Clustering Cusanovich et al (2018) data using topic proportions with K = 13 topics
Kaixuan Luo
Last updated: 2020-12-01
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Knit directory: scATACseq-topics/
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| html | 6c97fa9 | kevinlkx | 2020-12-01 | Build site. |
| Rmd | ab321de | kevinlkx | 2020-12-01 | refine kmeans clusters and add cell label distributions |
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| Rmd | c00f12b | kevinlkx | 2020-10-21 | update colors and interpret clusters with tissue labels |
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| Rmd | a4daf6d | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
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| Rmd | 98920ed | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
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| Rmd | f8bea96 | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
Here we explore the structure in the Cusanovich et al (2018) ATAC-seq data inferred from the multinomial topic model with \(k = 13\).
Load packages and some functions used in this analysis.
library(Matrix)
library(dplyr)
library(ggplot2)
library(cowplot)
library(fastTopics)
source("code/plots.R")Load the data. The counts are no longer needed at this stage of the analysis.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
rm(counts)Plot PCs of the topic proportions
We first use PCA to explore the structure inferred from the multinomial topic model with \(k = 13\):
Load the \(k = 13\) Poisson NMF fit.
out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fitPlot PCs of the topic proportions.
p.pca1.1 <- pca_plot(poisson2multinom(fit),pcs = 1:2,fill = "none")
p.pca1.2 <- pca_plot(poisson2multinom(fit),pcs = 3:4,fill = "none")
p.pca1.3 <- pca_plot(poisson2multinom(fit),pcs = 5:6,fill = "none")
p.pca1.4 <- pca_plot(poisson2multinom(fit),pcs = 7:8,fill = "none")
p.pca1.5 <- pca_plot(poisson2multinom(fit),pcs = 9:10,fill = "none")
p.pca1.6 <- pca_plot(poisson2multinom(fit),pcs = 11:12,fill = "none")
plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4,p.pca1.5,p.pca1.6)
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Some of the structure is more evident from “hexbin” plots showing the density of the points.
breaks <- c(0,1,5,10,100,Inf)
p.pca2.1 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 1:2, breaks = breaks) + guides(fill = "none")
p.pca2.2 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 3:4, breaks = breaks) + guides(fill = "none")
p.pca2.3 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 5:6, breaks = breaks) + guides(fill = "none")
p.pca2.4 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 7:8, breaks = breaks) + guides(fill = "none")
p.pca2.5 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 9:10, breaks = breaks) + guides(fill = "none")
p.pca2.6 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 11:12, breaks = breaks) + guides(fill = "none")
plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4,p.pca2.5,p.pca2.6)
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Layer the tissue and cell labels onto the PC plots
Next, we layer the tissue and cell labels onto the PC plots.
PCs 1 and 2:
p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.2 <- labeled_pca_plot(fit,1:2,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.1,p.pca3.2,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 3 and 4:
p.pca3.3 <- labeled_pca_plot(fit,3:4,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.4 <- labeled_pca_plot(fit,3:4,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.3,p.pca3.4,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 5 and 6:
p.pca3.5 <- labeled_pca_plot(fit,5:6,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.6 <- labeled_pca_plot(fit,5:6,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.5,p.pca3.6,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 7 and 8:
p.pca3.7 <- labeled_pca_plot(fit,7:8,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.8 <- labeled_pca_plot(fit,7:8,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.7,p.pca3.8,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 9 and 10:
p.pca3.9 <- labeled_pca_plot(fit,9:10,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.10 <- labeled_pca_plot(fit,9:10,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.9,p.pca3.10,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 11 and 12:
p.pca3.11 <- labeled_pca_plot(fit,11:12,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.12 <- labeled_pca_plot(fit,11:12,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.11,p.pca3.12,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Visualize by structure plot grouped by tissues
set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)
p.structure.1 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = samples[rows, "tissue"],n = Inf,gap = 40,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 42 because original setting of 50 was too large for the number of samples (132)
# Perplexity automatically changed to 40 because original setting of 50 was too large for the number of samples (124)
print(p.structure.1)
| Version | Author | Date |
|---|---|---|
| 6c97fa9 | kevinlkx | 2020-12-01 |
k-means clustering on topic proportions
Define clusters using k-means, and then create structure plot based on the clusters from k-means.
