

Jianhai Zhang [e-mail]
Dr. Thomas Girke [e-mail]

Examples

library(SummarizedExperiment)
# Import single-cell data.
sce.pa <- system.file("extdata/shinyApp/data", "cell_mouse_brain.rds", 
package="spatialHeatmap")
sce <- readRDS(sce.pa)
# Pre-processing.
sce.dimred.quick <- process_cell_meta(sce, 
qc.metric=list(subsets=list(Mt=rowData(sce)$featureType=='mito'), threshold=1)) 
colData(sce.dimred.quick)[1:3, 1:2] 
sce.aggr.quick <- aggr_rep(sce.dimred.quick, assay.na='logcounts', sam.factor='label', 
aggr='mean')
# Import the aSVG image.   
svg.mus.brain.pa <- system.file("extdata/shinyApp/data", "mus_musculus.brain.svg", 
package="spatialHeatmap")
svg.mus.brain <- read_svg(svg.mus.brain.pa) 
# List for mapping single cells to bulk. 
lis.match.quick <- list(hypothalamus=c('hypothalamus'), cortex.S1=c('cerebral.cortex', 'nose'))

# SPHM class for storing aSVG, bulk/sc data, and matching list. 
dat.quick <- SPHM(svg=svg.mus.brain, bulk=sce.aggr.quick, cell=sce.dimred.quick, 
match=lis.match.quick)

# Co-visualization plot. 
# covis(data=dat.quick, ID=c('Apod'), dimred='PCA', cell.group='label', 
# tar.cell=names(lis.match.quick), assay.na='logcounts', bar.width=0.11, dim.lgd.nrow=1, 
# height=0.7, legend.r=1.5, legend.key.size=0.02, legend.text.size=12, legend.nrow=3) 

jianhaizhang/spatialHeatmap documentation built on Nov. 28, 2024, 4:44 p.m.