The most recent update of this html document occurred: Wed May 16 10:53:51 2018
library(knitr)
library(ggplot2)
library(reshape)
library(DESeq2)## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
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## Vignettes contain introductory material; view with
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## rowSds, rowVarslibrary(CHBUtils)
library(gtools)
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## intersect, setdiff, setequal, unionlibrary(isomiRs)## Loading required package: DiscriMiner## Loading required package: TMB## Loading required package: RcppEigenlibrary(pheatmap)
knitr::opts_chunk$set(tidy=TRUE, highlight=TRUE, dev="png", fig.width=6,fig.heigh=6,
cache=FALSE, highlight=TRUE, autodep=TRUE,
warning=FALSE, error=FALSE,
message=FALSE, prompt=TRUE, comment='', fig.cap='', bootstrap.show.code=FALSE)
root_path = "C:/rstudio/mirna/final/"
root_file = file.path(root_path, "srna_out_files")
dir.create(root_file, showWarnings = FALSE)
metadata_fn = list.files(file.path(root_path), pattern = "summary.csv$",recursive = T, full.names = T)
metadata = read.csv(metadata_fn, row.names="sample_id")
condition = names(metadata)[1]
design = metadata
formula = ~ condition # modify this to get your own formula, it should be a column in your metadata
isde=TRUE # turn this true to make DE ananlysisExploratory analysis
In this section we will see descriptive figures about quality of the data, reads with adapter, reads mapped to miRNAs, reads mapped to other small RNAs.
size distribution
After adapter removal, we can plot the size distribution of the small RNAs.
> files = list.files(file.path(root_path), pattern = "trimming_stats", recursive = T)
> isadapter = length(files) > 0> names(files) = sapply(files, function(x) {
+ gsub("-ready.trimming_stats", "", basename(x))
+ })
>
>
> tab = data.frame()
> for (sample in rownames(metadata)) {
+ d = read.table(file.path(root_path, files[sample]), sep = " ")
+ tab = rbind(tab, d %>% mutate(sample = sample, group = metadata[sample,
+ condition]))
+ }
>
>
> reads_adapter = tab %>% group_by(sample, group) %>% summarise(total = sum(V2))
> ggplot(reads_adapter, aes(x = sample, y = total, fill = group)) + geom_bar(stat = "identity",
+ position = "dodge") + ggtitle("total number of reads with adapter") + ylab("# reads") +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
> ggplot(tab, aes(x = V1, y = V2, group = sample)) + geom_bar(stat = "identity",
+ position = "dodge") + facet_wrap(~group, ncol = 2) + ggtitle("size distribution") +
+ ylab("# reads") + xlab("size") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))
> files = list.files(file.path(root_path), pattern = "mirbase-ready", recursive = T,
+ full.names = T)
> ismirbase = length(files) > 0
> mirdeep2_files = list.files(file.path(root_path), pattern = "novel-ready", recursive = T,
+ full.names = T)
> ismirdeep2 = length(mirdeep2_files) > 0miRNA
total miRNA expression annotated with mirbase
> names(files) = sapply(files, function(x) {
+ gsub("-mirbase-ready.counts", "", basename(x))
+ })
>
> obj <- IsomirDataSeqFromFiles(files = files[rownames(design)], coldata = design,
+ header = T, skip = 0)> ggplot(data.frame(sample = colnames(counts(obj)), total = colSums(counts(obj)))) +
+ geom_bar(aes(x = sample, y = total), stat = "identity") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))
> mirna_step <- as.data.frame(colSums(counts(obj)))Distribution of mirna expression
> ggplot(melt(counts(obj))) + geom_boxplot(aes(x = X2, y = value)) + scale_y_log10() +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
cumulative distribution of miRNAs
> cs <- as.data.frame(apply(counts(obj), 2, function(x) {
+ cumsum(sort(x, decreasing = T))
+ }))
> cs$pos <- 1:nrow(cs)
>
> ggplot((melt(cs, id.vars = "pos"))) + geom_line(aes(x = pos, y = value, color = variable)) +
+ scale_y_log10()
Clustering
> counts = counts(obj)
> dds = DESeqDataSetFromMatrix(counts[rowSums(counts > 0) > 10, ], colData = design,
+ design = ~1)
> vst = rlog(dds)
>
> # pheatmap(assay(vst), annotation_col = design, show_rownames = F,
> # clustering_distance_cols = 'correlation', clustering_method = 'ward.D')MDS plot
> # mds(assay(vst), condition = design[,condition])complexity
Number of miRNAs with > 3 counts.
