adj_mod: Computing adjacency matrix and identifying network modules

In jianhaizhang/spatialHeatmap: spatialHeatmap

Computing adjacency matrix and identifying network modules

Description

This function is designed to identify network modules in a data matrix, e.g. returned by submatrix, which builds on the WGCNA algorithm (Langfelder and Horvath 2008; Ravasz et al. 2002). Each module consists of i.e. groups of biomolecules showing most similar coexpression profiles.

Usage

adj_mod(
  data,
  assay.na = NULL,
  type = "signed",
  power = if (type == "distance") 1 else 6,
  arg.adj = list(),
  TOMType = "unsigned",
  arg.tom = list(),
  method = "complete",
  ds = 0:3,
  minSize = 15,
  arg.cut = list(),
  dir = NULL
)

Arguments

data	The subsetted data matrix returned by the function submatrix.
assay.na	Applicable when data is 'SummarizedExperiment' or 'SingleCellExperiment', where multiple assays could be stored. The name of target assay to use.
type	The network type, one of 'unsigned', 'signed', 'signed hybrid', or 'distance'. Correlations and distances are transformed as follows: for type="unsigned", adjacency=|cor|^power; for type="signed", adjacency=(0.5 * (1+cor) )^power; for type="signed hybrid", if cor>0 adjacency=cor^power, otherwise adjacency=0; and for type="distance", adjacency=(1-(dist/max(dist))^2)^power. Refer to WGCNA (Langfelder and Horvath 2008) for more details.
power	A numeric of soft thresholding power for generating the adjacency matrix. The default is 1 for type=='distance' and 6 for other network types.
arg.adj	A list of additional arguments passed to adjacency, e.g. list(corFnc='cor'). The default is an empty list list().
TOMType	one of "none", "unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2" and "signed Nowick 2". If "none", adjacency will be used for clustering. See TOMsimilarityFromExpr for details.
arg.tom	A list of additional arguments passed to TOMsimilarity, e.g. list(verbose=1). The default is an empty list list().
method	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid".
ds	One of 0, 1, 2, or 3. The strigency of module identification. The smaller, the less modules with larger sizes.
minSize	The expected minimum module size. The default is 15. Refer to WGCNA for more details.
arg.cut	A list of additional arguments passed to cutreeHybrid, e.g. list(verbose=2). The default is an empty list list().
dir	The directory to save the results, where the adjacency matrix and module assignments are saved as TSV-format files "adj.txt" and "mod.txt" respectively.

Value

A list containing the adjacency matrix and module assignments.

Details

To identify modules, first a similarity matrix including similarities between any pair of biomolecules is computed using distance or correlation-based similarity metrics. Second, the similarity matrix is transformed into an adjacency matrix. Third, the adjacency matrix is used to calculate a topological overlap matrix (TOM) where shared neighborhood information among biomolecules is used to preserve robust connections, while removing spurious connections. Fourth, the distance transformed TOM is used for hierarchical clustering with the "flashClust" package (Langfelder and Horvath 2012). Fifth, network modules are identified with the "dynamicTreeCut" package (Langfelder, Zhang, and Steve Horvath 2016). Its ds (deepSplit) argument can be assigned integer values from 0 to 3, where higher values increase the stringency of the module identification process. Since this is a coexpression analysis, total samples should be at least 5. Otherwise, identified modules are not reliable.

These procedures are wrapped in adj_mod for convenience. The result is a list containing the adjacency matrix and the final module assignments stored in a data.frame. Since the interactive network feature (see network) used in the downstream visualization performs best on smaller modules, only modules obtained with stringent ds settings (here ds=2 and ds=3) are returned.

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 Ravasz, E, A L Somera, D A Mongru, Z N Oltvai, and A L Barabási. 2002. “Hierarchical Organization of Modularity in Metabolic Networks.” Science 297 (5586): 1551–5. Dragulescu A, Arendt C (2020). _xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files_.

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 July 31, 2024, 2:59 a.m.