Last updated: 2019-07-28

Checks: 7 0

Knit directory: transcriptome_cll/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190511)

The command set.seed(20190511) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: cbac5c2

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  data/c2.cp.kegg.v6.0.symbols.gmt
    Untracked:  data/ddsrnaCLL_150218.RData
    Untracked:  docs/figure/ATM.Rmd/
    Untracked:  docs/figure/Del17p13.Rmd/
    Untracked:  docs/figure/Del8p12.Rmd/
    Untracked:  docs/figure/Gain8q24.Rmd/
    Untracked:  docs/figure/Med12.Rmd/
    Untracked:  docs/figure/Notch1.Rmd/
    Untracked:  docs/figure/TP53.Rmd/
    Untracked:  output/diff_genes/
    Untracked:  output/diff_meth.rds
    Untracked:  output/diff_meth_IGHV.rds
    Untracked:  output/figures/
    Untracked:  output/res_epistatsis_ighv_tri12.rds

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

Epistasis in CLL

We find correlation and coexpression of several muatations and genetic variants in CLL. How is this reflected on the transcriptome level? Is there a connection?

IGHV and Trisomy12

IGHV and trisomy12 are the most severe genomic modification on transcriptome level. How do they affect each other?

load packages

library(Biobase)
library(ggplot2)
library(genefilter)
library(DESeq2)
library(gridExtra)
library(reshape2)
library(dplyr)
library(geneplotter)
library(RColorBrewer)
library(ComplexHeatmap)
library(circlize)
library(piano)
library(ggpubr)
library(here)

load datasets

data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")

#Countdata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))

Epistasis model

Use deseq2 to determine genes, which can be described by epistatisc interactions (focus on trisomy12 and IGHV)

###Deseq
ddsCLL <- estimateSizeFactors(ddsCLL)

#exclude NAs
ddsCLL <- ddsCLL[,!is.na(colData(ddsCLL)[,"IGHV"])]
ddsCLL <- ddsCLL[,!is.na(colData(ddsCLL)[,"trisomy12"])]

RNAnorm <- varianceStabilizingTransformation(ddsCLL, blind=T)
colnames(RNAnorm) <- colData(RNAnorm)$PatID 

#design matrix with interaction term
design(ddsCLL) <- as.formula(paste("~ IGHV + trisomy12 + IGHV:trisomy12"))
#rnaRaw <- DESeq(ddsCLL, betaPrior = FALSE)
#resultsNames(rnaRaw)
#res <- results(rnaRaw, name = "IGHVU.trisomy121")

#saveRDS(res, paste0(output_dir, "/res_epistatsis_ighv_tri12.rds"))
res <- readRDS(paste0(output_dir, "/res_epistatsis_ighv_tri12.rds"))

resOrdered <- res[order(res$pvalue),]
resOrderedTab <- as.data.frame(resOrdered)
resOrderedTab$symbol <- rowData(RNAnorm[rownames(resOrdered),])$symbol

resSig <- subset(resOrdered, padj < 0.1)# & abs(log2FoldChange) > 2)
resTab <- as.data.frame(resSig)
resTab$symbol <- rowData(RNAnorm[rownames(resSig),])$symbol

Gene expression of epistatic genes

Heatmap of interacting genes

  #filter by variant
  sig_Genes <- rownames(resSig)
  
  #gene expression data
  geneExpression = assay(RNAnorm)[sig_Genes,]
  rownames(geneExpression) <- rowData(RNAnorm)$symbol[which(rownames(RNAnorm) %in% sig_Genes)]

