Last updated: 2020-01-25
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Knit directory: peco-paper/
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| Rmd | fdebe2b | jhsiao999 | 2020-01-25 | code to compute prediction error in unthinned and thinned data |
Load packages
library(SingleCellExperiment)
library(peco)
library(ggplot2)
library(dplyr)Prepare training/testing data
sce <- readRDS("data/sce-final.rds")
sce <- sce[grep("ENSG", rownames(sce)),]
fdata <- data.frame(colData(sce))
fdata <- data.frame(rowData(sce))
counts <- data.frame(assay(sce, "counts"))
sce_normed <- data_transform_quantile(sce)
log2cpm_quant <- assay(sce_normed, "cpm_quantNormed")
inds <- c("NA18511", "NA18855", "NA18870", "NA19098", "NA19101", "NA19160")
theta <- pdata$theta
# function to make training/testing data
makedata_supervised <- function(sce, log2cpm_quant,
theta) {
message("Create data/data_training_test folder \n")
if (!file.exists("data/data_training_test")) { dir.create("data/data_training_test") }
library(SingleCellExperiment)
library(peco)
pdata <- data.frame(colData(sce))
fdata <- data.frame(rowData(sce))
counts <- data.frame(assay(sce))
counts <- counts[grep("ENSG", rownames(counts)), ]
log2cpm <- t(log2(1+(10^6)*(t(counts)/pdata$molecules)))
for (ind in unique(pdata$chip_id)) {
ii_test <- c(1:nrow(pdata))[which(pdata$chip_id == ind)]
ii_train <- c(1:nrow(pdata))[which(pdata$chip_id != ind)]
pdata_test <- pdata[ii_test,]
pdata_train <- pdata[ii_train,]
log2cpm_quant_test <- log2cpm_quant[,ii_test]
log2cpm_quant_train <- log2cpm_quant[,ii_train]
theta <- pdata$theta
names(theta) <- rownames(pdata)
log2cpm_test <- log2cpm[,ii_test]
log2cpm_train <- log2cpm[,ii_train]
counts_test <- counts[,ii_test]
counts_train <- counts[,ii_train]
theta_test <- theta[ii_test]
theta_train <- theta[ii_train]
#sig.genes <- readRDS("output/npreg-trendfilter-quantile.Rmd/out.stats.ordered.sig.476.rds")
data_training <- list(theta_train=theta_train,
log2cpm_quant_train=log2cpm_quant_train,
log2cpm_train=log2cpm_train,
counts_train=counts_train,
pdata_train=pdata_train,
fdata=fdata)
data_test <- list(theta_test=theta_test,
log2cpm_quant_test=log2cpm_quant_test,
log2cpm_test=log2cpm_test,
counts_test = counts_test,
pdata_test=pdata_test,
fdata=fdata)
saveRDS(data_training,
file=file.path(paste0("data/data_training_test/ind_",ind,"_data_training.rds")))
saveRDS(data_test,
file=file.path(paste0("data/data_training_test/ind_",ind,"_data_test.rds")))
}
}
makedata_supervised(sce, log2cpm_quant, theta)Prepare thinned data. We used R package seqgendiff to thin the expression.
# function to make thinned data
makedata_thinned <- function(sce, thinlog2_rate) {
library(SingleCellExperiment)
library(peco)
pdata <- data.frame(colData(sce))
fdata <- data.frame(rowData(sce))
counts <- assay(sce)[grep("ENSG", rownames(sce)), ]
log2cpm <- t(log2(1+(10^6)*(t(counts)/pdata$molecules)))
