submatrix: Subsetting data matrix

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

Subsetting data matrix

Description

Given one or multiple biomolecules (gene, protein, metabolite, etc) from a data matrix, this function subsets other biomolecules that have similar expression profiles with each of the given biomolecule independently. The subset data matrix of each biomolecule is combined in a row-wise manner and returned.

Usage

submatrix(
  data,
  assay.na = NULL,
  ID,
  p = 0.3,
  n = NULL,
  v = NULL,
  fun = "cor",
  cor.absolute = FALSE,
  arg.cor = list(method = "pearson"),
  arg.dist = list(method = "euclidean"),
  file = NULL
)

Arguments

data	A 'data.frame', 'SummarizedExperiment', or 'SingleCellExperiment' object, where the columns and rows of the data matrix are samples and biomolecules respectively. Since this function builds on co-expression analysis, samples should be at least 5, otherwise, the results are not reliable.
assay.na	Applicable when data is 'SummarizedExperiment' or 'SingleCellExperiment', where multiple assays could be stored. The name of assay to use.
ID	A vector of biomolecules of interest.
p	The proportion of top biomolecules with most similar expression profiles with a given biomolecule. Only biomolecules within this proportion are returned. It applies to each biomolecule independently and selected biomolecules of each given biomolecule are returned together.
n	An integer of top biomolecules with most similar expression profiles with a given biomolecule. Only biomolecules within this number are returned. It applies to each biomolecule independently and selected biomolecules of each given biomolecule are returned together.
v	A cutoff of correlation coefficient (CC, -1 to 1) or distance (>=0) for subsetting biomolecules sharing the most similar expression profiles with a given biomolecule. If fun='cor', only biomolecules with CC larger than v are returned. If fun='dist', only biomolecules with distance less than v are returned. It applies to each given biomolecule independently and selected biomolecules of each given biomolecule are returned together.
fun	The function to calculate similarity/distance measures, 'cor' (default) or 'dist', corresponding to cor or dist from the "stats" package respectively.
cor.absolute	Logical. If 'TRUE', absolute correlation coefficients (CCs) are used. Only applies to fun='cor'. Default is 'FALSE', meaning the CCs preserve the negative sign when subsetting biomolecules.
arg.cor	A list of arguments passed to cor in the "stats" package. Default is list(method="pearson").
arg.dist	A list of arguments passed to dist in the "stats" package. Default is list(method="euclidean").
file	The file name to save subset data matrix.

Value

The subset data matrix in form of 'data.frame', 'SummarizedExperiment', or 'SingleCellExperiment'.

Author(s)

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

References

Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559 Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/ 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/ Peter Langfelder, Bin Zhang and with contributions from Steve Horvath (2016). dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms. R package version 1.63-1. https://CRAN.R-project.org/package=dynamicTreeCut Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1 Keays, Maria. 2019. ExpressionAtlas: Download Datasets from EMBL-EBI Expression Atlas 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 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

## 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)

## Subset the data matrix for gene 'ENSGALG00000019846' and 'ENSGALG00000000112'.
se.sub.mat <- submatrix(data=se.chk.fil, ID=c('ENSGALG00000019846', 
'ENSGALG00000000112'), p=0.1) 

## Hierarchical clustering. 
library(dendextend)
# Static matrix heatmap.
mhm.res <- matrix_hm(ID=c('ENSGALG00000019846', 'ENSGALG00000000112'), data=se.sub.mat, 
angleCol=80, angleRow=35, cexRow=0.8, cexCol=0.8, margin=c(8, 10), static=TRUE, 
arg.lis1=list(offsetRow=0.01, offsetCol=0.01))
# Clusters containing "ENSGALG00000019846".
cut_dendro(mhm.res$rowDendrogram, h=15, 'ENSGALG00000019846')

# Interactive matrix heatmap.
 matrix_hm(ID=c('ENSGALG00000019846', 'ENSGALG00000000112'), data=se.sub.mat, 
angleCol=80, angleRow=35, cexRow=0.8, cexCol=0.8, margin=c(8, 10), static=FALSE, 
arg.lis1=list(offsetRow=0.01, offsetCol=0.01)) 


# In case the interactive heatmap is not automatically opened, run the following code snippet.
# It saves the heatmap as an HTML file that is assigned to the "file" argument.

mhm <- matrix_hm(ID=c('ENSGALG00000019846', 'ENSGALG00000000112'), data=se.sub.mat, 
angleCol=80, angleRow=35, cexRow=0.8, cexCol=0.8, margin=c(8, 10), static=FALSE, 
arg.lis1=list(offsetRow=0.01, offsetCol=0.01))
htmlwidgets::saveWidget(widget=mhm, file='mhm.html', selfcontained=FALSE)
browseURL('mhm.html')


## Adjacency matrix and module identification 
adj.mod <- adj_mod(data=se.sub.mat)

# The adjacency is a measure of co-expression similarity between genes, where larger
# value denotes higher similarity.
adj.mod[['adj']][1:3, 1:3]

# The modules are identified at four sensitivity levels (ds=0, 1, 2, or 3). From 0 to 3, 
# more modules are identified but module sizes are smaller. The 4 sets of module 
# assignments are returned in a data frame, where column names are sensitivity levels. 
# The numbers in each column are module labels, where "0" means genes not assigned to 
# any module.
adj.mod[['mod']][1:3, ]

# Static network graph. Nodes are genes and edges are adjacencies between genes. 
# The thicker edge denotes higher adjacency (co-expression similarity) while larger node
# indicates higher gene connectivity (sum of a gene's adjacencies with all its direct 
# neighbors). The target gene is labeled by "_target".
network(ID="ENSGALG00000019846", data=se.sub.mat, adj.mod=adj.mod, adj.min=0, 
vertex.label.cex=1.5, vertex.cex=4, static=TRUE)

# Interactive network. The target gene ID is appended "_target".  
 network(ID="ENSGALG00000019846", data=se.sub.mat, adj.mod=adj.mod, static=FALSE) 

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