  #scale and censor
  geneExpression_new <- log2(geneExpression)
  geneExpression_new<- t(scale(t(geneExpression_new)))
  geneExpression_new[geneExpression_new > 6] <- 6
  geneExpression_new[geneExpression_new < -6] <- -6

  mutnames <- c("none", "IGHV", "trisomy12", "both")
  
  mutStatus <- data.frame(colData(RNAnorm)) %>% mutate(IGHVnew = ifelse(IGHV %in% "M", 1, 0)) %>% dplyr::select(-IGHV) %>% mutate(IGHV = IGHVnew) %>% dplyr::select_("PatID", "IGHV", "trisomy12") %>% mutate_("namA" = "IGHV", "namB" = "trisomy12") %>% mutate(naA = as.numeric(as.character(namA))) %>% mutate(naB = as.numeric(as.character(namB))) %>% mutate(mut = factor(mutnames[1 + naA + 2 * naB], levels = mutnames)) %>% arrange(mut)

Warning: select_() is deprecated. 
Please use select() instead

The 'programming' vignette or the tidyeval book can help you
to program with select() : https://tidyeval.tidyverse.org
This warning is displayed once per session.

Warning: mutate_() is deprecated. 
Please use mutate() instead

The 'programming' vignette or the tidyeval book can help you
to program with mutate() : https://tidyeval.tidyverse.org
This warning is displayed once per session.

  geneExpression_new <- geneExpression_new[,mutStatus$PatID]


  #colors
  #colors <- colorRampPalette( rev(brewer.pal(11,"RdBu")) )(255)
  colors = colorRamp2(c(-6,-2,0,2,6), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
  annocol <- get_palette("jco", 10)
  chromcol <- list(chromosome = c("12" = annocol[6], "other" = annocol[5]))
  
  annocolor <- list(Variant = c("none" = annocol[1], annocol[2], annocol[3], "both" = annocol[4]))
  names(annocolor$Variant) <- c("none", "IGHV", "trisomy12", "both")
  mutationStatus <- data.frame(mutStatus$mut)
  rownames(mutationStatus) <- mutStatus$PatID
  colnames(mutationStatus) <- "Variant"

  #Column annotation
  ha_col = HeatmapAnnotation(df = mutationStatus, col = annocolor, annotation_height = unit(1.8, "cm"), annotation_legend_param = list(title_gp = gpar(fontsize = 60), labels_gp = gpar(fontsize = 50),  grid_height = unit(2.5, "cm"), grid_width = unit(2.5, "cm"), gap = unit(15, "mm")))

  #rowcluster
  geneExpression_dist <- dist(geneExpression_new)
  rowcluster = hclust(geneExpression_dist, method = "ward.D2")

  #heatmap
  h1 <- Heatmap(geneExpression_new, col = colors ,column_title = paste0("Gene interactions:", "IGHV", "-", "trisomy12"), column_title_gp = gpar(fontsize = 60, fontface = "bold"), heatmap_legend_param = list(title = "Expr", title_gp = gpar(fontsize = 60), grid_height = unit(2.5, "cm"), grid_width = unit(2.5, "cm"), gap = unit(15, "mm"), labels_gp = gpar(fontsize = 50), labels = c(-6,-3, 0,3, 6)) , row_dend_width = unit(3.5, "cm"), show_row_dend = T, show_column_names =FALSE , top_annotation = ha_col, show_row_names = FALSE, show_column_dend = FALSE, cluster_columns = FALSE, cluster_rows = rowcluster, split = 5, gap = unit(0.6,"cm"), column_order = mutStatus$PatID)

  #chromosome annotation
  #chromosome distribution
chrom <- as.data.frame(rowData(RNAnorm[sig_Genes,])$chromosome)
rownames(chrom) <- sig_Genes
colnames(chrom ) <- "chromosome"
#select for chromosome12
chrom$chromosome <- ifelse(chrom$chromosome == 12, 12, "other")
  
  ha_chrom = rowAnnotation(df = chrom, col = chromcol, annotation_width = unit(2.3, "cm"), annotation_legend_param = list(ncol = 2, title_gp = gpar(fontsize = 60), labels_gp = gpar(fontsize = 50),  grid_height = unit(2.5, "cm"), grid_width = unit(2.5, "cm")))
  
