Analysis of TCGA RPPA LIHC samples
Jovan Tanevski
2021-08-02
Last updated: 2021-08-02
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a152862 | Jovan Tanevski | 2021-08-02 | add NMF based analysis |
| html | c25dcca | Jovan Tanevski | 2021-08-02 | Build site. |
| Rmd | 21cf743 | Jovan Tanevski | 2021-08-02 | rank normalization, leiden analysis, diff exp |
| html | a386eaa | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | 9ab4acc | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | bd05dbf | Jovan Tanevski | 2021-07-28 | add expression profiles |
| html | e45bf1d | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | dcab1bf | Jovan Tanevski | 2021-07-28 | set figure output to svg |
| html | 33e71a0 | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | bb998b8 | Jovan Tanevski | 2021-07-28 | hclust with factoextra |
| html | 1cb80af | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | d0b6e0f | Jovan Tanevski | 2021-07-28 | add pca to tcga analysis |
| html | d73f586 | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | c4324f5 | Jovan Tanevski | 2021-07-28 | add umap |
| html | 0bfae68 | Jovan Tanevski | 2021-07-28 | Build site. |
| Rmd | 82e3712 | Jovan Tanevski | 2021-07-28 | add basic hclust |
Setup
Load required libraries.
library(tidyverse)
library(skimr)
library(uwot)
library(factoextra)
library(cowplot)
library(FNN)
library(igraph)
library(leiden)
library(limma)
library(NMF)Read filtered TCGA RRPA data and display summary statistics.
tcga.raw <- read_csv("data/TCGA-RPPA-LIHC_selected.csv", col_types = cols()) %>%
select(-TumorType) %>%
column_to_rownames("SampleID")
skim(tcga.raw)| Name | tcga.raw |
| Number of rows | 184 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| P53 | 0 | 1 | -1.13 | 0.46 | -2.54 | -1.40 | -0.96 | -0.76 | -0.51 | ▁▂▂▅▇ |
| AKT_pS473 | 0 | 1 | -0.63 | 0.71 | -2.76 | -1.02 | -0.41 | -0.15 | 0.96 | ▁▂▃▇▁ |
| AKT_pT308 | 0 | 1 | 0.07 | 0.42 | -1.16 | -0.07 | 0.09 | 0.22 | 1.72 | ▁▃▇▁▁ |
| BETACATENIN | 0 | 1 | 1.50 | 0.72 | -1.30 | 1.08 | 1.51 | 1.87 | 3.70 | ▁▁▇▅▁ |
| JNK_pT183Y185 | 0 | 1 | -0.21 | 0.29 | -1.14 | -0.36 | -0.17 | -0.04 | 0.48 | ▁▂▇▇▂ |
| MEK1_pS217S221 | 0 | 1 | -0.17 | 0.36 | -0.82 | -0.35 | -0.23 | -0.10 | 2.58 | ▇▃▁▁▁ |
| P38_pT180Y182 | 0 | 1 | 0.49 | 0.57 | -1.45 | 0.31 | 0.50 | 0.72 | 2.95 | ▁▂▇▁▁ |
| P70S6K_pT389 | 0 | 1 | -1.21 | 0.75 | -3.22 | -1.58 | -0.95 | -0.65 | 1.09 | ▂▃▇▃▁ |
| PDK1_pS241 | 0 | 1 | 0.40 | 0.31 | -0.56 | 0.23 | 0.37 | 0.55 | 1.62 | ▁▇▇▂▁ |
| S6_pS235S236 | 0 | 1 | -0.72 | 0.75 | -3.53 | -1.06 | -0.67 | -0.36 | 1.12 | ▁▁▅▇▂ |
| YAP_pS127 | 0 | 1 | 2.21 | 0.61 | 0.68 | 1.78 | 2.10 | 2.54 | 4.05 | ▁▇▇▂▁ |
| TRANSGLUTAMINASE | 0 | 1 | -0.42 | 0.56 | -1.24 | -0.83 | -0.59 | -0.20 | 2.72 | ▇▃▁▁▁ |