# ---- thinning!
library(seqgendiff)
nsamp <- ncol(counts)
inputmat <- counts
thinlog2 <- rexp(nsamp, rate = thinlog2_rate)
outmat <- thin_lib(inputmat, thinlog2 = thinlog2)
counts_thin <- outmat$mat
dimnames(counts_thin) <- dimnames(counts)
libsize <- colSums(counts_thin)
keep_samples <- which(colMeans(counts_thin==0)>.01)
keep_genes <- which(rowMeans(counts_thin == 0 ) < .5)
counts_thin <- counts_thin[keep_genes, keep_samples]
libsize <- libsize[keep_samples]
log2cpm_thin <- t(log2(1+(10^6)*t(counts_thin)/libsize))
dimnames(log2cpm_thin) <- dimnames(counts_thin)
log2cpm_thin_quant <- do.call(rbind,
lapply(1:nrow(log2cpm_thin), function(i) {
qqnorm(log2cpm_thin[i,], plot.it = F )$x }) )
dimnames(log2cpm_thin_quant) <- dimnames(counts_thin)
saveRDS(list(counts_thin=counts_thin,
log2cpm_thin= log2cpm_thin,
log2cpm_thin_quant=log2cpm_thin_quant),
file=file.path(paste0("data/data_training_test/data_thinlog2_",
sprintf("%03d", 100*thinlog2_rate),".rds")))
pdata_thin <- pdata[keep_samples,]
for (i in 1:length(unique(pdata$chip_id))) {
ind <- unique(pdata_thin$chip_id)[i]
ii_test <- c(1:nrow(pdata_thin))[which(pdata_thin$chip_id == ind)]
counts_test <- counts[,ii_test]
counts_thin_test <- counts_thin[,ii_test]
log2cpm_thin_test <- log2cpm_thin[,ii_test]
log2cpm_thin_quant_test <- log2cpm_thin_quant[,ii_test]
data_thin_test <- list(counts_test=counts_test,
counts_thin_test=counts_thin_test,
log2cpm_thin_test=log2cpm_thin_test,
log2cpm_thin_quant_test=log2cpm_thin_quant_test)
saveRDS(data_thin_test,
file=file.path(paste0("data/data_training_test/ind_",ind,"_data_test_thinlog2_",
sprintf("%03d", 100*thinlog2_rate),".rds")))
}
}
makedata_thinned(sce, thinlog2_rate = .8)
makedata_thinned(sce, thinlog2_rate = .33)Library size
data_thinlog2_033 <- readRDS("data/data_training_test/data_thinlog2_033.rds")
data_thinlog2_080 <- readRDS("data/data_training_test/data_thinlog2_080.rds")
# thin library size by a factor of 4.24
mean(colSums(data_thinlog2_033$counts_thin))/mean(colSums(counts))
mean(colSums(counts)/(1/.23))
mean(colSums(data_thinlog2_033$counts_thin))
1/.23
# thin library size by a factor of 2.2
mean(colSums(data_thinlog2_080$counts_thin))/mean(colSums(counts))
mean(colSums(counts)/(1/.45))
mean(colSums(data_thinlog2_080$counts_thin))
1/.45Number of genes detected
top5genes <- c("ENSG00000170312","ENSG00000175063",
"ENSG00000131747", "ENSG00000198518", "ENSG00000197061")
inds <- unique(pdata$chip_id)
do.call(rbind, lapply(1:length(inds), function(i) {
ind <- inds[i]
samps <- rownames(pdata)[which(pdata$chip_id == ind)]
exp_tmp <- data_thinlog2_080$counts_thin[,colnames(data_thinlog2_080$counts_thin) %in% samps]
data.frame(ind = ind,
g_detect = min(rowMeans(exp_tmp > 0)),
g_detect_top50 = mean(rowMeans(exp_tmp[which(rownames(exp_tmp) %in% top5genes),] > 0)))
}))
do.call(rbind, lapply(1:length(inds), function(i) {
ind <- inds[i]
samps <- rownames(pdata)[which(pdata$chip_id == ind)]
exp_tmp <- data_thinlog2_033$counts_thin[,colnames(data_thinlog2_033$counts_thin) %in% samps]
data.frame(ind = ind,
g_detect = min(rowMeans(exp_tmp > 0)))
}))
do.call(rbind, lapply(1:length(inds), function(i) {
ind <- inds[i]
samps <- rownames(pdata)[which(pdata$chip_id == ind)]
exp_tmp <- counts[,colnames(counts) %in% samps]
data.frame(ind = ind,
g_detect = min(rowMeans(exp_tmp > 0)))