Define clusters using k-means with 15 clusters:
set.seed(10)
clusters.15 <- factor(kmeans(poisson2multinom(fit)$L,centers = 15)$cluster)
print(table(clusters.15))
# clusters.15
# 1 2 3 4 5 6 7 8 9 10 11 12 13
# 2740 3868 3775 3879 3645 3651 12037 2955 6045 12026 6068 3582 9319
# 14 15
# 3411 4172Structure plot based on 15 clusters
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.kmeans.15 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.15[rows],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 42 because original setting of 50 was too large for the number of samples (130)
# Perplexity automatically changed to 46 because original setting of 50 was too large for the number of samples (144)
print(p.structure.kmeans.15)
Refine k-means clusters: - Split cluster 10 into two clusters. - (maybe also merge cluster 4 and 9)
set.seed(10)
clusters.refined <- as.numeric(clusters.15)
idx_cluster10 <- which(clusters.15 == 10)
cluster10.refined <- kmeans(poisson2multinom(fit)$L[idx_cluster10, ],centers = 2)$cluster+15
clusters.refined[idx_cluster10] <- cluster10.refined
clusters.refined <- factor(clusters.refined, labels = paste0("c", c(1:length(unique(clusters.refined)))))
print(table(clusters.refined))
samples$cluster_kmeans <- clusters.refined
# clusters.refined
# c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13
# 2740 3868 3775 3879 3645 3651 12037 2955 6045 6068 3582 9319 3411
# c14 c15 c16
# 4172 6423 5603Structure plot based on refined clusters
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.kmeans.refined <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.refined[rows],n = Inf,gap = 40,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 41 because original setting of 50 was too large for the number of samples (129)
# Perplexity automatically changed to 46 because original setting of 50 was too large for the number of samples (144)
print(p.structure.kmeans.refined)
| Version | Author | Date |
|---|---|---|
| 6c97fa9 | kevinlkx | 2020-12-01 |
Group samples by k-means clusters first, then by tissue labels:
p.structure.kmeans.refined <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.refined[rows],
rows = order(samples[rows, "tissue"]), # samples are grouped by clusters first, then by tissue labels
n = Inf,gap = 40,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
print(p.structure.kmeans.refined)
| Version | Author | Date |
|---|---|---|
| 6c97fa9 | kevinlkx | 2020-12-01 |
The clusters defined by k-means on topic proportions reasonably identify the clusters shown in the PCA hexbin plots (below).
p.pca.4.1 <- labeled_pca_plot(fit,1:2,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.2 <- labeled_pca_plot(fit,3:4,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.3 <- labeled_pca_plot(fit,5:6,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.4 <- labeled_pca_plot(fit,7:8,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.5 <- labeled_pca_plot(fit,9:10,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.6 <- labeled_pca_plot(fit,11:12,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
plot_grid(p.pca.4.1,p.pca.4.2,p.pca.4.3,p.pca.4.4,p.pca.4.5,p.pca.4.6)
plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4,p.pca2.5,p.pca2.6)
We then label the cells in each cluster with the known tissue labels.
Tissues:
samples$tissue <- as.factor(samples$tissue)
cat(length(levels(samples$tissue)), "tissues. \n")
table(samples$tissue)
colors_tissues <- c("darkblue", "darkgray", "red", "springgreen", "brown", "purple", "skyblue", "black",
"darkgreen", "plum", "yellow", "orange", "lightgray")
# 13 tissues.
#
# BoneMarrow Cerebellum Heart Kidney
# 8403 2278 7650 6431
# LargeIntestine Liver Lung PreFrontalCortex
# 7086 6167 9996 5959
# SmallIntestine Spleen Testes Thymus
# 4077 4020 2723 7617
# WholeBrain
# 8766Plot the distribution of tissues by cluster.
Stacked barplot for the counts of tissues by clusters:
library(plyr);library(dplyr)
