Perform differential expression analysis on topics and clusters identified in mixture of FACS-purified PBMC data
Peter Carbonetto
Last updated: 2021-02-10
Checks: 6 1
Knit directory: single-cell-topics/analysis/
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- diff-count-analysis-clusters
- diff-count-analysis-subpop
- diff-count-analysis-topics
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 13fedd9 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | f8e1d99 | Peter Carbonetto | 2021-02-10 | Re-built plots_purified_pbmc page. |
| Rmd | 3e298d4 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | d1d4185 | Peter Carbonetto | 2021-02-10 | Re-built plots_purified_pbmc with interactive volcano plots. |
| Rmd | 676e053 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 52d16f3 | Peter Carbonetto | 2021-02-10 | Created topic volcano plots for paper in plots_purified_pbmc analysis. |
| html | f596d8a | Peter Carbonetto | 2021-02-09 | Made further refinements to the volcano plots in the |
| Rmd | cf1e3d2 | Peter Carbonetto | 2021-02-09 | Added script purified_pbmc_k7.R. |
| Rmd | dd1d2ca | Peter Carbonetto | 2021-02-09 | Working on volcano plots in plots_purified_pbmc analysis. |
| html | d0775d2 | Peter Carbonetto | 2021-02-09 | Re-built plots_purified_pbmc page after making improvements to the |
| Rmd | aecd700 | Peter Carbonetto | 2021-02-09 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 1f0eb8c | Peter Carbonetto | 2021-02-08 | More improvements to volcano plots in plots_purified_pbmc.Rmd. |
| Rmd | be27738 | Peter Carbonetto | 2021-02-08 | Improved volcano plots for FACS cell populations in plots_purified_pbmc.Rmd. |
| html | ab3eeb8 | Peter Carbonetto | 2021-01-29 | Build site. |
| Rmd | 0b73397 | Peter Carbonetto | 2021-01-29 | Added more volcano plots to plots_purified_pbmc analysis. |
| html | 42ebc62 | Peter Carbonetto | 2021-01-29 | Added volcano plots for FACS populations in plots_purified_pbmc |
| Rmd | 4edefd0 | Peter Carbonetto | 2021-01-29 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| Rmd | 01c1139 | Peter Carbonetto | 2021-01-29 | Adding new code for volcano plots in plots_purified_pbmc analysis. |
| html | a4bc59b | Peter Carbonetto | 2021-01-06 | Re-built plots_purified_pbmc page after removing cache. |
| Rmd | b92d4db | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| html | a0b6c2b | Peter Carbonetto | 2021-01-06 | Build site. |
| Rmd | ed8a595 | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | 0411340 | Peter Carbonetto | 2021-01-06 | Added scatterplots to plots_purified_pbmc analysis. |
| Rmd | e16bf80 | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| Rmd | a12c42a | Peter Carbonetto | 2021-01-05 | Implemented function lfc_scatterplot in functions_for_plots_purified_pbmc.R. |
| Rmd | 9fd0455 | Peter Carbonetto | 2021-01-05 | Added steps to save volcano plots in plots_purified_pbmc analysis. |
| html | fad8e3d | Peter Carbonetto | 2021-01-05 | First build of plots_purified_pbmc page. |
| Rmd | bf07930 | Peter Carbonetto | 2021-01-05 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 40c2d84 | Peter Carbonetto | 2021-01-04 | Working on log-fold change scatterplots in plots_purified_pbmc analysis. |
| Rmd | e437ddf | Peter Carbonetto | 2021-01-04 | Working on volcano plots in plots_purified_pbmc analysis. |
Here we perform a differential expression analysis using the topic model fit to the mixture of FACS-purified data, as well as the clusters identified from this topic model.
Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(ggrepel)
library(cowplot)
source("../code/functions_for_plots_purified_pbmc.R")Load the count data, the \(K = 6\) topic model fit, and the 7 clusters identified in the clustering analysis.
load("../data/pbmc_purified.RData")
fit <- readRDS(file.path("../output/pbmc-purified/rds",
"fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")Perform differential expression analysis using the FACS labeling:
celltype <- as.character(samples$celltype)
celltype[celltype == "CD4+/CD45RA+/CD25- Naive T" |
celltype == "CD4+/CD45RO+ Memory" |
celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
celltype == "CD4+ T Helper2" |
celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)
table(celltype)
diff_count_facs <- diff_count_clusters(celltype,counts)
# celltype
# CD14+ Monocyte CD19+ B CD34+ CD56+ NK
# 2612 10085 9232 8385
# CD8+ Cytotoxic T T cell
# 10209 54132
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
These volcano plots summarize the results of the differential expression analysis using the FACS labeling:
p1 <- volcano_plot(diff_count_facs,"CD19+ B",genes$symbol,
label_above_quantile = 0.9995,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("B cells")
p2 <- volcano_plot(diff_count_facs,"CD14+ Monocyte",genes$symbol,
label_above_quantile = 0.9995,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD14+ cells")
p3 <- volcano_plot(diff_count_facs,"CD34+",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD34+ cells")
p4 <- volcano_plot(diff_count_facs,"CD56+ NK",genes$symbol,
label_above_quantile = 0.9995,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("NK cells")
p5 <- volcano_plot(diff_count_facs,"CD8+ Cytotoxic T",genes$symbol,
label_above_quantile = 0.9995,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD8+ T cells")
p6 <- volcano_plot(diff_count_facs,"T cell",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("T cells")
plot_grid(p1,p2,p3,p4,p5,p6,nrow = 2,ncol = 3)
| Version | Author | Date |
|---|---|---|
| f8e1d99 | Peter Carbonetto | 2021-02-10 |
| d1d4185 | Peter Carbonetto | 2021-02-10 |
| f596d8a | Peter Carbonetto | 2021-02-09 |
| d0775d2 | Peter Carbonetto | 2021-02-09 |
| ab3eeb8 | Peter Carbonetto | 2021-01-29 |
| 42ebc62 | Peter Carbonetto | 2021-01-29 |
| a4bc59b | Peter Carbonetto | 2021-01-06 |
| a0b6c2b | Peter Carbonetto | 2021-01-06 |
| 0411340 | Peter Carbonetto | 2021-01-06 |
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Perform differential expression analysis using the clusters:
table(samples$cluster)
diff_count_clusters <- diff_count_clusters(samples$cluster,counts)
#
# B CD14+ CD34+ CD8+ dendritic NK T
# 10439 2956 8237 3757 308 8380 60578
# Fitting 21952 x 7 = 153664 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
These volcano plots summarize the results of the differential expression analysis using the clusters:
p7 <- volcano_plot(diff_count_clusters,"B",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("B cells")
# 10134 out of 18417 data points will be included in plot
p8 <- volcano_plot(diff_count_clusters,"CD14+",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD14+ cells")
# 10130 out of 18417 data points will be included in plot
p9 <- volcano_plot(diff_count_clusters,"CD34+",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD34+ cells")
# 10130 out of 18417 data points will be included in plot
p10 <- volcano_plot(diff_count_clusters,"dendritic",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("dendritic cells")
# 10130 out of 18417 data points will be included in plot
p11 <- volcano_plot(diff_count_clusters,"NK",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("NK cells")
# 10130 out of 18417 data points will be included in plot
p12 <- volcano_plot(diff_count_clusters,"CD8+",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("CD8+ T cells")
# 10130 out of 18417 data points will be included in plot
p13 <- volcano_plot(diff_count_clusters,"T",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("T cells")
# 10131 out of 18417 data points will be included in plot
plot_grid(p7,p8,p9,p10,p11,p12,p13,nrow = 3,ncol = 3)
| Version | Author | Date |
|---|---|---|
| f8e1d99 | Peter Carbonetto | 2021-02-10 |
| d1d4185 | Peter Carbonetto | 2021-02-10 |
| f596d8a | Peter Carbonetto | 2021-02-09 |
| d0775d2 | Peter Carbonetto | 2021-02-09 |
| ab3eeb8 | Peter Carbonetto | 2021-01-29 |
| 42ebc62 | Peter Carbonetto | 2021-01-29 |
| a4bc59b | Peter Carbonetto | 2021-01-06 |
| a0b6c2b | Peter Carbonetto | 2021-01-06 |
| 0411340 | Peter Carbonetto | 2021-01-06 |