> kable(as.data.frame(colSums(counts > 10)))| colSums(counts > 10) | |
|---|---|
| SRR2496797 | 254 |
| SRR2496785 | 264 |
| SRR2496788 | 139 |
| SRR2496792 | 340 |
| SRR2496795 | 304 |
| SRR2496783 | 104 |
| SRR2496791 | 208 |
| SRR2496787 | 246 |
| SRR2496782 | 290 |
| SRR2496784 | 265 |
| SRR2496786 | 82 |
| SRR2496798 | 266 |
| SRR2496796 | 202 |
| SRR2496781 | 101 |
| SRR2496790 | 293 |
| SRR2496793 | 92 |
| SRR2496789 | 130 |
| SRR2496794 | 321 |
Differential expression
DESeq2 is used for this analysis.
> library(DESeq2)
> # library(DEGreport)
> library(vsn)> #' save file
> save_file <- function(dat, fn, basedir = ".") {
+ tab <- cbind(id = data.frame(id = row.names(dat)), as.data.frame(dat))
+ write.table(tab, file.path(basedir, fn), quote = F, sep = "\t", row.names = F)
+ }
>
> filter_handle <- function(res) {
+ res_nona <- res[!is.na(res$padj), ]
+ keep <- res_nona$padj < 0.1
+ res_nona[keep, ]
+ }
>
> handle_deseq2 = function(dds, summarydata, column, prefix, all_combs = NULL) {
+ if (is.null(all_combs))
+ all_combs = combn(levels(summarydata[, column]), 2, simplify = FALSE)
+ all_results = list()
+ contrast_strings = list()
+ rlog = rlog(dds)
+ for (comb in all_combs) {
+ contrast_string = paste(comb, collapse = "_vs_")
+ cat("\n\n## Comparison: ", contrast_string, "\n")
+ contrast = c(column, comb)
+ res = results(dds, contrast = contrast)
+ res = res[order(res$padj), ]
+ all_results = c(all_results, res)
+ contrast_strings = c(contrast_strings, contrast_string)
+ print_out(dds, rlog, res, paste0(prefix, "_", contrast_string))
+ }
+ names(all_results) = contrast_strings
+ return(all_results)
+ }
>
> do_de = function(raw, summarydata, condition, minc = 3) {
+ dss = DESeqDataSetFromMatrix(countData = raw[rowMeans(raw) > minc, ], colData = summarydata,
+ design = ~condition)
+ dss = DESeq(dss)
+ dss
+ }
>
> do_norm = function(dss, path, prefix) {
+ rlog_ma = assay(rlog(dss))
+ count_ma = counts(dss, normalized = TRUE)
+ raw = counts(dss, normalized = FALSE)
+
+ fn_log = paste0(prefix, "_log_matrix.txt")
+ save_file(rlog_ma, fn_log, path)
+
+ fn_count = paste0(prefix, "_norm_matrix.txt")
+ save_file(count_ma, fn_count, path)
+
+ fn_raw = paste0(prefix, "_raw_matrix.txt")
+ save_file(raw, fn_raw, path)
+ }
>
> print_out = function(dss, rlog = NULL, res = NULL, prefix = "standard_") {
+ plotDispEsts(dss)
+ if (is.null(res))
+ res = results(dss)
+ if (is.null(rlog))
+ rlog = rlog(dss)
+
+ rlogmat = assay(rlog)
+ design = as.data.frame(colData(dss)[, names(colData(dss)) != "sizeFactor",
+ drop = FALSE])
+
+ out_df = as.data.frame(res)
+ out_df = out_df[!is.na(out_df$padj), ]
+ out_df = out_df[order(out_df$padj), ]
+ do_norm(dss, root_file, prefix)
+
+ cat("\n", paste(capture.output(summary(res))[1:8], collapse = "<br>"), "\n")
+ cat("\n\n### MA plot plot\n\n")
+ DESeq2::plotMA(res)
+
+ cat("\n\n### Top DE miRNAs\n\n")
+ print(kable(head(out_df, 20)))
+ fn = paste(prefix, ".tsv", sep = "")
+ save_file(out_df, fn, root_file)
+
+ sign = row.names(out_df)[out_df$padj < 0.05 & !is.na(out_df$padj) & abs(out_df$log2FoldChange) >
+ 0.5]
+
+ cat("\n\n### Heatmap most significand(", length(sign), "), padj<0.05 and log2FC > 0.5\n")
+ if (length(sign) < 5000) {
+ cat("Too few genes to plot.")