# ------------------------------------------------------------------------------
# You have loaded plyr after dplyr - this is likely to cause problems.
# If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
# library(plyr); library(dplyr)
# ------------------------------------------------------------------------------
#
# Attaching package: 'plyr'
# The following objects are masked from 'package:dplyr':
#
# arrange, count, desc, failwith, id, mutate, rename, summarise,
# summarize
library(RColorBrewer)
freq_cluster_tissue <- count(samples, vars=c("cluster_kmeans","tissue"))
# stacked barplot for the counts of tissues by clusters
p.barplot.1 <- ggplot(freq_cluster_tissue, aes(fill=tissue, y=freq, x=cluster_kmeans)) +
geom_bar(position="stack", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Number of cells") +
scale_fill_manual(values = colors_tissues) +
guides(fill=guide_legend(ncol=2)) +
theme(
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
print(p.barplot.1)
Percent stacked barplot for the counts of tissues by clusters:
p.barplot.2 <- ggplot(freq_cluster_tissue, aes(fill=tissue, y=freq, x=cluster_kmeans)) +
geom_bar(position="fill", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Proportion of cells") +
scale_fill_manual(values = colors_tissues) +
guides(fill=guide_legend(ncol=2)) +
theme(
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
print(p.barplot.2)
- We can see a few clusters are tissue specific:
- cluster c3 is heart specific;
- cluster c8 is kidney specific;
- cluster c16 is liver specific;
- cluster c1 is primarily bone marrow;
- cluster c12 is primarily thymus.
Some clusters are combinations of related tissues: - cluster c9 is half lung and half spleen; - cluster c5 and c15 are mainly from pre-frontal cortex, whole brain (and cerebellum) -- all neuron related. - cluster c14 is also from whole brain and cerebellum. - cluster c11 is mainly from Kidney, LargeIntestine, and Lung.
Some clusters are more heterogeneous mixtures of different tissues: e.g. c2, c6, c7, c12.
freq_table_cluster_tissue <- with(samples,table(tissue,cluster_kmeans))
print(freq_table_cluster_tissue)
