In jinhyunju/icreport: Tools for creating a visual summary of matrix decomposition results.

Component Visualization Report

• Generated with R package icreport

# setting global options 
figure.outpath <- paste(output.path,"/icreport_figures/",prefix,"/",sep="")
opts_chunk$set(fig.path=figure.outpath,dev=file.ext, warning=FALSE, message=FALSE)
options(warn=-1,width = 100)
flower.palette <- c("#283545","#E09E12","#A72613","#95CDDC","#395717","#3A3B35","#D68931","#442243")

cat("Independent Component Analysis Report \n")
cat("Dataset = ", prefix,"\n")

Individual Component Information

for( i in 1:dim(ica_result$A)[1]){
    ###################### Plotting without Gene information ########################## 
    # check if there is a specified order for the ICs (default is by explained variance)
    p <- ica_summary_df$idx[i]
    # Running Mclust on IC coefficient to estimate ideal number of clusters in 1D IC coefficient space 
    mclust.obj <- suppressMessages(mclust::Mclust(ica_result$A[p,], modelNames = "V"))


    component_plots <- list()
    component_plots[[1]] <- plot_ic_chr(ica_result = ica_result,
                              ic_idx = p,
                              x.axis = x.axis,
                              plot.title = paste(prefix,"_IC#_",p,sep =""))

    component_plots[[2]] <- ggplot(coeff.plot.df, aes(x = idx, y = IC, col = color_geno)) + geom_point() + theme_bw()+ theme(legend.position = "none")

    component_plots[[3]] <- ggplot(coeff.plot.df, aes(x = IC, fill = color_geno)) + geom_histogram() + coord_flip() + theme_bw() 


    if(ica_summary_df[i,"geno_cor"] == 0 ){
        component_plots[[3]] <- component_plots[[3]] + theme(legend.position = "none")
    }

    cat("------------------------------------------------------------------------------","\n")

    ic.info <- data.frame(row.names = rownames(ica_summary_df)[i], 
                          "plot_number" = i,
                          "Variance_Percent" = ica_summary_df[i,"percent.var"],
                          "N correlated Genotypes" = ica_summary_df[i,"geno_cor"],
                          "Number_of_Peaks" = ica_summary_df[i,"N.peaks"])
    print(ic.info)
    cat("---- Top 10 Gene Weights ---- \n")
    gene.table <- t(round(ica_result$S[order(abs(ica_result$S[,p]), decreasing = T)[1:10],p, drop = FALSE],digits = 2))
    print(gene.table)

    rm(ic.info, gene.table)
    cat("------------------------------------------------------------------------------","\n")

    suppressMessages(multiplot(plotlist = component_plots, layout = matrix(c(1,1,1,1,2,2,3,3),ncol =4 , byrow = TRUE)))

}

jinhyunju/icreport documentation built on May 19, 2019, 10:35 a.m.