Rank normalize as suggested in https://gdac.broadinstitute.org/runs/analyses__2016_01_28/reports/cancer/LIHC/RPPA_Clustering_CNMF/nozzle.html
tcga <- mutate_all(tcga.raw, rank)Dimensionality reduction
tcga.pca <- prcomp(tcga)
summary(tcga.pca)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 112.2545 66.8808 57.91412 54.79530 52.30290 45.98226
Proportion of Variance 0.3702 0.1314 0.09853 0.08821 0.08036 0.06211
Cumulative Proportion 0.3702 0.5016 0.60012 0.68833 0.76869 0.83081
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 41.5038 35.65454 34.96764 28.14257 23.00992 14.8757
Proportion of Variance 0.0506 0.03735 0.03592 0.02327 0.01555 0.0065
Cumulative Proportion 0.8814 0.91876 0.95468 0.97795 0.99350 1.0000tcga.umap <- umap(tcga, n_neighbors = 5, n_epochs = 1000)Hierarchical clustering
Perform hierarchical clustering of the data and plot the resulting dendrogram
tcga.hclust <- eclust(tcga, "hclust")Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.fviz_dend(tcga.hclust, rect = TRUE)Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.
fviz_gap_stat(tcga.hclust$gap_stat)
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
fviz_silhouette(tcga.hclust) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) cluster size ave.sil.width
1 1 43 0.05
2 2 23 0.25
3 3 17 0.34
4 4 40 0.03
5 5 35 0.02
6 6 12 0.16
7 7 14 0.01
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Plot in 2D PCA and UMAP
fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(tcga.hclust$cluster)) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
tcga.umap.clus <-
tcga.umap %>%
cbind(tcga.hclust$cluster) %>%
`colnames<-`(c("U1", "U2", "Cluster")) %>%
as_tibble() %>%
mutate_at("Cluster", as.factor)
ggplot(tcga.umap.clus, aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Expression profiles per cluster
tcga.clustered <- tcga.raw %>%
mutate(Cluster = as.factor(tcga.hclust$cluster)) %>%
pivot_longer(names_to = "Marker", values_to = "Z", -Cluster)
profiles <- seq_len(tcga.hclust$nbclust) %>% map(~
ggplot(
tcga.clustered %>% filter(Cluster == .x),
aes(x = Marker, y = Z, color = Marker)
) +
stat_summary(fun.data = mean_sdl, show.legend = FALSE) +
ylim(-3, 3) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
plot_grid(plotlist = profiles, labels = paste("Cluster", seq_len(tcga.hclust$nbclust)))Warning: Removed 17 rows containing non-finite values (stat_summary).Warning: Removed 2 rows containing missing values (geom_segment).Warning: Removed 2 rows containing non-finite values (stat_summary).Warning: Removed 6 rows containing non-finite values (stat_summary).Warning: Removed 2 rows containing missing values (geom_segment).Warning: Removed 4 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing missing values (geom_segment).Warning: Removed 4 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing missing values (geom_segment).
