database: Creat databases for the Shiny App

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

Creat databases for the Shiny App

Description

The function write_hdf5 is designed to construct the a backend database for the Shiny App (shiny_shm), while read_hdf5 is designed to query the database.

Usage

write_hdf5(data, dir = "./data_shm", replace = FALSE)

read_hdf5(file, name = NULL, dir)

Arguments

data	A nested ‘list' of each numeric data and aSVG pair. Each pair consists of four slots: 'name', 'display', 'data', and 'svg', e.g. 'lis1 <- list(name=’mus.brain', display='Mouse brain (SHM)', data='./mus_brain.txt', svg='./mus_brain.svg')'. The 'name' contains a syntactically valid entry for R while the 'display' contains an entry for display on the user interface. The 'data' and 'svg' contain paths of numeric data and aSVGs that will be included in the data base respectively. The supported data containers include 'data.frame' and 'SummarizedExperiment'. See the 'data' argument in filter_data for data formats. After formatted, the data should be saved as ".rds" files with the function saveRDS, then assign the corresponding paths the 'data' slot. By contrast, the 'svg' slot contains one or multiple aSVG/raster image file paths. Each numeric data set is first saved in an independent DHF5-based 'SummarizedExperiment' (SE) object, then all these saved SE objects and aSVG/raster images are compressed together into a file "data_shm.tar", i.e. the backend database. In addition, the nested 'list' assigned to 'data' is also included in the database for querying purpose (read_hdf5).
dir	The directory for saving assay data and aSVGs in query.
replace	If 'TRUE', the existing database with the same name in 'dir' will be replaced.
file	The path of a backend database of Shiny App, which is the file of 'data_shm.tar' generated by write_hdf5.
name	One or multiple data set names (see the 'data' argument in write_hdf5) in a vector, such as c('test_leaf', 'test_chicken'). If 'match', the matching 'list' between assay data and aSVGs will be returned.

Value

‘write_hdf5': a file ’data_shm.tar' saved on disk.
'read_hdf5': a nested 'list' of file paths corresponding to data-aSVG pairs.

Author(s)

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

References

SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1
Pagès H, Lawrence M, Aboyoun P (2023). _S4Vectors: Foundation of vector-like and list-like containers in Bioconductor_. doi:10.18129/B9.bioc.S4Vectors <https://doi.org/10.18129/B9.bioc.S4Vectors>, R package version 0.38.1, <https://bioconductor.org/packages/S4Vectors>.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Fischer B, Smith M, Pau G (2023). rhdf5: R Interface to HDF5. doi:10.18129/B9.bioc.rhdf5, R package version 2.46.0, https://bioconductor.org/packages/rhdf5
Mustroph, Angelika, M Eugenia Zanetti, Charles J H Jang, Hans E Holtan, Peter P Repetti, David W Galbraith, Thomas Girke, and Julia Bailey-Serres. 2009. “Profiling Translatomes of Discrete Cell Populations Resolves Altered Cellular Priorities During Hypoxia in Arabidopsis.” Proc Natl Acad Sci U S A 106 (44): 18843–8
Davis, Sean, and Paul Meltzer. 2007. “GEOquery: A Bridge Between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics 14: 1846–7
Gautier, Laurent, Leslie Cope, Benjamin M. Bolstad, and Rafael A. Irizarry. 2004. “Affy—analysis of Affymetrix GeneChip Data at the Probe Level.” Bioinformatics 20 (3). Oxford, UK: Oxford University Press: 307–15. doi:10.1093/bioinformatics/btg405
Keays, Maria. 2019. ExpressionAtlas: Download Datasets from EMBL-EBI Expression Atlas
Huber, W., V. J. Carey, R. Gentleman, S. An ders, M. Carlson, B. S. Carvalho, H. C. Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis Wit H Bioconductor.” Nature Methods 12 (2): 115–21. http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3252.html
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. "Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2." Genome Biology 15 (12): 550. doi:10.1186/s13059-014-0550-8
McCarthy, Davis J., Chen, Yunshun, Smyth, and Gordon K. 2012. "Differential Expression Analysis of Multifactor RNA-Seq Experiments with Respect to Biological Variation." Nucleic Acids Research 40 (10): 4288–97
Cardoso-Moreira, Margarida, Jean Halbert, Delphine Valloton, Britta Velten, Chunyan Chen, Yi Shao, Angélica Liechti, et al. 2019. “Gene Expression Across Mammalian Organ Development.” Nature 571 (7766): 505–9

