# Multi-Tissue Analysis

In this vignette, we will analyze a gene expression dataset with samples from multiple tissues. We will: download a public dataset identify the genes expressed in two tissues run enrichment analysis, cognizant of each tissues’ expression profile visualize network-based relationships between the tissues’ expression profiles

# Enrichment to Identify Tissue-Specific Patterns

We will use data from BgeeDB normal-tissue expression. In research, we will typically want to compare normal to one or more treatment or disease groups. Thus, consider this as an illustrative example.

    # Load RITAN
    library(RITANdata)
    library(RITAN)

    # Install the Bgee package. GO.db is a dependency of a dependency and may need to be installed seperately.
    for (pkg in c('GO.db','BgeeDB','biomaRt')){
      if (! (pkg %in% rownames(installed.packages()) )){
        if (!requireNamespace("BiocManager", quietly=TRUE))
            install.packages("BiocManager")
        BiocManager::install(pkg)
      }
      library(pkg, character.only = TRUE)
    }

    for (pkg in c('tidyselect','venn','magrittr','ggplot2','igraph')){
      if (! (pkg %in% rownames(installed.packages()) )){
        install.packages(pkg)
      }
      library(pkg, character.only = TRUE)
    }

    # Setup Bgee query & get data (this may take some time)
    bgee <- Bgee$new(species = "Homo_sapiens", dataType = "rna_seq", release = "13.2")
    data <- getData(bgee)
    e    <- formatData(bgee, data[[1]], callType = "present", stats = "rpkm")

    # Explore the dataset with: str(sampleNames(e)), str(featureNames(e)), str(phenoData(e))
    table(phenoData(e)@data$Anatomical.entity.name)

    ## -------------------- -
    ## Get expression in two tissues
    tmp <- exprs(e)[ , phenoData(e)@data$Anatomical.entity.name == "heart" ]
    i   <- apply( tmp, 1, function(x){ any(is.na(x)) })
    expr_heart <- tmp[ !i, ]

    tmp <- exprs(e)[ , phenoData(e)@data$Anatomical.entity.name == "skeletal muscle tissue" ]
    i   <- apply( tmp, 1, function(x){ any(is.na(x)) })
    expr_skele <- tmp[ !i, ]

    venn::venn( list(Heart = rownames(expr_heart),
                     Skeletal = rownames(expr_skele) ),
                cexil= 1, cexsn = 1, zcolor = "style" )

    ## -------------------- -
    ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ensembl", "http://Aug2017.archive.ensembl.org" ) # version 90

    map_heart <- getBM( attributes = c('ensembl_gene_id','ensembl_transcript_id','hgnc_symbol'),
                        filters = 'ensembl_gene_id', values = rownames(expr_heart), mart = ensembl )

    map_skele <- getBM( attributes = c('ensembl_gene_id','ensembl_transcript_id','hgnc_symbol'),
                        filters = 'ensembl_gene_id', values = rownames(expr_skele), mart = ensembl )

    ## -------------------- -
    ## Functions associated with each tissue's top genes
    ## Important: the p-values reported here are observational, not inferential.

    mh <- apply( expr_heart, 1, mean )
    top_heart <- map_heart$hgnc_symbol[ map_heart$ensembl_gene_id %in% rownames( expr_heart )[ mh > quantile(mh, .975) ] ] %>% setdiff(.,'')

    ms <- apply( expr_skele, 1, mean )
    top_skele <- map_skele$hgnc_symbol[ map_skele$ensembl_gene_id %in% rownames( expr_skele )[ ms > quantile(ms, .975) ] ] %>% setdiff(.,'')

    e <- term_enrichment_by_subset( list( Heart    = top_heart,
                                          Skeletal = top_skele ),
                                    resources = 'GO_slim_PIR', all_symbols = cached_coding_genes )

    plot( e[ apply(e[, c(3:4)], 1, max) >= 12, ], cap=40, label_size_y = 8, wrap_y_labels = FALSE )

    ## -------------------- -
    ## Network Interactions Within Each Tissue

    net_h <- network_overlap( top_heart, resources = c('CCSB','dPPI','HumanNet') )
    net_s <- network_overlap( top_skele, resources = c('CCSB','dPPI','HumanNet') )

    net2g <- function(x){
      edges <- as.matrix( x[, c(1,3)] )
      G <- igraph::make_undirected_graph( c(t(edges)) )
      return(G)
    }

    g_h <- net2g( net_h )
    g_s <- net2g( net_s )

    g_dif <- igraph::difference( g_h, g_s )
    g_int <- igraph::intersection( g_h, g_s )

    cat(sprintf('
    Of the top expressed genes, %d are shared and %d differ.
    ', length(V(g_int)), length(V(g_dif)) ))

    par(mar=rep(0,4))
    plot(g_dif, vertex.size = 2, vertex.label = NA, vertex.frame.color = 'white', layout = layout_nicely )