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Differential expression analysis
design <- model.matrix(~0 + as.factor(tcga.hclust$cluster))
colnames(design) <- paste0("Cluster", seq_len(tcga.hclust$nbclust))
tcga.hclust.limma <- lmFit(t(tcga.raw), design = design)
tcga.hclust.limma$coefficients Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
P53 -0.83287575 -1.63930495 -0.686920525 -0.77840188 -1.45188350
AKT_pS473 -0.30863644 -1.33879677 -0.006580675 -0.21834276 -1.32477306
AKT_pT308 0.08732521 -0.27177766 0.218564880 0.18469631 -0.29203272
BETACATENIN 1.57155636 2.31044844 1.039230839 1.56262878 1.21849982
JNK_pT183Y185 -0.11316242 -0.53603513 -0.020171999 -0.06052635 -0.32400742
MEK1_pS217S221 -0.24976273 -0.31018141 -0.253137701 -0.23604609 0.05052373
P38_pT180Y182 0.51223883 0.02056018 0.769874692 0.58845685 0.63984603
P70S6K_pT389 -0.77571779 -1.94795561 -0.534009436 -0.69535853 -1.62072380
PDK1_pS241 0.38756470 0.59045467 0.116197084 0.16495428 0.63423716
S6_pS235S236 -0.67758055 -1.43636690 -0.361153007 -0.40006282 -0.97855764
YAP_pS127 2.03602902 3.00731625 1.850386691 2.08600134 2.04093606
TRANSGLUTAMINASE -0.74859832 -0.22338223 -0.911213400 -0.13653922 -0.45593106
Cluster6 Cluster7
P53 -1.59199405 -1.55125427
AKT_pS473 -1.36858380 -0.01857768
AKT_pT308 0.16397916 0.89903051
BETACATENIN 0.69812217 1.74893447
JNK_pT183Y185 -0.18383498 -0.40070734
MEK1_pS217S221 0.26605581 -0.29766097
P38_pT180Y182 -0.02873231 0.58069604
P70S6K_pT389 -2.07460770 -1.91867762
PDK1_pS241 0.31715543 0.63407898
S6_pS235S236 -0.18619595 -0.86118068
YAP_pS127 2.57126673 2.33398982
TRANSGLUTAMINASE 0.18560297 -0.40427359tests.hclust <- decideTests(eBayes(tcga.hclust.limma))
tests.hclust@.Data Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7
P53 -1 -1 -1 -1 -1 -1 -1
AKT_pS473 -1 -1 0 -1 -1 -1 0
AKT_pT308 1 -1 1 1 -1 0 1
BETACATENIN 1 1 1 1 1 1 1
JNK_pT183Y185 -1 -1 0 0 -1 -1 -1
MEK1_pS217S221 -1 -1 -1 -1 0 1 -1
P38_pT180Y182 1 0 1 1 1 0 1
P70S6K_pT389 -1 -1 -1 -1 -1 -1 -1
PDK1_pS241 1 1 0 1 1 1 1
S6_pS235S236 -1 -1 -1 -1 -1 0 -1
YAP_pS127 1 1 1 1 1 1 1
TRANSGLUTAMINASE -1 -1 -1 0 -1 0 -1summary(tests.hclust) Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7
Down 7 8 5 5 7 4 6
NotSig 0 1 3 2 1 4 1
Up 5 3 4 5 4 4 5Similarity and graph based clustering
Calculate leiden clustering based on weighted shared nearest neighbor graph
tcga.knn <- knn.index(tcga, 10)
jaccard.cuttoff <- 0.1
snn <- seq_len(nrow(tcga.knn) - 1) %>% map_dfr(\(id){
to <- seq(id + 1, nrow(tcga.knn))
jaccard <- to %>%
map_dbl(~ length(intersect(tcga.knn[id, ], tcga.knn[.x, ])) /
length(union(tcga.knn[id, ], tcga.knn[.x, ])))
tibble(from = id, to = to, weight = jaccard) %>% filter(jaccard >= jaccard.cuttoff)
})
leiden.clusters <- graph_from_data_frame(snn, directed = FALSE) %>% leiden()Plot in 2D PCA and UMAP
fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(leiden.clusters)) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(leiden.clusters)), aes(x = U1, y = U2, color = Cluster, shape = Cluster)) +
geom_point() +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Expression profiles per cluster
tcga.clustered <- tcga.raw %>%
mutate(Cluster = as.factor(leiden.clusters)) %>%
pivot_longer(names_to = "Marker", values_to = "Z", -Cluster)
profiles <- seq_len(max(leiden.clusters)) %>% map(~
ggplot(
tcga.clustered %>% filter(Cluster == .x),
aes(x = Marker, y = Z, color = Marker)
) +
stat_summary(fun.data = mean_sdl, show.legend = FALSE) +
ylim(-3, 3) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
plot_grid(plotlist = profiles, labels = paste("Cluster", seq_len(max(leiden.clusters))))Warning: Removed 1 rows containing non-finite values (stat_summary).Warning: Removed 10 rows containing non-finite values (stat_summary).Warning: Removed 2 rows containing missing values (geom_segment).Warning: Removed 2 rows containing non-finite values (stat_summary).Warning: Removed 19 rows containing non-finite values (stat_summary).Warning: Removed 3 rows containing missing values (geom_segment).Warning: Removed 1 rows containing non-finite values (stat_summary).
