In Fjeanneret/multiSight: Multi-omics Classification, Functional Enrichment and Network Inference analysis

library(DT)
library(dplyr)
library(kableExtra)
library(knitr)
library(networkD3)
library(htmlwidgets)

Model performances

featDiablo <- getClassif(obj, method= "diablo", result = "featDetails")
if (length(featDiablo) > 0 )
{ 

    cat("### Diablo model \n")

    cat("Diablo **sPLS-DA** method selects a relevant features subset to 
        classification performance and correlation values. These features
        could be used in functional enrichment analysis to further analysis 
        provide by *MONIA*. \n")
    cat("\n#### Features selected for each omic dataset \n ")

    i = 1
    while (i <= length(featDiablo))
    {
        df <- featDiablo[[i]][[1]]$data
        if (!is.null(df$padjdf)) df <- df %>% arrange(padj)
        df[, 2:7] <- format(df[, 2:7], digits = 3)
        caption <- paste0("Omic", i, ": Features selected by Diablo - sPLS-DA 
                          method")
        tbl <- kbl(df, format = "simple", caption = caption)
        print(tbl)

        i = i + 1
    }
    cat("\n#### sPLS-DA performances \n ")
}else
{
    cat("### Diablo model not computed \n")
}

perfDiablo <- getClassif(obj, method = "diablo", result = "performance")
if (length(perfDiablo) > 0 )
{
  print(perfDiablo)
}

# featBiosigner <- obj$classification$biosignerResult$featDetails
featBiosigner <- getClassif(obj, method= "biosigner", result = "featDetails")

if (length(featBiosigner) > 0 )
{ 

    cat("### Biosigner model \n")

    cat("Biosigner method selects a small statistical significative features
    subset to classification performance. 
\n")
    cat("#### Features selected for each omic dataset \n ")

    i <- 1
    while (i <= length(featBiosigner))
    {
        df <- featBiosigner[[i]][[1]]$data
        if (!is.null(df$padjdf)) df <- df %>% arrange(padj)
        df[, 2:7] <- format(df[, 2:7], digits = 3)
        caption <- paste0("Omic", i, ": Features selected by Biosigner 
        (SVM & RF) method")
        tbl <- kbl(df, format = "simple", caption = caption)
        print(tbl)

        i <- i + 1
    }
    cat("\n#### Biosigner performances \n ")
}else
{
    cat("### Biosigner model not computed \n")
}

# perfBiosigner <- obj$classification$biosignerResult$performance
perfBiosigner <- getClassif(obj, method = "biosigner", result = "performance")

if (length(perfBiosigner) > 0 )
{
    print(perfBiosigner)
}

Enrichment analysis

Functional enrichment analysis have been computed with Diablo subsets of features. You could retrieve "single-omic" results and "multi-omic" below. A Stouffer's p-value is a p-values pooling result weighted or not by number of features used to identify at least one biological elements for each omic data.

# dataNames <- names(obj$enrichment$diablo$featureConverted)
convFeat <- getEnrichment(obj, sourceFeat = "diablo", 
                          infoType = "featureConverted")
dataNames <- names(convFeat)

enrichResPathways <- getEnrichment(obj, sourceFeat = "diablo", 
                          infoType = "pathways")
enrichResGO <- getEnrichment(obj, sourceFeat = "diablo", 
                          infoType = "go")
# enrichResPathways <- obj$enrichment$diablo$pathways
# enrichResGO <- obj$enrichment$diablo$go

Pathways

if (length(enrichResPathways) > 0)
{
    i <- 1
    while(i <= length(enrichResPathways))
    {
        db <- enrichResPathways[[i]]$result
        j <- 1
        cat(paste0("\n <br><div style = 'display: inline-block;
        vertical-align:middle; width: 100%; text-align: center;'><h4>",  
        names(enrichResPathways)[i], "</h4></div> \n"))

                   # "</h1> \n"))
        # cat("\n \\subsection{", names(enrichResPathways)[i], "} \n")
        while (j <= length(db))
        {
            cat("\n \\subsubsection{Enrichment results for Omic", j, "} \n")
            if (j <= length(dataNames)) # Single omic enrichment table
            {
                ## load a small part of table
                t <- db[[j]][seq(1, 10),  seq(1, 7)]
                t$ID <- addEnrichIDUrl(t$ID, names(enrichResPathways)[i])

                table <- t %>% 
                  kbl(digits = 4, row.names = FALSE, format = "simple", 
                    caption = paste0(names(enrichResPathways)[i], 
                                     " - Enrichment result table for Omic", 
                                     j))

                print(table)
            }
            else if (j == length(dataNames) + 1 ) ## Stouffer table
            {
                cat("\n \\subsubsection{Multi-Omic 
                    enrichment analysis with Stouffer's p-value}\n")