+ } else {
+ pheatmap(rlogmat[sign, ], show_rownames = F, annotation_col = design,
+ clustering_distance_cols = "correlation", clustering_method = "ward.D2")
+ mds(rlogmat[sign, ], condition = design[, condition])
+ }
+
+ }Analysis for miRNA
> counts = counts(obj)
> dss = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) > 3, ],
+ colData = design, design = ~batch)
> dss = DESeq(dss)
> all_results = handle_deseq2(dss, design, condition, "mirna_")Comparison: F_vs_M
out of 406 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 20, 4.9%
LFC < 0 (down) : 2, 0.49%
outliers [1] : 0, 0%
low counts [2] : 142, 35%
(mean count < 4)
MA plot plot
Top DE miRNAs
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| hsa-miR-4646-5p | 3.405981e+01 | 8.797824 | 1.7809461 | 4.939972 | 0.0000008 | 0.0002063 |
| hsa-miR-219a-2-3p | 5.981795e+01 | 7.451697 | 1.7239601 | 4.322431 | 0.0000154 | 0.0020370 |
| hsa-miR-1275 | 9.207448e+01 | 6.228503 | 1.5449361 | 4.031560 | 0.0000554 | 0.0048759 |
| hsa-miR-10b-5p | 4.028161e+05 | -2.416169 | 0.6273645 | -3.851300 | 0.0001175 | 0.0051697 |
| hsa-miR-486-5p | 1.611402e+06 | -2.413232 | 0.6208915 | -3.886720 | 0.0001016 | 0.0051697 |
| hsa-miR-576-3p | 2.427332e+01 | 6.639493 | 1.7238433 | 3.851564 | 0.0001174 | 0.0051697 |
| hsa-miR-7847-3p | 2.851261e+01 | 6.423556 | 1.7263968 | 3.720788 | 0.0001986 | 0.0074901 |
| hsa-miR-2110 | 3.624389e+01 | 5.169466 | 1.4117892 | 3.661641 | 0.0002506 | 0.0082699 |
| hsa-miR-766-5p | 4.702677e+01 | 9.299753 | 2.6627057 | 3.492595 | 0.0004784 | 0.0140316 |
| hsa-miR-548k | 4.989899e+00 | 6.149741 | 1.8623425 | 3.302153 | 0.0009595 | 0.0253296 |
| hsa-miR-193b-5p | 4.921041e+01 | 3.502641 | 1.0763114 | 3.254301 | 0.0011367 | 0.0272813 |
| hsa-miR-320d | 1.178751e+02 | 3.052208 | 0.9528917 | 3.203100 | 0.0013596 | 0.0299105 |
| hsa-miR-181c-5p | 9.698695e+01 | 3.028320 | 0.9577253 | 3.161993 | 0.0015669 | 0.0305699 |
| hsa-miR-4665-5p | 4.865710e+01 | 9.350295 | 2.9663909 | 3.152078 | 0.0016211 | 0.0305699 |
| hsa-miR-3620-5p | 2.632829e+01 | 5.576037 | 1.8232027 | 3.058375 | 0.0022254 | 0.0391672 |
| hsa-miR-6894-5p | 8.263860e+00 | 6.009911 | 2.0274985 | 2.964200 | 0.0030347 | 0.0500727 |
| hsa-miR-6740-5p | 2.187887e+02 | 3.333372 | 1.1501896 | 2.898106 | 0.0037542 | 0.0583010 |
| hsa-miR-30c-1-3p | 1.282674e+02 | 3.310783 | 1.1676563 | 2.835409 | 0.0045767 | 0.0635921 |
| hsa-miR-7975 | 6.217590e+00 | 6.263759 | 2.1988187 | 2.848693 | 0.0043899 | 0.0635921 |
| hsa-miR-378a-3p | 6.583108e+03 | 2.154846 | 0.7784957 | 2.767962 | 0.0056408 | 0.0744587 |
Heatmap most significand( 15 ), padj<0.05 and log2FC > 0.5
Too few genes to plot.