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Differential expression analysis
design <- model.matrix(~0 + as.factor(leiden.clusters))
colnames(design) <- paste0("Cluster", seq_len(max(leiden.clusters)))
tcga.leiden.limma <- lmFit(t(tcga.raw), design = design)
tcga.leiden.limma$coefficients Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
P53 -0.9673433 -1.53283086 -0.75756942 -1.61767917 -0.749373251
AKT_pS473 -0.3200889 -1.28276689 -0.16000561 -1.15973070 -0.194008436
AKT_pT308 0.2124778 -0.18171392 0.21593973 -0.04769457 0.153441990
BETACATENIN 1.7912135 1.25643693 1.03564301 2.09187795 1.416551169
JNK_pT183Y185 -0.1965589 -0.32069886 -0.03275702 -0.49164891 -0.009853884
MEK1_pS217S221 -0.2999638 0.05567082 -0.15493844 -0.26691280 -0.206574355
P38_pT180Y182 0.5421273 0.49421905 0.77606040 -0.01780252 0.514742787
P70S6K_pT389 -0.9958935 -1.84333252 -0.60204473 -1.95723563 -0.625471023
PDK1_pS241 0.3879572 0.56800614 0.21918573 0.58110009 0.209576601
S6_pS235S236 -0.6349137 -0.82814538 -0.47055819 -1.33662344 -0.368775477
YAP_pS127 2.0322800 2.23326298 2.06163471 2.82246037 1.999431332
TRANSGLUTAMINASE -0.4973532 -0.24179636 -0.80334063 -0.36399467 -0.082472412tests.leiden <- decideTests(eBayes(tcga.leiden.limma))
tests.leiden@.Data Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
P53 -1 -1 -1 -1 -1
AKT_pS473 -1 -1 0 -1 0
AKT_pT308 1 -1 1 0 0
BETACATENIN 1 1 1 1 1
JNK_pT183Y185 -1 -1 0 -1 0
MEK1_pS217S221 -1 0 -1 -1 -1
P38_pT180Y182 1 1 1 0 1
P70S6K_pT389 -1 -1 -1 -1 -1
PDK1_pS241 1 1 1 1 1
S6_pS235S236 -1 -1 -1 -1 -1
YAP_pS127 1 1 1 1 1
TRANSGLUTAMINASE -1 -1 -1 -1 0summary(tests.leiden) Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
Down 7 7 5 7 4
NotSig 0 1 2 2 4
Up 5 4 5 3 4Consensus NMF
tcga.nmf.rank <- nmfEstimateRank(as.matrix(t(tcga)), seq(2,12), nrun = 10)
plot(tcga.nmf.rank)Warning: Removed 1 rows containing missing values (geom_point).