                ## load a small part of table
                # t <- db[[j]][c(seq(1, 6), seq(8, 10)), ]
                t <- db[[j]][seq(1, 10), ] %>% select(everything(), -geneID)
                t$ID <- addEnrichIDUrl(t$ID, names(enrichResPathways)[i]) 

                table <- t %>% 
                    kbl(digits = 4, 
                        row.names = FALSE, 
                        format = "simple", 
                        caption = paste0(names(enrichResPathways)[i], 
                                         " - Multi-omic enrichment table"))
                print(table)
            }
            else if (j == length(dataNames) + 2) ## enrichment Map
            {
              cat("\n \\subsubsection{Multi-Omic 
                    enrichment map with Stouffer's p-value <= 0.1}\n")
              print(db[[j]])
            }
            j = j + 1
        }
        i = i + 1
    }
}else{
  cat("No pathways enrichment result \n")
}

Gene Ontology

if (length(enrichResGO) > 0)
{
    i = 1
    while(i <= length(enrichResGO))
    {
        db <- enrichResGO[[i]]
        j = 1
        cat(paste0("\n <br><div style = 'display: inline-block;
        vertical-align:middle; width: 100%; text-align: center;'><h4>",  
        names(enrichResGO)[i], "</h4></div> \n"))
        # cat("\n \\subsection{", names(enrichResGO)[i], "}\n \\ ")
        while (j <= length(db))
        {
            cat("\n \\subsubsection{Enrichment results for Omic", j, "}\n")
            if (j <= length(dataNames)) # Single omic enrichment table
            {
                ## load a small part of table
                t <- db[[j]][seq(1, 10), seq(1, 7)]
                t$ID <- addEnrichIDUrl(t$ID, names(enrichResGO)[i]) 

                ## displays table
                table <- t %>%
                kbl(digits = 4, 
                    row.names = FALSE, 
                    format = "simple",
                    caption = paste0(names(enrichResGO)[i], 
                                     " - Enrichment result table for Omic", 
                                     j))
                print(table)
            }
            else if (j == length(dataNames) + 1 ) ## Stouffer table
            {
                cat("\n \\subsubsection{Multi-Omic 
                    enrichment analysis with Stouffer's p-value}\n")

                ## load a small part of table
                t <- db[[j]][seq(1, 10), ] %>% select(everything(), -geneID)
                t$ID <- addEnrichIDUrl(t$ID, names(enrichResGO)[i]) 

                table <- t %>% kbl(digits = 4, 
                        row.names = FALSE, 
                        format = "simple", 
                        caption = paste0(names(enrichResGO)[i],
                                         " - Multi-omic enrichment table"))
                print(table)
            }
            else if (j == length(dataNames) + 2) ## enrichment Map
            {
              cat("\n \\subsubsection{Multi-Omic 
                    enrichment map with Stouffer's p-value <= 0.1}\n")
              print(db[[j]])
            }
            j = j + 1
        }
        i = i + 1
    }
}else{
  cat("No ontology enrichment result \n")
}

Multi-Omic Network inference

Several network analysis methods are used by MONIA. You can retrieve correlation, partial correlation and mutual information networks whose relevancy to improve relevant relation identification between numerical values have been shown in Hawe, Johann S. and Theis, Fabian J. and Heinig, Matthias, 2019.