}))Plot out properties of thinned data
# checking...
thing_080 <- readRDS("data/data_training_test/data_thinlog2_080.rds")
thing_033 <- readRDS("data/data_training_test/data_thinlog2_033.rds")
subsam <- do.call(rbind, list(data.frame(libsize=colSums(counts), thinlog2="1"),
data.frame(libsize=colSums(thing_080$counts_thin), thinlog2="080"),
data.frame(libsize=colSums(thing_033$counts_thin), thinlog2="033")))
subsam$thinlog2 <- factor(subsam$thinlog2,
levels=c("033", "080", "1"),
labels=c("4.4", "2.2", "1 (none)"))
ggplot(subsam, aes(x=thinlog2, y=libsize, group=thinlog2, fill=thinlog2)) +
geom_violin() +
geom_boxplot(width=.2, col="black") +
labs(fill="Thinning factor") +
ylab("Sample molecule count") +
xlab("Thinning factor")
subsam %>% group_by(thinlog2) %>% summarize(mn = mean(libsize), sd = sd(libsize))fits_all <- readRDS("data/fit.quant.rds")
genes_all <- names(fits_all)[order(sapply(fits_all,"[[",3), decreasing=T)]
res_unthinned <- do.call(rbind, lapply(seq_along(unique(pdata$chip_id)), function(ind) {
ind <- unique(pdata$chip_id)[i]
res_thin_each <- do.call(rbind, lapply(2:50, function(ngenes) {
data_test <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_test.rds"))
data_train <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_training.rds"))
which_genes <- genes_all[1:ngenes]
fit_train <- cycle_npreg_insample(
Y = with(data_train,
log2cpm_quant_train[which(rownames(log2cpm_quant_train) %in% which_genes), ]),
theta = with(data_train, theta_train))
fit_test <- cycle_npreg_outsample(
Y_test=with(data_test,
log2cpm_quant_test[which(rownames(log2cpm_quant_test) %in% which_genes), ]),
sigma_est=with(fit_train, sigma_est),
funs_est=with(fit_train, funs_est))
diff_time <- circ_dist(data_test$theta_test,
rotation(data_test$theta_test, fit_test$cell_times_est))
out <- data.frame(phase_pred_rot=rotation(data_test$theta_test, fit_test$cell_times_est),
phase_ref=data_test$theta_test,
diff_time=diff_time,
ind = ind,
ngenes = ngenes)
return(out)
}) )
}) )
res_unthinned %>% group_by(ind, ngenes) %>%
# filter(ngenes <= 20) %>%
summarise(diff_mean = mean(diff_time/2/pi),
diff_se = sd(diff_time/2/pi)/sqrt(length(diff_time/2/pi))) %>%
ggplot(., aes(x=factor(ngenes), y=diff_mean, group = ind)) +
# geom_vline(xintercept=seq(5, 50,5)-1, col="gray90", lty=1) +
geom_hline(yintercept=seq(.1, .2, .01), col="gray90", lty=1) +
geom_line(aes(col=ind), lwd=.7) + # ggtitle("thinlog2 = .80") +
scale_fill_brewer(palette="Dark2") +
geom_errorbar(aes(ymin=diff_mean-diff_se,
ymax=diff_mean+diff_se, col=ind), width=.2, alpha=.5) +
stat_summary(fun.y=mean,geom="line",lwd=.5, group=1) +
ylim(0,.3) + geom_hline(yintercept=.25, col="red") +
labs(color="Test data (Cell line)") +
xlab("Number of cyclic genes used in peco prediction") +
ylab("Prediction error (% circle)") +
scale_x_discrete(breaks=c(2:50),
labels=c(rep("",3),5, rep("",4), 10, rep("",4), 15,
rep("",4), 20, rep("",4), 25,
rep("",4), 30, rep("",4), 35,
rep("",4), 40, rep("",4), 45, rep("",4), 50)) +
ggtitle("Performance in unthinned data")Use peco predictors trained on unthinned data of samples from 5 individual cell lines to predict cell cycle phase in thinned data of samples from an individual not seen in training.