   
  #Annotate most significant genes
  top50 <-  rownames(subset(resOrdered, padj < 0.001))
  int_genes <- rowData(RNAnorm[top50,])$symbol

subset <- as.data.frame(rowData(RNAnorm[sig_Genes,]))
subset <- subset[-which(subset$symbol %in% ""),]
subset <- subset[-which(subset$symbol %in% NA),]

subset <- subset[which(subset$symbol %in% int_genes),]
rownames(subset) <- subset$symbol
geneIDs <- which(rownames(geneExpression_new) %in% rownames(subset))
labels <- rownames(geneExpression_new)[geneIDs]
ha_genes <- rowAnnotation(link = row_anno_link(at = geneIDs, labels = labels, labels_gp = gpar(fontsize = 45)), width = unit(9, "cm"))

Warning: anno_link() is deprecated, please use anno_mark() instead.

  #svg(filename="~/git/figures_thesis/gene_expr/epistatsisTri12IGHV.svg", width=30, height=50)
  #pdf(file="/home/almut/Dokumente/git/Transcriptome_CLL/paper/figures/epistasis_Deseq.pdf", width=35, height=45)
  draw(h1 + ha_genes + ha_chrom)

  #dev.off()
  #draw(h1 + ha_chrom + ha_genes)
 IGHVTri12 <- list("sig_Genes" = sig_Genes, "geneExp" = geneExpression_new, "h1"= h1)

Genewise count distribution

#function to create stripchart plots for specific genes
gene_count <- function(gene_nam){
  geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% gene_nam)]
  geneNum <- counts(ddsCLL)[geneEnsID,]
  mutPat <- as.data.frame(mutationStatus[colData(ddsCLL)$PatID,])
  colnames(mutPat) <- c("genotype")
  geneDat <- cbind(mutPat, geneNum)
  colnames(geneDat) <- c("genotype", "counts")
  
  p <- ggstripchart(geneDat, x = "genotype", y = "counts",
          color = "genotype",
          palette = "jco",
          add = "mean_sd",
          title = paste(gene_nam),
          ylab = "gene counts",
          font.x = 25, font.y = 25, font.legend = 20) + font("xy.text", size = 20) + font("title", size = 30, face = "bold")
 #ggsave(file=paste0("/home/almut/Dokumente/git/Transcriptome_CLL/paper/figures/epi_genes/genetic_interaction_", gene_nam, ".svg"), plot=p, width=7, height=5)
  p
}


#interesting genes
geneList <- c("LEF1", "TIMELESS", "CHAD", "BCL2A1", "EML6", "PPP1R14A", "EPHB6", "GEN1", "EBF1", "EBF4")


lapply(geneList, gene_count)

[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]


[[7]]


[[8]]


[[9]]


[[10]]

Clusterwise count distribution

cluster <- c("buffering_dn","sup_tri", "buffering_up" ,"buffering_up_strong", "inversion_up")

cluster_size <- lapply(c(1:5), function(clusternr){
  size <- length(row_order(IGHVTri12$h1)[[clusternr]])
  name <- cluster[clusternr]
  clust <- c(name, as.numeric(size))
}) %>% do.call(rbind,.) 

cluster_size <- as.data.frame(cluster_size)
colnames(cluster_size) <- c("name", "size")
cluster_size$size <- as.numeric(as.character(cluster_size$size))
distr <- ggbarplot(cluster_size, "name", "size",
   fill = "name", color = "name",
   palette = "jco",
   ylab = "Number of genes",
   xlab = "cluster")
 
 #ggsave(file="/home/almut/Dokumente/masterarbeit/workinprogress/distrib_ofEpiTypes.svg", plot=distr, width=8, height=5)
distr

Enrichment analysis

List-based enrichment - Piano package Fisher’s exact on genes from one cluster only.

