spatialHeatmap-package: spatialHeatmap: Visualizing Spatial Assays in Anatomical...

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

spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions Spatial Heatmap, Spatial Enrichment, Data Mining, Co-visualization

Description

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

Details

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The spatialHeatmap package provides functionalities for visualizing cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. The color scheme used to represent the assay values can be customized by the user. This core functionality is called a spatial heatmap plot. It is enhanced with nearest neighbor visualization tools for groups of measured items (e.g. gene modules) sharing related abundance profiles, including matrix heatmaps combined with hierarchical clustering dendrograms and network representations. The functionalities of spatialHeatmap can be used either in a command-driven mode from within R or a graphical user interface (GUI) provided by a Shiny App that is also part of this package. While the R-based mode provides flexibility to customize and automate analysis routines, the Shiny App includes a variety of convenience features that will appeal to many biologists. Moreover, the Shiny App has been designed to work on both local computers as well as server-based deployments (e.g. cloud-based or custom servers) that can be accessed remotely as a centralized web service for using spatialHeatmap's functionalities with community and/or private data.

As anatomical images the package supports both tissue maps from public repositories and custom images provided by the user. In general any type of image can be used as long as it can be provided in SVG (Scalable Vector Graphics) format, where the corresponding spatial features have been defined (see aSVG below). The numeric values plotted onto a spatial heatmap are usually quantitative measurements from a wide range of profiling technologies, such as microarrays, next generation sequencing (e.g. RNA-Seq and scRNA-Seq), proteomics, metabolomics, or many other small- or large-scale experiments. For convenience, several preprocessing and normalization methods for the most common use cases are included that support raw and/or preprocessed data. Currently, the main application domains of the spatialHeatmap package are numeric data sets and spatially mapped images from biological and biomedical areas. Moreover, the package has been designed to also work with many other spatial data types, such a population data plotted onto geographic maps. This high level of flexibility is one of the unique features of spatialHeatmap. Related software tools for biological applications in this field are largely based on pure web applications (Winter et al. 2007; Waese et al. 2017) or local tools (Maag 2018; Muschelli, Sweeney, and Crainiceanu 2014) that typically lack customization functionalities. These restrictions limit users to utilizing pre-existing expression data and/or fixed sets of anatomical image collections. To close this gap for biological use cases, we have developed spatialHeatmap as a generic R/Bioconductor package for plotting quantitative values onto any type of spatially mapped images in a programmable environment and/or in an intuitive to use GUI application.

Author(s)

Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes [aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Author: Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes [aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Jianhai Zhang (PhD candidate at Genetics, Genomics and Bioinformatics, University of California, Riverside), Dr. Thomas Girke (Professor at Department of Botany and Plant Sciences, University of California, Riverside) Maintainer: Jianhai Zhang <[e-mail]> Jianhai Zhang <jzhan067@@ucr.edu>.

References

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See Also

norm_data, aggr_rep, filter_data, spatial_hm, submatrix, adj_mod, matrix_hm, network, return_feature, update_feature, shiny_shm, custom_shiny

Examples

#' ## The example data included in this package come from an RNA-seq analysis on 
#' ## development of 7 chicken organs under 9 time points (Cardoso-Moreira et al. 2019). 
#' ## The complete raw count data are downloaded using the R package ExpressionAtlas
#' ## (Keays 2019) with the accession number "E-MTAB-6769". 
#'
#' # Access example count data. 
#' count.chk <- read.table(system.file('extdata/shinyApp/data/count_chicken.txt', 
#' package='spatialHeatmap'), header=TRUE, row.names=1, sep='\t')
#' count.chk[1:3, 1:5]
#'
#' # A targets file describing spatial features and variables is made based on the 
#' # experiment design.
#' target.chk <- read.table(system.file('extdata/shinyApp/data/target_chicken.txt', 
#' package='spatialHeatmap'), header=TRUE, row.names=1, sep='\t')
#' # Every column in example data 2 corresponds with a row in the targets file. 
#' target.chk[1:5, ]
#' # Store example data in "SummarizedExperiment".
#' library(SummarizedExperiment)
#' se.chk <- SummarizedExperiment(assay=count.chk, colData=target.chk)
#'
#' # Normalize data.
#' se.chk.nor <- norm_data(data=se.chk, norm.fun='CNF', log2.trans=TRUE)
#'
#' # Aggregate replicates of "spatialFeature_variable", where spatial features are organs
#' # and variables are ages.
#' se.chk.aggr <- aggr_rep(data=se.chk.nor, sam.factor='organism_part', con.factor='age',
#' aggr='mean')
#' assay(se.chk.aggr)[1:3, 1:3]
#'
#' # Genes with experssion values >= 5 in at least 1% of all samples (pOA), and coefficient
#' # of variance (CV) between 0.2 and 100 are retained.
#' se.chk.fil <- filter_data(data=se.chk.aggr, sam.factor='organism_part', con.factor='age', 
#' pOA=c(0.01, 5), CV=c(0.2, 100), file=NULL)
#' 
#' # The chicken aSVG downloaded from the EBI aSVG repository (https://github.com/ebi-gene-
#' # expression-group/anatomogram/tree/master/src/svg) is included in this package and 
#' # accessed as below.
#' svg.chk <- system.file("extdata/shinyApp/data", "gallus_gallus.svg",
#' package="spatialHeatmap")
#' # Read the chicken aSVG file.
#' svg.chk <- read_svg(svg.path=svg.chk)
#'
#' # Store assay data and aSVG in an "SHM" class.
#' dat.chk <- SPHM(svg=svg.chk, bulk=se.chk.fil)
#' # Plot spatial heatmaps with gene "ENSGALG00000019846".
#' shm(data=dat.chk, ID='ENSGALG00000019846', legend.r=1.9,
#' legend.nrow=5, sub.title.size=7, ncol=3)
#'
#' # Save SHMs as HTML and video files in the "~/test" directory. 
#' \donttest{
#' if (!dir.exists('~/test')) dir.create('~/test')
#' shm(data=dat.chk, ID='ENSGALG00000019846', legend.r=1.9,
#' legend.nrow=5, sub.title.size=7, ncol=3, out.dir='~/test')
#' }
#'

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