fits_all <- readRDS("data/fit.quant.rds")
genes_all <- names(fits_all)[order(sapply(fits_all,"[[",3), decreasing=T)]
res_thin_080 <- do.call(rbind, lapply(seq_along(unique(pdata$chip_id)), function(ind) {
ind <- unique(pdata$chip_id)[i]
res_thin_each <- do.call(rbind, lapply(2:50, function(ngenes) {
data_test <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_test.rds"))
data_train <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_training.rds"))
which_genes <- genes_all[1:ngenes]
fit_train <- cycle_npreg_insample(
Y = with(data_train,
log2cpm_quant_train[which(rownames(log2cpm_quant_train) %in% which_genes), ]),
theta = with(data_train, theta_train))
data_thin_test_080 <- readRDS(paste0("data/data_training_test/ind_",
ind, "_data_test_thinlog2_080.rds"))
fit_test_080 <- cycle_npreg_outsample(
Y_test=with(data_thin_test_080,
log2cpm_thin_quant_test[which(rownames(log2cpm_thin_quant_test) %in% which_genes), ]),
sigma_est=with(fit_train, sigma_est),
funs_est=with(fit_train, funs_est))
diff_time <- circ_dist(data_test$theta_test,
rotation(data_test$theta_test, fit_test_080$cell_times_est))
out <- data.frame(phase_pred_rot=rotation(data_test$theta_test, fit_test$cell_times_est),
phase_ref=data_test$theta_test,
diff_time=diff_time,
ind = ind,
ngenes = ngenes)
return(out)
}) )
}) )
res_thin_033 %>% group_by(ind, ngenes) %>%
# filter(ngenes <= 20) %>%
summarise(diff_mean = mean(diff_time/2/pi),
diff_se = sd(diff_time/2/pi)/sqrt(length(diff_time/2/pi))) %>%
ggplot(., aes(x=factor(ngenes), y=diff_mean, group = ind)) +
# geom_vline(xintercept=seq(5, 50,5)-1, col="gray90", lty=1) +
geom_hline(yintercept=seq(.1, .2, .01), col="gray90", lty=1) +
geom_line(aes(col=ind), lwd=.7) + # ggtitle("thinlog2 = .80") +
scale_fill_brewer(palette="Dark2") +
geom_errorbar(aes(ymin=diff_mean-diff_se,
ymax=diff_mean+diff_se, col=ind), width=.2, alpha=.5) +
stat_summary(fun.y=mean,geom="line",lwd=.5, group=1) +
ylim(0,.3) + geom_hline(yintercept=.25, col="red") +
labs(color="Test data (Cell line)") +
xlab("Number of cyclic genes used in peco prediction") +
ylab("Prediction error (% circle)") +
scale_x_discrete(breaks=c(2:50),
labels=c(rep("",3),5, rep("",4), 10, rep("",4), 15,
rep("",4), 20, rep("",4), 25,
rep("",4), 30, rep("",4), 35,
rep("",4), 40, rep("",4), 45, rep("",4), 50)) +
ggtitle("Performance in data thinned by a factor of 2.2")
res_thin_033 <- do.call(rbind, lapply(seq_along(unique(pdata$chip_id)), function(ind) {
ind <- unique(pdata$chip_id)[i]
res_thin_each <- do.call(rbind, lapply(2:50, function(ngenes) {