> DESeq2::plotCounts(dds, put_gene_name_here)Analysis for novel miRNA
> counts = counts(obj_mirdeep)
> dss_mirdeep2 = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) >
+ 3, ], colData = design, design = ~batch)
>
> dss = DESeq(dss)
> all_results = handle_deseq2(dss_mirdeep2, design, condition, "mirdeep2_")Analysis for isomiRs
> counts_iso = counts(isoCounts(obj, ref = T, iso5 = T, iso3 = T, add = T, subs = T))
> dss_iso = DESeqDataSetFromMatrix(countData = counts_iso[rowSums(counts_iso >
+ 0) > 3, ], colData = design, design = ~batch)
>
> dss_iso = DESeq(dss_iso)
> all_results = handle_deseq2(dss_iso, design, condition, "isomirs_")Comparison: F_vs_M
out of 2332 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 106, 4.5%
LFC < 0 (down) : 28, 1.2%
outliers [1] : 0, 0%
low counts [2] : 769, 33%
(mean count < 2)
MA plot plot
Top DE miRNAs
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| hsa-miR-483-5p.iso.t5:0.t3:0.ad:A.mm:0 | 168.88734 | 11.226455 | 1.696447 | 6.617626 | 0.00e+00 | 0.0000001 |
| hsa-miR-193b-5p.iso.t5:0.t3:tga.ad:0.mm:0 | 116.42661 | 9.109118 | 1.475957 | 6.171668 | 0.00e+00 | 0.0000005 |
| hsa-miR-320a.iso.t5:0.t3:0.ad:TAT.mm:0 | 34.67457 | 8.921150 | 1.720230 | 5.186020 | 2.00e-07 | 0.0000969 |
| hsa-miR-584-5p.iso.t5:0.t3:g.ad:AT.mm:0 | 10.22043 | 7.249124 | 1.405099 | 5.159157 | 2.00e-07 | 0.0000969 |
| hsa-miR-1307-3p.iso.t5:0.t3:G.ad:0.mm:0 | 19.78330 | 8.166836 | 1.620583 | 5.039443 | 5.00e-07 | 0.0001303 |
| hsa-miR-320a.iso.t5:0.t3:cga.ad:ATT.mm:0 | 12.57756 | 7.528003 | 1.514466 | 4.970733 | 7.00e-07 | 0.0001303 |
| hsa-miR-320b.iso.t5:0.t3:caa.ad:ATT.mm:0 | 12.57756 | 7.528003 | 1.514466 | 4.970733 | 7.00e-07 | 0.0001303 |
| hsa-miR-320c.iso.t5:0.t3:t.ad:ATT.mm:0 | 12.57756 | 7.528003 | 1.514466 | 4.970733 | 7.00e-07 | 0.0001303 |
| hsa-miR-30c-1-3p.iso.t5:0.t3:cc.ad:0.mm:0 | 54.38555 | 8.168848 | 1.666152 | 4.902821 | 9.00e-07 | 0.0001641 |
| hsa-miR-6740-5p.iso.t5:ag.t3:A.ad:0.mm:0 | 22.77918 | 8.273814 | 1.817105 | 4.553295 | 5.30e-06 | 0.0007724 |
| hsa-miR-873-3p.ref.t5:0.t3:0.ad:0.mm:0 | 18.28245 | 8.007490 | 1.760961 | 4.547227 | 5.40e-06 | 0.0007724 |
| hsa-miR-6740-5p.ref.t5:0.t3:0.ad:0.mm:0 | 33.27529 | 7.614839 | 1.683390 | 4.523516 | 6.10e-06 | 0.0007922 |
| hsa-miR-320a.iso.t5:G.t3:cga.ad:AT.mm:0 | 22.23981 | 7.916747 | 1.821125 | 4.347175 | 1.38e-05 | 0.0014369 |
| hsa-miR-320b.iso.t5:G.t3:caa.ad:AT.mm:0 | 22.23981 | 7.916747 | 1.821125 | 4.347175 | 1.38e-05 | 0.0014369 |
| hsa-miR-320c.iso.t5:G.t3:t.ad:AT.mm:0 | 22.23981 | 7.916747 | 1.821125 | 4.347175 | 1.38e-05 | 0.0014369 |
| hsa-miR-576-3p.iso.t5:0.t3:C.ad:0.mm:0 | 21.64953 | 8.209513 | 1.922180 | 4.270938 | 1.95e-05 | 0.0019015 |
| hsa-miR-9-5p.iso.t5:0.t3:a.ad:T.mm:0 | 13.14213 | 7.578587 | 1.821900 | 4.159717 | 3.19e-05 | 0.0029296 |
| hsa-miR-584-5p.iso.t5:0.t3:g.ad:A.mm:0 | 34.96398 | 6.050823 | 1.465173 | 4.129767 | 3.63e-05 | 0.0031532 |
| hsa-miR-320a.iso.t5:0.t3:a.ad:TT.mm:0 | 611.38604 | 4.212629 | 1.028334 | 4.096556 | 4.19e-05 | 0.0034142 |
| hsa-miR-378a-3p.iso.t5:a.t3:0.ad:A.mm:0 | 22.81826 | 6.364778 | 1.557300 | 4.087059 | 4.37e-05 | 0.0034142 |
Heatmap most significand( 95 ), padj<0.05 and log2FC > 0.5
Too few genes to plot.