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Perform differential expression analysis using the multinomial topic model, after removing the dendritic cells cluster:
rows <- which(samples$cluster != "dendritic")
fit_no_dendritic <- select_loadings(fit,loadings = rows)
diff_count_topics <- diff_count_analysis(fit_no_dendritic,counts[rows,])
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
These volcano plots summarize the results of the differential expression analysis using the topic model:
p14 <- volcano_plot(diff_count_topics,"k3",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 3 (B cells)")
p15 <- volcano_plot(diff_count_topics,"k2",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 2 (CD14+ cells)")
p16 <- volcano_plot(diff_count_topics,"k5",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 5 (CD34+ cells)")
p17 <- volcano_plot(diff_count_topics,"k4",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 4 (NK cells)")
p18 <- volcano_plot(diff_count_topics,"k1",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 1 (T cells)")
p19 <- volcano_plot(diff_count_topics,"k6",genes$symbol,
label_above_quantile = 0.999,
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5) +
guides(fill = "none") +
ggtitle("topic 6 (ribosomal proteins)")
plot_grid(p14,p15,p16,p17,p18,p19,nrow = 2,ncol = 3)
The results of the differential expression analysis can also be browsed in interactive volcano plots:
volcano_plotly(diff_count_topics,"k3",
"volcano_plot_purified_pbmc_bcells.html",
genes$symbol,title = "topic 3 (B cells)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_topics,"k2",
"volcano_plot_purified_pbmc_cd14.html",
genes$symbol,title = "topic 2 (CD14+)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_topics,"k5",
"volcano_plot_purified_pbmc_cd34.html",
genes$symbol,title = "topic 5 (CD34+)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_topics,"k4",
"volcano_plot_purified_pbmc_nk.html",
genes$symbol,title = "topic 4 (NK cells)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_topics,"k1",
"volcano_plot_purified_pbmc_tcells.html",
genes$symbol,title = "topic 1 (T cells)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_topics,"k6",
"volcano_plot_purified_pbmc_ribosomal_proteins.html",
genes$symbol,title = "topic 6 (ribosomal proteins)",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10103 out of 18367 data points will be included in plot
volcano_plotly(diff_count_clusters,"CD8+",
"volcano_plot_purified_pbmc_cd8.html",
genes$symbol,title = "CD8+ cluster",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10130 out of 18417 data points will be included in plot
volcano_plotly(diff_count_clusters,"dendritic",
"volcano_plot_purified_pbmc_dendritic.html",
genes$symbol,title = "dendritic cells cluster",
subsample_below_quantile = 0.5,
filter_low_counts = 5e-5,
width = 450,height = 500)
# 10130 out of 18417 data points will be included in plotThe interactive volcano plots mayalso be viewed by clicking these links: topic 3 (B cells), topic 4 (NK cells), topic 2 (CD14+), topic 5 (CD34+), topics 1 (T cells), topics 6 (T cells), CD8+ cluster and dendritic cells cluster.
The volcano plot for topic 6 suggests an enrichment of ribosomal protein genes. Indeed, there is a very strong correlation between the topic 6 mixture proportion and the fraction of total expression attributed to ribosomal genes:
rpgenes <- c("RPS2","RPS3","RPS3A","RPS4X","RPS6","RPS7","RPS8","RPS9",
"RPS10","RPS11","RPS12","RPS13","RPS14","RPS15","RPS15A",
"RPS16","RPS17","RPS18","RPS19","RPS20","RPS21","RPS23",
"RPS24","RPS25","RPS26","RPS27","RPS27A","RPS28","RPS29",
"RPL3","RPL4","RPL5","RPL6","RPL7A","RPL8","RPL9","RPL10",
"RPL10A","RPL12","RPL13A","RPL14","RPL15","RPL17","RPL18",
"RPL18A","RPL19","RPL21","RPL22","RPL23","RPL23A","RPL24",
"RPL26","RPL27A","RPL30","RPL31","RPL32","RPL34","RPL35",
"RPL36","RPL36A","RPL37","RPL39","RPL41")
rgscatterplot <- function (i, title = NULL) {
j <- which(is.element(genes$symbol,rpgenes))
pdat <- data.frame(x = fit$L[i,6],
y = rowSums(counts[i,j])/rowSums(counts[i,]))
return(ggplot(pdat,aes(x = x,y = y)) +
geom_point() +
geom_smooth(method = "lm",se = FALSE,color = "dodgerblue",
size = 0.5,linetype = "dashed") +
ylim(0,0.6) +
labs(x = "topic 6 proportion",
y = "ribosomal expression",
title = title) +
theme_cowplot(font_size = 10) +
theme(plot.title = element_text(size = 10,face = "plain")))
}
p20 <- rgscatterplot(which(celltype == "CD19+ B"),"B cells")
p21 <- rgscatterplot(which(celltype == "CD34+"),"CD34+ cells")
p22 <- rgscatterplot(which(!(celltype == "CD19+ B" | celltype == "CD34+")),
"all other cells")
plot_grid(p20,p21,p22,nrow = 1,ncol = 3)
The list of ribosomal protein genes is from Yoshihama et al (2002).