data_test <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_test.rds"))
data_train <- readRDS(paste0("data/data_training_test/ind_",ind,"_data_training.rds"))
which_genes <- genes_all[1:ngenes]
fit_train <- cycle_npreg_insample(
Y = with(data_train,
log2cpm_quant_train[which(rownames(log2cpm_quant_train) %in% which_genes), ]),
theta = with(data_train, theta_train))
data_thin_test_033 <- readRDS(paste0("data/data_training_test/ind_",
ind, "_data_test_thinlog2_033.rds"))
fit_test_033 <- cycle_npreg_outsample(
Y_test=with(data_thin_test_033,
log2cpm_thin_quant_test[which(rownames(log2cpm_thin_quant_test) %in% which_genes), ]),
sigma_est=with(fit_train, sigma_est),
funs_est=with(fit_train, funs_est))
diff_time <- circ_dist(data_test$theta_test,
rotation(data_test$theta_test, fit_test_033$cell_times_est))
out <- data.frame(phase_pred_rot=rotation(data_test$theta_test, fit_test$cell_times_est),
phase_ref=data_test$theta_test,
diff_time=diff_time,
ind = ind,
ngenes = ngenes)
return(out)
}) )
}) )
res_thin_033 %>% group_by(ind, ngenes) %>%
# filter(ngenes <= 20) %>%
summarise(diff_mean = mean(diff_time/2/pi),
diff_se = sd(diff_time/2/pi)/sqrt(length(diff_time/2/pi))) %>%
ggplot(., aes(x=factor(ngenes), y=diff_mean, group = ind)) +
# geom_vline(xintercept=seq(5, 50,5)-1, col="gray90", lty=1) +
geom_hline(yintercept=seq(.1, .2, .01), col="gray90", lty=1) +
geom_line(aes(col=ind), lwd=.7) + # ggtitle("thinlog2 = .80") +
scale_fill_brewer(palette="Dark2") +
geom_errorbar(aes(ymin=diff_mean-diff_se,
ymax=diff_mean+diff_se, col=ind), width=.2, alpha=.5) +
stat_summary(fun.y=mean,geom="line",lwd=.5, group=1) +
ylim(0,.3) + geom_hline(yintercept=.25, col="red") +
labs(color="Test data (Cell line)") +
xlab("Number of cyclic genes used in peco prediction") +
ylab("Prediction error (% circle)") +
scale_x_discrete(breaks=c(2:50),
labels=c(rep("",3),5, rep("",4), 10, rep("",4), 15,
rep("",4), 20, rep("",4), 25,
rep("",4), 30, rep("",4), 35,
rep("",4), 40, rep("",4), 45, rep("",4), 50)) +
ggtitle("Performance in data thinned by a factor of 4.3")sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_0.8.0.1 ggplot2_3.2.1
[3] peco_0.99.6 SingleCellExperiment_1.4.1
[5] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
[7] BiocParallel_1.16.0 matrixStats_0.55.0
[9] Biobase_2.42.0 GenomicRanges_1.34.0
[11] GenomeInfoDb_1.18.1 IRanges_2.16.0
[13] S4Vectors_0.20.1 BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] viridis_0.5.1 genlasso_1.4
[3] viridisLite_0.3.0 foreach_1.4.4
[5] DelayedMatrixStats_1.4.0 assertthat_0.2.1
[7] vipor_0.4.5 GenomeInfoDbData_1.2.0
[9] yaml_2.2.0 pillar_1.3.1