#load gene set collection
#Hallmark
gsc <- loadGSC("/home/almut/Dokumente/masterarbeit/data/h.all.v6.0.symbols.gmt", type="gmt")
#Kegg
gsc_Kegg <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c2.cp.kegg.v6.0.symbols.gmt", type="gmt")
tft <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c3.tft.v6.0.symbols.gmt", type="gmt")
c5 <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c5.all.v6.0.symbols.gmt", type="gmt")
c6 <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c6.all.v6.0.symbols.gmt", type="gmt")
c7 <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c7.all.v6.0.symbols.gmt", type="gmt")
cgp <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c2.cgp.v6.0.symbols.gmt", type="gmt")

#GSA on cluster defined by kmeans clustering. See cluster in heatmap...
setWiseGSA <- function(clusternr, gmtFile){
  #get sig genes and pvalues for each cluster
  geneNam <- IGHVTri12$sig_Genes[row_order(IGHVTri12$h1)[[clusternr]]] 
  pVal <- resSig[geneNam, "padj"]
  #needs to be symbol/remove those without symbol, but ENSID only
  genName <- rowData(RNAnorm[geneNam,])$symbol
  gsTab <- data.frame(gene = genName, stat = pVal)
  gsTab <- gsTab[-which(gsTab$gene %in% c("", NA)),]
  gsaTab <- data.frame(row.names = gsTab$gene, stat = gsTab$stat)
  res <- runGSA(geneLevelStats = gsaTab,
                      geneSetStat = "fisher",
                     adjMethod = "fdr", gsc=gmtFile,
                     signifMethod = 'nullDist',
                     #nPerm = 50000,
                      gsSizeLim=c(1, Inf))
  Res <- arrange(GSAsummaryTable(res),desc(`Stat (non-dir.)`))
  #rename results to plot
  resPlot <- Res[, c(1:5)]
  colnames(resPlot) <- c("pathway", "gene_number", "stat", "p", "p.adj")

  #barplot
  #ggplot(resPlot %>% mutate(log10Padj = -log10(p.adj)), aes(x = reorder(pathway, log10Padj), y = log10Padj, fill = gene_number)) + geom_bar(stat = "identity") + coord_flip() + ggtitle(cluster[clusternr])
  
  enrichPlot <- resPlot[c(1:10),] %>% mutate(log10Padj = -log10(p.adj)) %>% mutate(genes = ifelse(gene_number > 5, ">5", "<=5"))
  
  p <- ggbarplot(enrichPlot, x = "pathway", y = "log10Padj",
          fill = "genes",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",          # Sort the value in ascending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "-log10(padj)",
          legend.title = "Pathway",
          rotate = TRUE,
          font.x = 20, font.y = 20, font.legend = 20, legend = "right", 
          title = paste0("GSEA in ", cluster[clusternr]),
          ggtheme = theme_pubr()
          ) + font("xy.text", size = 22) + font("title", size = 20, face = "bold")
  
   ggsave(file=paste0("/home/almut/Dokumente/masterarbeit/results/enrichment_epistasis/GSEA_in_", clusternr, ".pdf"), plot=p, width=16, height=7)
#pdf(file= paste0("/home/almut/Dokumente/git/Transcriptome_CLL/paper/figures/GSEA_in_", clusternr, ".pdf"), width=40, height=35)
  p
 # dev.off()
  
  
  }

clusternr = c(1:5)


lapply(as.numeric(clusternr), FUN = "setWiseGSA", gmtFile = gsc)

Running gene set analysis:

Checking arguments...done!

Final gene/gene-set association: 34 genes and 35 gene sets

  Details:

  Calculating gene set statistics from 34 out of 118 gene-level statistics

  Removed 4352 genes from GSC due to lack of matching gene statistics

  Removed 15 gene sets containing no genes after gene removal

  Removed additionally 0 gene sets not matching the size limits

  Loaded additional information for 35 gene sets

Calculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!

Running gene set analysis:

Checking arguments...done!