tcga.nmf <- nmf(as.matrix(t(tcga)), 9, nrun = 10)Extract basis of NMF (signature of cluster)
basismap(tcga.nmf)
Extract coefficients of NMF (soft clustering of samples)
coefmap(tcga.nmf)
Check for signs of overfitting
consensusmap(tcga.nmf)
Assign clusters
nmf.clusters <- apply(tcga.nmf@fit@H, 2, which.max)Plot in 2D PCA and UMAP
fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(nmf.clusters)) +
theme_classic()
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(nmf.clusters)), aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
Differential expression analysis
design <- model.matrix(~0 + as.factor(nmf.clusters))
colnames(design) <- paste0("Cluster", seq_len(max(nmf.clusters)))
tcga.nmf.limma <- lmFit(t(tcga.raw), design = design)
tcga.nmf.limma$coefficients Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
P53 -1.10694961 -1.47281717 -1.4390930 -1.31952436 -1.408395531
AKT_pS473 -0.57406575 -1.03079810 -1.1267523 -0.98040237 -1.435032358
AKT_pT308 0.05333472 -0.06691784 -0.1742176 -0.39565713 -0.124579611
BETACATENIN 2.47820655 1.69944114 1.6562291 1.50041542 1.094901251
JNK_pT183Y185 -0.32673969 -0.40956008 -0.5157945 -0.43549726 0.001981008
MEK1_pS217S221 -0.30372607 -0.19884053 -0.2561024 -0.31368660 0.452610710
P38_pT180Y182 0.24978589 0.23245516 1.1584952 0.06678912 0.447332278
P70S6K_pT389 -1.24207931 -1.73111606 -1.6578623 -1.50621120 -1.555388810
PDK1_pS241 0.52947256 0.53284938 0.4288817 0.67767127 0.556231887
S6_pS235S236 -1.08064394 -0.95305470 -1.2276411 -1.35723076 -0.860677320
YAP_pS127 1.94124952 2.93622011 1.9401463 1.56874728 2.056839337
TRANSGLUTAMINASE -0.70096178 -0.36776500 -0.6494818 -0.37180974 -0.139566359
Cluster6 Cluster7 Cluster8 Cluster9
P53 -0.75792378 -1.16642743 -1.44794565 -1.7343508
AKT_pS473 -0.17752958 -0.80854698 -1.35388746 0.6065127
AKT_pT308 0.16494551 -0.07193855 -0.35792865 1.5088877
BETACATENIN 1.39488420 1.45777476 1.23146467 1.5788436
JNK_pT183Y185 -0.05473442 -0.39775043 -0.68698203 -0.4520198
MEK1_pS217S221 -0.25505234 -0.38203065 -0.43545558 -0.2443669
P38_pT180Y182 0.58923331 0.36995499 0.31986997 0.8981164
P70S6K_pT389 -0.66793673 -1.23538317 -1.74264302 -2.1618971
PDK1_pS241 0.23571112 0.37396033 0.44641453 0.6912316
S6_pS235S236 -0.47921236 -0.58950486 -0.09480915 -0.5763633
YAP_pS127 1.95673778 1.88453913 1.43437318 1.9099130
TRANSGLUTAMINASE -0.52400250 0.33394102 -0.41896611 -0.3269469tests.nmf <- decideTests(eBayes(tcga.nmf.limma))
tests.nmf@.Data Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7
P53 -1 -1 -1 -1 -1 -1 -1
AKT_pS473 -1 -1 -1 -1 -1 -1 -1
AKT_pT308 0 0 0 -1 0 1 0
BETACATENIN 1 1 1 1 1 1 1
JNK_pT183Y185 -1 -1 -1 -1 0 -1 -1
MEK1_pS217S221 -1 -1 -1 0 1 -1 -1