# cluster_kmeans
# tissue c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12
# BoneMarrow 2455 15 0 1399 0 33 1060 0 276 3063 0 102
# Cerebellum 0 51 0 7 525 44 191 0 30 42 0 0
# Heart 0 1374 3768 126 3 1225 803 5 68 259 3 13
# Kidney 0 757 0 47 0 402 98 2950 48 280 1614 19
# LargeIntestine 0 73 1 107 4 665 2778 0 71 172 441 94
# Liver 0 268 0 97 0 84 31 0 39 50 1 34
# Lung 2 1093 5 1324 2 659 1010 0 2678 465 1521 773
# PreFrontalCortex 0 75 0 84 1169 48 118 0 131 49 0 1
# SmallIntestine 0 22 0 75 0 341 2112 0 9 1457 1 27
# Spleen 43 3 0 427 0 5 24 0 2510 116 0 892
# Testes 240 19 0 1 0 37 2379 0 2 38 0 0
# Thymus 0 2 1 82 0 9 32 0 90 30 1 7363
# WholeBrain 0 116 0 103 1942 99 1401 0 93 47 0 1
# cluster_kmeans
# tissue c13 c14 c15 c16
# BoneMarrow 0 0 0 0
# Cerebellum 15 1177 196 0
# Heart 3 0 0 0
# Kidney 215 0 0 1
# LargeIntestine 2680 0 0 0
# Liver 4 1 0 5558
# Lung 434 2 2 26
# PreFrontalCortex 19 4 4261 0
# SmallIntestine 18 0 0 15
# Spleen 0 0 0 0
# Testes 4 0 0 3
# Thymus 7 0 0 0
# WholeBrain 12 2988 1964 0Plot the distribution of cell labels by cluster.
Stacked barplot for the counts of cell labels by clusters:
freq_cluster_celllabel <- count(samples, vars=c("cluster_kmeans","cell_label"))
colors_celllabels <- colorRampPalette(brewer.pal(12, "Set3"))(40)
# stacked barplot for the counts of tissues by clusters
p.barplot.3 <- ggplot(freq_cluster_celllabel, aes(fill=cell_label, y=freq, x=cluster_kmeans)) +
geom_bar(position="stack", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Number of cells") +
scale_fill_manual(values = colors_celllabels) +
guides(fill=guide_legend(ncol=3)) +
theme(
legend.title = element_text(size = 9),
legend.text = element_text(size = 7)
)
print(p.barplot.3)
| Version | Author | Date |
|---|---|---|
| 6c97fa9 | kevinlkx | 2020-12-01 |
Percent stacked barplot for the counts of tissues by clusters:
p.barplot.4 <- ggplot(freq_cluster_celllabel, aes(fill=cell_label, y=freq, x=cluster_kmeans)) +
geom_bar(position="fill", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Proportion of cells") +
scale_fill_manual(values = colors_celllabels) +
guides(fill=guide_legend(ncol=3)) +
theme(
legend.title = element_text(size = 9),
legend.text = element_text(size = 7)
)
print(p.barplot.4)
| Version | Author | Date |
|---|---|---|
| 6c97fa9 | kevinlkx | 2020-12-01 |
freq_table_cluster_celllabel <- with(samples,table(cell_label,cluster_kmeans))
print(freq_table_cluster_celllabel)
# cluster_kmeans
# cell_label c1 c2 c3 c4 c5 c6 c7 c8 c9 c10
# Activated B cells 0 0 0 146 0 0 0 0 331 23
# Alveolar macrophages 0 0 0 322 0 2 16 0 185 34
# Astrocytes 0 0 0 0 1574 11 58 0 0 20
# B cells 0 0 0 470 0 3 648 0 4616 34
# Cardiomyocytes 0 15 3773 1 0 42 169 0 0 76
# Cerebellar granule cells 0 0 0 9 0 3 306 0 0 19
# Collecting duct 0 0 0 0 0 10 0 0 0 5
# Collisions 6 2 0 296 423 15 166 0 44 64
# DCT/CD 0 0 0 0 0 1 0 1 0 6
# Dendritic cells 1 0 0 416 0 2 12 0 424 102
# Distal convoluted tubule 0 0 0 0 0 0 0 0 0 6
# Endothelial I (glomerular) 0 386 0 0 0 158 0 0 0 8
# Endothelial I cells 0 512 0 0 0 438 1 0 0 1
# Endothelial II cells 0 2405 0 15 0 574 7 0 0 15
# Enterocytes 0 0 0 0 1 11 1585 0 0 54
# Erythroblasts 2420 0 0 37 0 0 79 0 0 275
# Ex. neurons CPN 0 0 0 0 0 0 10 0 0 0
# Ex. neurons CThPN 0 0 0 0 0 0 91 0 0 0
# Ex. neurons SCPN 0 0 0 0 0 0 40 0 0 32
# Hematopoietic progenitors 0 0 0 26 0 1 899 0 0 2499
# Hepatocytes 0 0 0 2 0 5 11 0 0 44
# Immature B cells 0 0 0 285 0 0 0 0 68 