[11] backports_1.1.2 lattice_0.20-38
[13] glue_1.3.0 digest_0.6.20
[15] promises_1.0.1 XVector_0.22.0
[17] colorspace_1.3-2 plyr_1.8.4
[19] htmltools_0.3.6 httpuv_1.4.5
[21] Matrix_1.2-17 pkgconfig_2.0.3
[23] zlibbioc_1.28.0 purrr_0.3.2
[25] mvtnorm_1.0-11 scales_1.0.0
[27] HDF5Array_1.10.1 whisker_0.3-2
[29] later_0.7.5 pracma_2.2.9
[31] git2r_0.26.1 tibble_2.1.1
[33] conicfit_1.0.4 withr_2.1.2
[35] lazyeval_0.2.1 magrittr_1.5
[37] crayon_1.3.4 evaluate_0.12
[39] fs_1.3.1 doParallel_1.0.14
[41] MASS_7.3-51.1 beeswarm_0.2.3
[43] geigen_2.3 tools_3.5.1
[45] scater_1.10.1 stringr_1.3.1
[47] Rhdf5lib_1.4.3 munsell_0.5.0
[49] compiler_3.5.1 rlang_0.4.0
[51] rhdf5_2.26.2 grid_3.5.1
[53] RCurl_1.95-4.11 iterators_1.0.12
[55] circular_0.4-93 igraph_1.2.2
[57] bitops_1.0-6 rmarkdown_1.10
[59] boot_1.3-20 gtable_0.2.0
[61] codetools_0.2-15 reshape2_1.4.3
[63] R6_2.4.0 gridExtra_2.3
[65] knitr_1.20 workflowr_1.6.0
[67] rprojroot_1.3-2 stringi_1.2.4
[69] ggbeeswarm_0.6.0 Rcpp_1.0.3
[71] tidyselect_0.2.5
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_0.8.0.1 ggplot2_3.2.1
[3] peco_0.99.6 SingleCellExperiment_1.4.1
[5] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
[7] BiocParallel_1.16.0 matrixStats_0.55.0
[9] Biobase_2.42.0 GenomicRanges_1.34.0
[11] GenomeInfoDb_1.18.1 IRanges_2.16.0
[13] S4Vectors_0.20.1 BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] viridis_0.5.1 genlasso_1.4
[3] viridisLite_0.3.0 foreach_1.4.4
[5] DelayedMatrixStats_1.4.0 assertthat_0.2.1
[7] vipor_0.4.5 GenomeInfoDbData_1.2.0
[9] yaml_2.2.0 pillar_1.3.1
[11] backports_1.1.2 lattice_0.20-38
[13] glue_1.3.0 digest_0.6.20
[15] promises_1.0.1 XVector_0.22.0
[17] colorspace_1.3-2 plyr_1.8.4
[19] htmltools_0.3.6 httpuv_1.4.5
[21] Matrix_1.2-17 pkgconfig_2.0.3
[23] zlibbioc_1.28.0 purrr_0.3.2
[25] mvtnorm_1.0-11 scales_1.0.0
[27] HDF5Array_1.10.1 whisker_0.3-2
[29] later_0.7.5 pracma_2.2.9
[31] git2r_0.26.1 tibble_2.1.1
[33] conicfit_1.0.4 withr_2.1.2
[35] lazyeval_0.2.1 magrittr_1.5
[37] crayon_1.3.4 evaluate_0.12
[39] fs_1.3.1 doParallel_1.0.14
[41] MASS_7.3-51.1 beeswarm_0.2.3
[43] geigen_2.3 tools_3.5.1
[45] scater_1.10.1 stringr_1.3.1
[47] Rhdf5lib_1.4.3 munsell_0.5.0
[49] compiler_3.5.1 rlang_0.4.0
[51] rhdf5_2.26.2 grid_3.5.1
[53] RCurl_1.95-4.11 iterators_1.0.12
[55] circular_0.4-93 igraph_1.2.2
[57] bitops_1.0-6 rmarkdown_1.10
[59] boot_1.3-20 gtable_0.2.0
[61] codetools_0.2-15 reshape2_1.4.3
[63] R6_2.4.0 gridExtra_2.3
[65] knitr_1.20 workflowr_1.6.0
[67] rprojroot_1.3-2 stringi_1.2.4
[69] ggbeeswarm_0.6.0 Rcpp_1.0.3
[71] tidyselect_0.2.5