Final gene/gene-set association: 37 genes and 33 gene sets

  Details:

  Calculating gene set statistics from 37 out of 220 gene-level statistics

  Removed 4349 genes from GSC due to lack of matching gene statistics

  Removed 17 gene sets containing no genes after gene removal

  Removed additionally 0 gene sets not matching the size limits

  Loaded additional information for 33 gene sets

Calculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!

Running gene set analysis:

Checking arguments...done!

Final gene/gene-set association: 36 genes and 35 gene sets

  Details:

  Calculating gene set statistics from 36 out of 111 gene-level statistics

  Removed 4350 genes from GSC due to lack of matching gene statistics

  Removed 15 gene sets containing no genes after gene removal

  Removed additionally 0 gene sets not matching the size limits

  Loaded additional information for 35 gene sets

Calculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!

Running gene set analysis:

Checking arguments...done!

Final gene/gene-set association: 20 genes and 23 gene sets

  Details:

  Calculating gene set statistics from 20 out of 105 gene-level statistics

  Removed 4366 genes from GSC due to lack of matching gene statistics

  Removed 27 gene sets containing no genes after gene removal

  Removed additionally 0 gene sets not matching the size limits

  Loaded additional information for 23 gene sets

Calculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!

Running gene set analysis:

Checking arguments...done!

Final gene/gene-set association: 39 genes and 29 gene sets

  Details:

  Calculating gene set statistics from 39 out of 164 gene-level statistics

  Removed 4347 genes from GSC due to lack of matching gene statistics

  Removed 21 gene sets containing no genes after gene removal

  Removed additionally 0 gene sets not matching the size limits

  Loaded additional information for 29 gene sets

Calculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!

[[1]]


[[2]]


[[3]]


[[4]]


[[5]]

Mixed epistatsis scheme

Scheme to show different ways of mixed epistasis

#generate a dataframe
mix <- t(data.frame(buffering_up = c(-0.1, -0.5, 0.5, 5), buffering_dn = c(0.5, 0.2, -0.2, -5), suppression_1 = c(0.5, -5, -6, 0.2), suppression_2 = c(-0.2, 5, 4,-0.5), suppression_3 = c(0.5, 0.1, -6, 1), suppression_4 = c(-0.2, 0.5, 4,-0.5), inversion_up = c(-0.2, -6, -4, 5), inversion_dn = c(0.1, 5, 4, -5)))
colnames(mix) <-  c("none", "IGHV", "trisomy12", "both")

annocol <- get_palette("jco", 10)
annocolor <- list(Variant = c("none" = annocol[1], annocol[2], annocol[3], "both" = annocol[4]))
names(annocolor$Variant) <- c("none", "IGHV", "trisomy12", "both")
variants <- as.data.frame(colnames(mix))
colnames(variants) <- "Variant"
rownames(variants) <- variants$Variant

  #Column annotation
  ha_col = HeatmapAnnotation(df = variants, col = annocolor, annotation_height = unit(0.9, "cm"), annotation_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15),  grid_height = unit(0.9, "cm"), grid_width = unit(0.9, "cm"), gap = unit(15, "mm")))

#heatmap
h1 <- Heatmap(mix, col = colors ,column_title = paste0("Types of mixed epistasis"), column_title_gp = gpar(fontsize = 20, fontface = "bold"), heatmap_legend_param = list(title = "Expr.", title_gp = gpar(fontsize = 20), grid_height = unit(1, "cm"), grid_width = unit(0.5, "cm"), gap = unit(10, "mm"), labels_gp = gpar(fontsize = 20), labels = c(-6,-3, 0,3, 6)) , show_row_dend = F, show_column_names =FALSE , top_annotation = ha_col, show_row_names = FALSE, show_column_dend = FALSE, cluster_columns = FALSE, cluster_rows = FALSE, split = c( rep("Buffering",2), rep("Suppression", 4), rep("Inversion", 2)), gap = unit(0.6,"cm"), row_title_gp = gpar(fontsize=19))