P38_pT180Y182 0 1 1 0 1 1 0
P70S6K_pT389 -1 -1 -1 -1 -1 -1 -1
PDK1_pS241 1 1 1 1 1 1 1
S6_pS235S236 -1 -1 -1 -1 -1 -1 0
YAP_pS127 1 1 1 1 1 1 1
TRANSGLUTAMINASE -1 -1 -1 0 0 -1 0
Cluster8 Cluster9
P53 -1 -1
AKT_pS473 -1 1
AKT_pT308 0 1
BETACATENIN 1 1
JNK_pT183Y185 -1 -1
MEK1_pS217S221 -1 -1
P38_pT180Y182 0 1
P70S6K_pT389 -1 -1
PDK1_pS241 1 1
S6_pS235S236 0 0
YAP_pS127 1 1
TRANSGLUTAMINASE 0 0summary(tests.nmf) Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8
Down 7 7 7 6 4 7 5 5
NotSig 2 1 1 3 3 0 4 4
Up 3 4 4 3 5 5 3 3
Cluster9
Down 4
NotSig 2
Up 6
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RColorBrewer_1.1-2 doParallel_1.0.16 iterators_1.0.13
[4] foreach_1.5.1 NMF_0.23.0 Biobase_2.52.0
[7] BiocGenerics_0.38.0 cluster_2.1.2 rngtools_1.5
[10] pkgmaker_0.32.2 registry_0.5-1 limma_3.48.1
[13] leiden_0.3.9 igraph_1.2.6 FNN_1.1.3
[16] cowplot_1.1.1 factoextra_1.0.7 uwot_0.1.10
[19] Matrix_1.3-4 skimr_2.1.3 forcats_0.5.1
[22] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[25] readr_2.0.0 tidyr_1.1.3 tibble_3.1.3
[28] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 Hmisc_4.5-0
[4] plyr_1.8.6 repr_1.1.3 splines_4.1.0
[7] gridBase_0.4-7 digest_0.6.27 htmltools_0.5.1.1
[10] viridis_0.6.1 fansi_0.5.0 checkmate_2.0.0
[13] magrittr_2.0.1 tzdb_0.1.2 openxlsx_4.2.4
[16] modelr_0.1.8 vroom_1.5.3 jpeg_0.1-9
[19] colorspace_2.0-2 rvest_1.0.1 ggrepel_0.9.1
[22] haven_2.4.1 xfun_0.24 crayon_1.4.1
[25] jsonlite_1.7.2 survival_3.2-11 glue_1.4.2
[28] gtable_0.3.0 car_3.0-11 abind_1.4-5
[31] scales_1.1.1 DBI_1.1.1 rstatix_0.7.0
[34] Rcpp_1.0.7 htmlTable_2.2.1 viridisLite_0.4.0
[37] xtable_1.8-4 reticulate_1.20 foreign_0.8-81
[40] bit_4.0.4 Formula_1.2-4 htmlwidgets_1.5.3
[43] httr_1.4.2 ellipsis_0.3.2 pkgconfig_2.0.3
[46] farver_2.1.0 nnet_7.3-16 sass_0.4.0
[49] dbplyr_2.1.1 utf8_1.2.2 tidyselect_1.1.1
[52] labeling_0.4.2 rlang_0.4.11 reshape2_1.4.4
[55] later_1.2.0 munsell_0.5.0 cellranger_1.1.0
[58] tools_4.1.0 cli_3.0.1 generics_0.1.0
[61] broom_0.7.9 evaluate_0.14 yaml_2.2.1
[64] knitr_1.33 bit64_4.0.5 fs_1.5.0
[67] zip_2.2.0 dendextend_1.15.1 whisker_0.4
[70] xml2_1.3.2 compiler_4.1.0 rstudioapi_0.13
[73] curl_4.3.2 png_0.1-7 ggsignif_0.6.2
[76] reprex_2.0.0 bslib_0.2.5.1 stringi_1.7.3
[79] highr_0.9 RSpectra_0.16-0 lattice_0.20-44
[82] vctrs_0.3.8 pillar_1.6.2 lifecycle_1.0.0
[85] jquerylib_0.1.4 data.table_1.14.0 httpuv_1.6.1
[88] R6_2.5.0 latticeExtra_0.6-29 promises_1.2.0.1
[91] gridExtra_2.3 rio_0.5.27 codetools_0.2-18
[94] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2
[97] hms_1.1.0 grid_4.1.0 rpart_4.1-15
[100] rmarkdown_2.9 carData_3.0-4 git2r_0.28.0
[103] ggpubr_0.4.0 lubridate_1.7.10 base64enc_0.1-3