Examples

## Example of a single aSVG instance.

# 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)

# Indicate spatial features and experiment variables with "spFeature" and "variable"
# in the "colData" slot respectively.
colnames(colData(se.chk))[8] <- 'spFeature'
colnames(colData(se.chk))[6] <- 'variable'
colData(se.chk)[1:2, ]

# Create a temporary directory "db_shm".
dir.db <- file.path(tempdir(check=TRUE), 'db_shm')
if (!dir.exists(dir.db)) dir.create(dir.db)

# Save the assay data in the temporary directory.
chk.dat.pa <- file.path(dir.db, 'test_chicken.rds')
saveRDS(se.chk, file=chk.dat.pa)

# 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")
# Store file paths of data and aSVG pair in a list. 
dat1 <- list(name='test_chicken', display='Test (chicken SHM)', data=chk.dat.pa, svg=svg.chk)

## Example of multiple aSVG and raster images.

# Two aSVG instances of maize leaves represent two growth stages and each aSVG has a raster 
# image counterpart.
# Random numeric data.
pa.leaf <- system.file("extdata/shinyApp/data", 'dat_overlay.txt',
package="spatialHeatmap")
dat.leaf <- read_fr(pa.leaf); dat.leaf[1:2, ]
# Save the assay data in the temporary directory.
leaf.dat.pa <- file.path(dir.db, 'test_leaf.rds')
saveRDS(dat.leaf, file=leaf.dat.pa)

# Paths of the two aSVG files.
svg.leaf1 <- system.file("extdata/shinyApp/data", 'maize_leaf_shm1.svg',
package="spatialHeatmap")
svg.leaf2 <- system.file("extdata/shinyApp/data", 'maize_leaf_shm2.svg',
package="spatialHeatmap") 
# Paths of the two corresponsing raster images.
rst.leaf1 <- system.file("extdata/shinyApp/data", 'maize_leaf_shm1.png',
package="spatialHeatmap")
rst.leaf2 <- system.file("extdata/shinyApp/data", 'maize_leaf_shm2.png',
package="spatialHeatmap") 

# Store file paths of data, aSVG, raster images in a list. 
dat2 <- list(name='test_leaf', display='Test (maize leaf SHM)', data=leaf.dat.pa, 
svg=c(svg.leaf1, svg.leaf2, rst.leaf1, rst.leaf2))

# Store these two data sets in a nested list. 
dat.lis <- list(dat1, dat2) 


# Save the database in the temporary directory. 
write_hdf5(data=dat.lis, dir=dir.db, replace=TRUE)
# Read data-aSVG pair of "test_leaf" from "data_shm.tar".
dat.q <- read_hdf5(file=file.path(dir.db, 'data_shm.tar'), name='test_leaf')
# Numeric data.
dat.leaf <- readRDS(dat.q[[1]]$data)
# aSVG.
svg1 <- dat.q[[1]]$svg[1]; svg2 <- dat.q[[1]]$svg[2]
rst1 <- dat.q[[1]]$svg[3]; rst2 <- dat.q[[1]]$svg[4]
svg.leaf <- read_svg(svg=c(svg1, svg2), raster=c(rst1, rst2))


  
# Create customized Shiny Apps with the database.
custom_shiny(db=file.path(dir.db, 'data_shm.tar'), app.dir='~/test_shiny')
# Create customized Shiny Apps with the data sets in a nested list.
custom_shiny(data=dat.lis, app.dir='~/test_shiny')
# Run the app.
shiny::runApp('~/test_shiny/shinyApp') 

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