R: Identify Spatial Feature-Specifcally Expressed Genes

spatial_enrich {spatialHeatmap}

R Documentation

Identify Spatial Feature-Specifcally Expressed Genes

Description

This functionality is an extension of the spatial heatmap. It identifies spatial feature-specifically expressed genes and thus enables the spatial heatmap to visualize feature-specific profiles. The spatial features include cellular compartments, tissues, organs, etc. The function compares the target feature with all other selected features in a pairwise manner. The genes significantly up- or down-regulated in the target feature across all pairwise comparisons are denoted final target feature-specifcally expressed genes. The underlying methods include edgeR (Robinson et al, 2010), limma (Ritchie et al, 2015), DESeq2 (Love et al, 2014), distinct (Tiberi et al, 2020). The feature-specific genes are first detected with each method and can be summarized across methods.
In addition to feature-specific genes, this function is also able to identify genes specifically expressed in certain condition or in composite factor. The latter is a combination of multiple expermental factors. E.g. the spatiotemporal factor is a combination of feature and time points.

Usage

spatial_enrich(
  data,
  methods = c("edgeR", "limma"),
  norm = "TMM",
  log2.trans.dis = TRUE,
  log2.fc = 1,
  p.adjust = "BH",
  fdr = 0.05,
  aggr = "mean",
  log2.trans.aggr = TRUE
)

Arguments

`data`	A `SummarizedExperiment` object, which is returned by `sub_data`. The `colData` slot is required to contain at least two columns of "features" and "factors" respectively. The `rowData` slot can optionally contain a column of discriptions of each gene and the column name should be `metadata`.
`methods`	One or more of `edgeR`, `limma`, `DESeq2`, `distinct`. The default is `c('edgeR', 'limma')`.
`norm`	The normalization method (`TMM`, `RLE`, `upperquartile`, `none`) in edgeR. The default is `TMM`. Details: https://www.rdocumentation.org/packages/edgeR/versions/3.14.0/topics/calcNormFactors
`log2.trans.dis`	Logical, only applicable when `distinct` is in `methods`. The default is `TRUE`, and the count data is transformed to log-2 scale.
`log2.fc`	The log2-fold change cutoff. The default is 1.
`p.adjust`	The method (`holm`, `hochberg`, `hommel`, `bonferroni`, `BH`, `BY`, `fdr`, `none`) to adjust p values in multiple hypothesis testing. The default is `BH`.
`fdr`	The FDR cutoff. The default is 0.05.
`aggr`	One of `mean` (default), `median`. The method to aggregated replicates in the data frame of feature-specific genes.
`log2.trans.aggr`	Logical. If `TRUE` (default), the aggregated data (see `aggr`) is transformed to log2-scale, included in the returned data frame of feature-specific genes, and would be further used in the spatial heatmaps.

Value

A nested list containing the feature-specific genes summarized across methods within methods.

Author(s)

Jianhai Zhang jianhai.zhang@email.ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu

References

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
Keays, Maria. 2019. ExpressionAtlas: Download Datasets from EMBL-EBI Expression Atlas
Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1
Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.
Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)
Simone Tiberi and Mark D. Robinson. (2020). distinct: distinct: a method for differential analyses via hierarchical permutation tests. R package version 1.2.0. https://github.com/SimoneTiberi/distinct

Examples

## In the following examples, the toy data come from an RNA-seq analysis on development of 7
## chicken organs under 9 time points (Cardoso-Moreira et al. 2019). For conveninece, it is
## included in this package. The complete raw count data are downloaded using the R package
## ExpressionAtlas (Keays 2019) with the accession number "E-MTAB-6769".   

library(SummarizedExperiment)

## Set up toy data.

# Access toy data. 
cnt.chk <- system.file('extdata/shinyApp/example/count_chicken.txt', package='spatialHeatmap')
count.chk <- read.table(cnt.chk, header=TRUE, row.names=1, sep='\t')
count.chk[1:3, 1:5]

# A targets file describing samples and conditions is required for toy data. It should be made
# based on the experiment design, which is accessible through the accession number 
# "E-MTAB-6769" in the R package ExpressionAtlas. An example targets file is included in this
# package and accessed below. 
# Access the count table. 
cnt.chk <- system.file('extdata/shinyApp/example/count_chicken.txt', package='spatialHeatmap')
count.chk <- read.table(cnt.chk, header=TRUE, row.names=1, sep='\t')
count.chk[1:3, 1:5]
# Access the example targets file. 
tar.chk <- system.file('extdata/shinyApp/example/target_chicken.txt', package='spatialHeatmap')
target.chk <- read.table(tar.chk, header=TRUE, row.names=1, sep='\t')
# Every column in toy data corresponds with a row in targets file. 
target.chk[1:5, ]
# Store toy data in "SummarizedExperiment".
se.chk <- SummarizedExperiment(assay=count.chk, colData=target.chk)
# The "rowData" slot can store a data frame of gene metadata, but not required. Only the 
# column named "metadata" will be recognized. 
# Pseudo row metadata.
metadata <- paste0('meta', seq_len(nrow(count.chk))); metadata[1:3]
rowData(se.chk) <- DataFrame(metadata=metadata)

# Subset the data by selected features (brain, heart, kidney) and factors (day10, day12).
data.sub <- sub_data(data=se.chk, feature='organism_part', features=c('brain', 'heart', 
'kidney'), factor='age', factors=c('day10', 'day12'), com.by='feature', target='brain')

## As conventions, raw sequencing count data should be normalized and filtered to
## reduce noise. Since normalization will be performed in spatial enrichment, only filtering
## is required.  

# Filter out genes with low counts and low variance. Genes with counts over 5 in
# at least 10% samples (pOA), and coefficient of variance (CV) between 3.5 and 100 are 
# retained.
data.sub.fil <- filter_data(data=data.sub, sam.factor='organism_part', con.factor='age',
pOA=c(0.1, 5), CV=c(0.7, 100), dir=NULL)
# Identify brain-specifically expressed genes relative to heart and kidney, where day10 and
# day12 are treated as replicates. 
deg.lis <- spatial_enrich(data.sub.fil)
# All up- and down-regulated genes in brain across methods. On the right is the data after
# replicates aggregated, and will be used in the spatial heatmaps.
deg.lis$deg.table[1:3, ]
# Up-regulated genes detected by edgeR.
deg.lis$lis.up.down$up.lis$edgeR.up[1:5]
# The aSVG path.
svg.chk <- system.file("extdata/shinyApp/example", "gallus_gallus.svg", 
package="spatialHeatmap")
# Plot one brain-specific gene in spatial heatmap.
spatial_hm(svg.path=svg.chk, data=deg.lis$deg.table, ID=deg.lis$deg.table$gene[1], legend.r=1.9, legend.nrow=2, sub.title.size=7, ncol=2, bar.width=0.11)
# Overlap of up-regulated brain-specific genes across methods.
deg_ovl(deg.lis$lis.up.down, type='up', plot='upset')
deg_ovl(deg.lis$lis.up.down, type='up', plot='matrix')
# Overlap of down-regulated brain-specific genes across methods.
deg_ovl(deg.lis$lis.up.down, type='down', plot='upset')
deg_ovl(deg.lis$lis.up.down, type='down', plot='matrix')
# Line graph of gene expression profile.
profile_gene(deg.lis$deg.table[1, ])

[Package spatialHeatmap version 2.0.0 Index]