Diablo features networks

Correlation network

if (length(getNetwork(obj, "diablo")) > 0)
{
    # corrNet <- obj$networkInference$diabloNetwork$graph$correlation
    corrNet <- getNetwork(obj, "diablo", "correlation")
    if (!is.null(corrNet))
    {
        onRender(corrNet,
          "function(el,x)
              { d3.selectAll('.node').on('mouseover', null); }")
    }else
    {
        cat("<i>Network picture not loaded in app</i>")
    }
}else{
    cat("<i>No diablo networks loaded or computed</i> \n")
}

Partial correlation network

if (length(getNetwork(obj, "diablo")) > 0)
{
    # pcorrNet <- obj$networkInference$diabloNetwork$graph$partialCorrelation
    pcorrNet <- getNetwork(obj, "diablo", "partialCor")
    if (!is.null(pcorrNet))
    {
        onRender(pcorrNet,
            "function(el,x)
                { d3.selectAll('.node').on('mouseover', null); }")
    }else{
        cat("<i>Network picture not loaded in app</i>")
    }
}else
{
    cat("<i>No diablo networks loaded or computed</i> \n")
}

Mutual information network

if (length(getNetwork(obj, "diablo")) > 0)
{
    # imNet <- obj$networkInference$diabloNetwork$graph$mutualInformation
    imNet <- getNetwork(obj, "diablo", "mutualInf")
    if (!is.null(imNet))
    {
        onRender(imNet,
            "function(el,x)
                { d3.selectAll('.node').on('mouseover', null); }")
    }else{
        cat("<i>Network picture not loaded in app</i>")
    }
}else{
    cat("<i>No diablo networks loaded or computed</i> \n")
}
cat("\n <hr style='height: 50px; width: 100% ;
    color: #f1f1f1; background-color: #f1f1f1;'> \n")

Biosigner features networks

Correlation network

if (length(getNetwork(obj, "biosigner")) > 0)
{
  # corrNet <- obj$networkInference$biosignerNetwork$graph$correlation
  corrNet <- getNetwork(obj, "biosigner", "correlation")
  if (!is.null(corrNet))
  {
    onRender(corrNet,
        "function(el,x)
            { d3.selectAll('.node').on('mouseover', null); }")
  }else{
      cat("<i>Network picture not loaded in app</i>")
  }
}else{
  cat ("<i>No biosigner networks loaded or computed</i> \n")
}

Partial correlation network

if (length(getNetwork(obj, "biosigner")) > 0)
{
  # pcorrNet <- obj$networkInference$biosignerNetwork$graph$partialCorrelation
  pcorrNet <- getNetwork(obj, "biosigner", "partialCor")
  if (!is.null(pcorrNet))
  {
      onRender(pcorrNet,
      "function(el,x)
          { d3.selectAll('.node').on('mouseover', null); }")
  }else{
      cat("<i>Network picture not loaded in app</i>")
  }
}else{
    cat("<i>No biosigner networks loaded or computed</i> \n")
}

Mutual information network

if (length(getNetwork(obj, "biosigner")) > 0)
{
    # miNet <- obj$networkInference$biosignerNetwork$graph$mutualInformation
    miNet <- getNetwork(obj, "biosigner", "mutualInf")
    if (!is.null(miNet))
    {
        onRender(miNet,
        "function(el,x)
            { d3.selectAll('.node').on('mouseover', null); }")
    }else{
        cat("<i>Network picture not loaded in app</i>")
    }
}else{
    cat("<i>No biosigner networks loaded or computed</i> \n")
}
cat("\n <hr style='height: 50px; width: 100% ;
    color: #f1f1f1; background-color: #f1f1f1;'> \n")

Bibliographic assumptions

if (length(obj$networkInference$diabloNetwork$biblio) > 0)
{
  cat("<h3>Diablo features | Bibliographic query: \n</h3>", 
      getPubMed(obj, "diablo", "request")
  )
  titles_urls <- getPubMed(obj, "diablo", "all")
  i <- 1
  while (i <=  length(titles_urls$title))
  {
    title <- titles_urls$title[[i]]
    url <- titles_urls$url[[i]]
    cat("<li><a href = ", url, "> ", title, "</a></li>\n")
    i <- i + 1
  }
}

if (length(obj$networkInference$biosignerNetwork$biblio) > 0)
{
    cat("<h3>Biosigner features | Bibliographic query:</h3>", 
        getPubMed(obj, "biosigner", "request"))
    titles_urls <- getPubMed(obj, "biosigner", "all")
    i <- 1
    while (i <=  length(titles_urls$title))
    {
        title <- titles_urls$title[[i]]
        url <- titles_urls$url[[i]]
        cat("<li><a href = ", url, "> ", title, "</a></li>")
        i <- i + 1
    }
}
cat("\n <hr style='height: 50px; width: 100% ;
    color: #f1f1f1; background-color: #f1f1f1;'> \n")

Fjeanneret/multiSight documentation built on April 6, 2022, 7:59 a.m.