218
# Inhibitory neurons 0 0 0 3 68 27 754 0 0 8
# Loop of henle 0 1 0 0 0 8 2 1 0 11
# Macrophages 0 0 0 337 0 14 191 0 127 42
# Microglia 0 1 0 159 1 3 31 0 226 1
# Monocytes 69 0 0 1010 0 1 50 0 9 32
# NK cells 0 0 0 231 0 1 2 0 0 16
# Oligodendrocytes 0 0 0 1 1506 1 30 0 0 17
# Podocytes 0 369 0 0 0 113 0 0 0 16
# Proximal tubule 0 1 0 1 0 16 33 2361 0 132
# Proximal tubule S3 0 0 0 0 0 0 2 592 0 0
# Purkinje cells 0 0 0 0 1 2 50 0 0 0
# Regulatory T cells 3 0 0 17 0 1 7 0 0 4
# SOM+ Interneurons 0 0 0 0 7 8 66 0 0 5
# Sperm 240 0 0 0 0 1 1814 0 0 32
# T cells 1 1 0 35 1 7 104 0 0 50
# Type I pneumocytes 0 0 0 1 0 3 0 0 0 27
# Type II pneumocytes 0 8 0 0 0 14 0 0 0 1
# Unknown 0 167 2 59 63 2155 4803 0 15 2139
# cluster_kmeans
# cell_label c11 c12 c13 c14 c15 c16
# Activated B cells 0 0 0 0 0 0
# Alveolar macrophages 0 0 0 0 0 0
# Astrocytes 0 0 0 0 3 0
# B cells 0 0 1 0 0 0
# Cardiomyocytes 0 0 0 0 0 0
# Cerebellar granule cells 0 0 0 3762 0 0
# Collecting duct 124 0 25 0 0 0
# Collisions 0 11 8 97 86 0
# DCT/CD 441 0 57 0 0 0
# Dendritic cells 0 0 1 0 0 0
# Distal convoluted tubule 301 0 12 0 0 0
# Endothelial I (glomerular) 0 0 0 0 0 0
# Endothelial I cells 0 0 0 0 0 0
# Endothelial II cells 0 1 1 1 0 0
# Enterocytes 442 0 2690 0 0 0
# Erythroblasts 0 0 0 0 0 0
# Ex. neurons CPN 0 0 0 0 1822 0
# Ex. neurons CThPN 0 0 0 0 1449 0
# Ex. neurons SCPN 0 0 0 0 1394 0
# Hematopoietic progenitors 0 0 0 0 0 0
# Hepatocytes 0 0 0 0 0 5602
# Immature B cells 0 0 0 0 0 0
# Inhibitory neurons 0 0 0 3 965 0
# Loop of henle 718 0 74 0 0 0
# Macrophages 0 0 0 0 0 0
# Microglia 0 0 0 0 0 0
# Monocytes 0 2 0 0 0 0
# NK cells 0 71 0 0 0 0
# Oligodendrocytes 0 0 0 0 3 0
# Podocytes 0 0 0 0 0 0
# Proximal tubule 7 0 19 0 0 0
# Proximal tubule S3 0 0 0 0 0 0
# Purkinje cells 0 0 0 262 5 0
# Regulatory T cells 0 475 0 0 0 0
# SOM+ Interneurons 0 0 0 1 466 0
# Sperm 0 0 2 0 0 0
# T cells 0 8753 2 0 0 0
# Type I pneumocytes 1415 6 170 0 0 0
# Type II pneumocytes 66 0 68 0 0 0
# Unknown 68 0 281 46 230 1Save the clustering results to an RDS file
saveRDS(samples, "output/clustering-Cusanovich2018.rds")
sessionInfo()
# R version 3.6.1 (2019-07-05)
# 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] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] RColorBrewer_1.1-2 plyr_1.8.6 fastTopics_0.3-180 cowplot_1.1.0
# [5] ggplot2_3.3.2 dplyr_1.0.2 Matrix_1.2-18 workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.8.2 Rcpp_1.0.5 lattice_0.20-41 tidyr_1.1.2
# [5] prettyunits_1.1.1 rprojroot_1.3-2 digest_0.6.27 R6_2.5.0
# [9] backports_1.2.0 MatrixModels_0.4-1 evaluate_0.14 coda_0.19-3
# [13] httr_1.4.2 pillar_1.4.7 rlang_0.4.8 progress_1.2.2
# [17] lazyeval_0.2.2 data.table_1.13.2 irlba_2.3.3 SparseM_1.77
# [21] whisker_0.4 hexbin_1.28.1 rmarkdown_2.5 labeling_0.4.2
# [25] Rtsne_0.15 stringr_1.4.0 htmlwidgets_1.5.2 munsell_0.5.0
# [29] compiler_3.6.1 httpuv_1.5.4 xfun_0.19 pkgconfig_2.0.3
# [33] mcmc_0.9-7 htmltools_0.5.0 tidyselect_1.1.0 tibble_3.0.4
# [37] quadprog_1.5-7 viridisLite_0.3.0 crayon_1.3.4 withr_2.3.0
# [41] later_1.1.0.1 MASS_7.3-53 grid_3.6.1 jsonlite_1.6
# [45] gtable_0.3.0 lifecycle_0.2.0 git2r_0.27.1 magrittr_1.5
# [49] scales_1.1.1 RcppParallel_5.0.2 stringi_1.5.3 farver_2.0.3
# [53] fs_1.3.1 promises_1.1.1 ellipsis_0.3.1 generics_0.0.2
# [57] vctrs_0.3.5 tools_3.6.1 glue_1.4.2 purrr_0.3.4
# [61] hms_0.5.3 yaml_2.2.0 colorspace_2.0-0 plotly_4.9.0
# [65] knitr_1.30 quantreg_5.41 MCMCpack_1.4-4