> DESeq2::plotCounts(dds, put_gene_name_here)Files
Files generated contains raw count, normalized counts, log2 normalized counts and DESeq2 results.
R Session Info
(useful if replicating these results)
> sessionInfo()R version 3.4.4 (2018-03-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=Russian_Russia.1251 LC_CTYPE=Russian_Russia.1251
[3] LC_MONETARY=Russian_Russia.1251 LC_NUMERIC=C
[5] LC_TIME=Russian_Russia.1251
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] vsn_3.46.0 bindrcpp_0.2
[3] pheatmap_1.0.8 isomiRs_1.6.0
[5] RcppEigen_0.3.3.4.0 TMB_1.7.12
[7] DiscriMiner_0.1-29 dplyr_0.7.4
[9] devtools_1.13.5 gridExtra_2.3
[11] gtools_3.5.0 CHBUtils_0.1
[13] genefilter_1.60.0 DESeq2_1.18.1
[15] SummarizedExperiment_1.8.1 DelayedArray_0.4.1
[17] matrixStats_0.53.1 Biobase_2.38.0
[19] GenomicRanges_1.30.0 GenomeInfoDb_1.14.0
[21] IRanges_2.12.0 S4Vectors_0.16.0
[23] BiocGenerics_0.24.0 reshape_0.8.7
[25] ggplot2_2.2.1 knitr_1.20
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_1.3-2 rprojroot_1.3-2
[4] htmlTable_1.11.2 XVector_0.18.0 base64enc_0.1-3
[7] rstudioapi_0.7 affyio_1.48.0 bit64_0.9-7
[10] AnnotationDbi_1.40.0 splines_3.4.4 geneplotter_1.56.0
[13] Formula_1.2-2 nloptr_1.0.4 annotate_1.56.1
[16] cluster_2.0.6 gamlss.dist_5.0-4 readr_1.1.1
[19] compiler_3.4.4 backports_1.1.2 assertthat_0.2.0
[22] Matrix_1.2-12 lazyeval_0.2.1 limma_3.34.9
[25] formatR_1.5 acepack_1.4.1 htmltools_0.3.6
[28] tools_3.4.4 gtable_0.2.0 glue_1.2.0
[31] GenomeInfoDbData_1.0.0 affy_1.56.0 Rcpp_0.12.16
[34] gdata_2.18.0 preprocessCore_1.40.0 nlme_3.1-131.1
[37] stringr_1.3.0 lme4_1.1-15 XML_3.98-1.10
[40] zlibbioc_1.24.0 MASS_7.3-49 scales_0.5.0
[43] BiocInstaller_1.28.0 hms_0.4.2 gamlss.data_5.0-0
[46] RColorBrewer_1.1-2 yaml_2.1.18 memoise_1.1.0
[49] rpart_4.1-13 latticeExtra_0.6-28 stringi_1.1.7
[52] RSQLite_2.0 highr_0.6 checkmate_1.8.5
[55] caTools_1.17.1 BiocParallel_1.12.0 rlang_0.2.0
[58] pkgconfig_2.0.1 bitops_1.0-6 evaluate_0.10.1
[61] lattice_0.20-35 purrr_0.2.4 bindr_0.1.1
[64] htmlwidgets_1.0 labeling_0.3 bit_1.1-12
[67] GGally_1.3.2 plyr_1.8.4 magrittr_1.5
[70] R6_2.2.2 gplots_3.0.1 Hmisc_4.1-1
[73] DBI_0.8 pillar_1.2.1 foreign_0.8-69
[76] withr_2.1.2 survival_2.41-3 RCurl_1.95-4.10
[79] nnet_7.3-12 tibble_1.4.2 gamlss_5.0-6
[82] KernSmooth_2.23-15 rmarkdown_1.9 locfit_1.5-9.1
[85] grid_3.4.4 data.table_1.10.4-3 blob_1.1.0
[88] digest_0.6.15 xtable_1.8-2 tidyr_0.8.0
[91] munsell_0.4.3