p10 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD19+ B","k3",
genes$symbol,label_above_quantile = 0.998,
xlab = "B cells FACS subpopulation",ylab = "topic 3")
p11 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD56+ NK","k4",
genes$symbol,label_above_quantile = 0.998,
xlab = "NK cells FACS subpopulation",ylab = "topic 4")
p12 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD14+ Monocyte","k2",
genes$symbol,label_above_quantile = 0.998,
xlab = "CD14+ FACS subpopulation",ylab = "topic 2")
p13 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD34+","k5",
genes$symbol,label_above_quantile = 0.998,
xlab = "CD34+ FACS subpopulation",ylab = "topic 4")
p14 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"T cell","k1+k6",
genes$symbol,label_above_quantile = 0.998,
xlab = "T cells FACS subpopulation",
ylab = "topics 1 + 6")
plot_grid(p10,p11,p12,p13,p14,nrow = 3,ncol = 2)
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] cowplot_1.0.0 ggrepel_0.9.0 ggplot2_3.3.0 fastTopics_0.4-33
# [5] Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] httr_1.4.2 tidyr_1.0.0 splines_3.6.2
# [4] jsonlite_1.6 viridisLite_0.3.0 RcppParallel_4.4.2
# [7] shiny_1.4.0 assertthat_0.2.1 mixsqp_0.3-44
# [10] yaml_2.2.0 progress_1.2.2 pillar_1.4.3
# [13] backports_1.1.5 lattice_0.20-38 quantreg_5.54
# [16] glue_1.3.1 quadprog_1.5-8 digest_0.6.23
# [19] promises_1.1.0 colorspace_1.4-1 htmltools_0.4.0
# [22] httpuv_1.5.2 pkgconfig_2.0.3 invgamma_1.1
# [25] SparseM_1.78 xtable_1.8-4 purrr_0.3.3
# [28] scales_1.1.0 whisker_0.4 later_1.0.0
# [31] Rtsne_0.15 MatrixModels_0.4-1 git2r_0.26.1
# [34] tibble_2.1.3 mgcv_1.8-31 farver_2.0.1
# [37] withr_2.1.2 ashr_2.2-51 lazyeval_0.2.2
# [40] mime_0.8 magrittr_1.5 crayon_1.3.4
# [43] mcmc_0.9-6 evaluate_0.14 fs_1.3.1
# [46] nlme_3.1-142 MASS_7.3-51.4 truncnorm_1.0-8
# [49] tools_3.6.2 data.table_1.12.8 prettyunits_1.1.1
# [52] hms_0.5.2 lifecycle_0.1.0 stringr_1.4.0
# [55] MCMCpack_1.4-5 plotly_4.9.2 munsell_0.5.0
# [58] irlba_2.3.3 compiler_3.6.2 rlang_0.4.5
# [61] grid_3.6.2 htmlwidgets_1.5.1 crosstalk_1.0.0
# [64] labeling_0.3 rmarkdown_2.3 gtable_0.3.0
# [67] R6_2.4.1 knitr_1.26 dplyr_0.8.3
# [70] fastmap_1.0.1 zeallot_0.1.0 workflowr_1.6.2.9000
# [73] rprojroot_1.3-2 stringi_1.4.3 SQUAREM_2017.10-1
# [76] Rcpp_1.0.5 vctrs_0.2.1 tidyselect_0.2.5
# [79] xfun_0.11 coda_0.19-3