#pdf(file="/home/almut/Dokumente/git/Transcriptome_CLL/paper/figures/mixed_epistasis_model.pdf", width=7, height=5)
draw(h1)

#dev.off()

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
 [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
 [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] here_0.1                    ggpubr_0.2                 
 [3] magrittr_1.5                piano_2.0.2                
 [5] circlize_0.4.6              ComplexHeatmap_2.0.0       
 [7] RColorBrewer_1.1-2          geneplotter_1.62.0         
 [9] annotate_1.62.0             XML_3.98-1.20              
[11] AnnotationDbi_1.46.0        lattice_0.20-38            
[13] dplyr_0.8.1                 reshape2_1.4.3             
[15] gridExtra_2.3               DESeq2_1.24.0              
[17] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[19] BiocParallel_1.18.0         matrixStats_0.54.0         
[21] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
[23] IRanges_2.18.1              S4Vectors_0.22.0           
[25] genefilter_1.66.0           ggplot2_3.1.1              
[27] Biobase_2.44.0              BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] fgsea_1.10.0           colorspace_1.4-1       rjson_0.2.20          
 [4] rprojroot_1.3-2        htmlTable_1.13.1       XVector_0.24.0        
 [7] GlobalOptions_0.1.0    base64enc_0.1-3        fs_1.3.1              
[10] clue_0.3-57            rstudioapi_0.10        DT_0.7                
[13] bit64_0.9-7            splines_3.6.0          knitr_1.23            
[16] jsonlite_1.6           Formula_1.2-3          workflowr_1.4.0       
[19] cluster_2.1.0          png_0.1-7              shinydashboard_0.7.1  
[22] shiny_1.3.2            compiler_3.6.0         backports_1.1.4       
[25] assertthat_0.2.1       Matrix_1.2-17          lazyeval_0.2.2        
[28] limma_3.40.2           later_0.8.0            visNetwork_2.0.7      
[31] acepack_1.4.1          htmltools_0.3.6        tools_3.6.0           
[34] igraph_1.2.4.1         gtable_0.3.0           glue_1.3.1            
[37] GenomeInfoDbData_1.2.1 fastmatch_1.1-0        Rcpp_1.0.1            
[40] slam_0.1-45            gdata_2.18.0           xfun_0.7              
[43] stringr_1.4.0          mime_0.7               gtools_3.8.1          
[46] zlibbioc_1.30.0        scales_1.0.0           promises_1.0.1        
[49] relations_0.6-8        sets_1.0-18            yaml_2.2.0            
[52] memoise_1.1.0          rpart_4.1-15           latticeExtra_0.6-28   
[55] stringi_1.4.3          RSQLite_2.1.1          checkmate_1.9.3       
[58] caTools_1.17.1.2       shape_1.4.4            rlang_0.3.4           
[61] pkgconfig_2.0.2        bitops_1.0-6           evaluate_0.14         
[64] purrr_0.3.2            labeling_0.3           htmlwidgets_1.3       
[67] bit_1.1-14             tidyselect_0.2.5       ggsci_2.9             
[70] plyr_1.8.4             R6_2.4.0               gplots_3.0.1.1        
[73] Hmisc_4.2-0            DBI_1.0.0              pillar_1.4.1          
[76] foreign_0.8-71         withr_2.1.2            survival_2.44-1.1     
[79] RCurl_1.95-4.12        nnet_7.3-12            tibble_2.1.3          
[82] crayon_1.3.4           KernSmooth_2.23-15     rmarkdown_1.13        
[85] GetoptLong_0.1.7       locfit_1.5-9.1         data.table_1.12.2     
[88] marray_1.62.0          blob_1.1.1             git2r_0.25.2          
[91] digest_0.6.19          xtable_1.8-4           httpuv_1.5.1          
[94] munsell_0.5.0          shinyjs_1.0

epistasis

aluetge

2019-07-26