scGPS introduction

Quan Nguyen and Michael Thompson

2022-04-26

1. Installation instruction

# To install scGPS from github (Depending on the configuration of the local
# computer or HPC, possible custom C++ compilation may be required - see
# installation trouble-shootings below)
devtools::install_github("IMB-Computational-Genomics-Lab/scGPS")

# for C++ compilation trouble-shooting, manual download and installation can be
# done from github

git clone https://github.com/IMB-Computational-Genomics-Lab/scGPS

# then check in scGPS/src if any of the precompiled (e.g.  those with *.so and
# *.o) files exist and delete them before recompiling

# then with the scGPS as the R working directory, manually install and load
# using devtools functionality
# Install the package
devtools::install()
#load the package to the workspace 
library(scGPS)

2. A simple workflow of the scGPS:

The purpose of this workflow is to solve the following task:

2.1 Create scGPS objects

2.2 Run prediction

2.3 Summarise results

3. A complete workflow of the scGPS:

The purpose of this workflow is to solve the following task:

3.1 Identify clusters in a dataset using CORE

(skip this step if clusters are known)

3.2 Identify clusters in a dataset using SCORE (Stable Clustering at Optimal REsolution)

(skip this step if clusters are known)

(SCORE aims to get stable subpopulation results by introducing bagging aggregation and bootstrapping to the CORE algorithm)

3.3 Visualise all cluster results in all iterations

3.4 Compare clustering results with other dimensional reduction methods (e.g., tSNE)

3.5 Find gene markers and annotate clusters

4. Relationship between clusters within one sample or between two samples

The purpose of this workflow is to solve the following task:

4.1 Start the scGPS prediction to find relationship between clusters

4.2 Display summary results for the prediction

#get the number of rows for the summary matrix
row_cluster <-length(unique(colData(mixedpop2)[,1]))

#summary results LDA to to show the percent of cells classified as cells
#belonging by LDA classifier
summary_prediction_lda(LSOLDA_dat=LSOLDA_dat, nPredSubpop = row_cluster )
#>                 V1               V2                                names
#> 1 6.41711229946524 8.02139037433155 LDA for subpop 1 in target mixedpop2
#> 2 92.1428571428571 89.2857142857143 LDA for subpop 2 in target mixedpop2
#> 3  3.7593984962406 6.76691729323308 LDA for subpop 3 in target mixedpop2
#> 4             32.5             47.5 LDA for subpop 4 in target mixedpop2

#summary results Lasso to show the percent of cells classified as cells
#belonging by Lasso classifier
summary_prediction_lasso(LSOLDA_dat=LSOLDA_dat, nPredSubpop = row_cluster)
#>                 V1               V2                                      names
#> 1 40.1069518716578 83.4224598930481 ElasticNet for subpop1 in target mixedpop2
#> 2 96.4285714285714 97.1428571428571 ElasticNet for subpop2 in target mixedpop2
#> 3 54.8872180451128 51.1278195488722 ElasticNet for subpop3 in target mixedpop2
#> 4               75             72.5 ElasticNet for subpop4 in target mixedpop2

# summary maximum deviance explained by the model during the model training
summary_deviance(object = LSOLDA_dat)
#> $allDeviance
#> [1] "61.37" "82.73"
#> 
#> $DeviMax
#>           dat_DE$Dfd          Deviance           DEgenes
#> 1                  0             82.73    genes_cluster1
#> 2                  1             82.73    genes_cluster1
#> 3                  2             82.73    genes_cluster1
#> 4                  3             82.73    genes_cluster1
#> 5                  6             82.73    genes_cluster1
#> 6                  8             82.73    genes_cluster1
#> 7                 11             82.73    genes_cluster1
#> 8                 12             82.73    genes_cluster1
#> 9                 16             82.73    genes_cluster1
#> 10                17             82.73    genes_cluster1
#> 11                19             82.73    genes_cluster1
#> 12                21             82.73    genes_cluster1
#> 13                23             82.73    genes_cluster1
#> 14                25             82.73    genes_cluster1
#> 15                27             82.73    genes_cluster1
#> 16                28             82.73    genes_cluster1
#> 17                32             82.73    genes_cluster1
#> 18                36             82.73    genes_cluster1
#> 19                39             82.73    genes_cluster1
#> 20                41             82.73    genes_cluster1
#> 21                45             82.73    genes_cluster1
#> 22                51             82.73    genes_cluster1
#> 23                56             82.73    genes_cluster1
#> 24                58             82.73    genes_cluster1
#> 25                59             82.73    genes_cluster1
#> 26                60             82.73    genes_cluster1
#> 27                61             82.73    genes_cluster1
#> 28                67             82.73    genes_cluster1
#> 29                71             82.73    genes_cluster1
#> 30 remaining DEgenes remaining DEgenes remaining DEgenes
#> 
#> $LassoGenesMax
#> NULL

# summary accuracy to check the model accuracy in the leave-out test set
summary_accuracy(object = LSOLDA_dat)
#> [1] 66.07143 66.07143

4.3 Plot the relationship between clusters in one sample

Here we look at one example use case to find relationship between clusters within one sample or between two sample

#run prediction for 3 clusters
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
#cluster_mixedpop2 <- as.numeric(as.vector(colData(mixedpop2)[,1]))

c_selectID <- 1
#top 200 gene markers distinguishing cluster 1
genes = DEgenes$id[1:200]

LSOLDA_dat1 <- bootstrap_prediction(nboots = 2, mixedpop1 = mixedpop2,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop2,
                        cluster_mixedpop2 = cluster_mixedpop2)

c_selectID <- 2
genes = DEgenes$id[1:200]

LSOLDA_dat2 <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop2,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop2,
                        cluster_mixedpop2 = cluster_mixedpop2)

c_selectID <- 3
genes = DEgenes$id[1:200]
LSOLDA_dat3 <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop2,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop2,
                        cluster_mixedpop2 = cluster_mixedpop2)

c_selectID <- 4
genes = DEgenes$id[1:200]
LSOLDA_dat4 <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop2,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop2,
                        cluster_mixedpop2 = cluster_mixedpop2)


#prepare table input for sankey plot

LASSO_C1S2  <- reformat_LASSO(c_selectID=1, mp_selectID = 2,
                             LSOLDA_dat=LSOLDA_dat1,
                             nPredSubpop = length(unique(colData(mixedpop2)
                                [,1])),
                             Nodes_group ="#7570b3")

LASSO_C2S2  <- reformat_LASSO(c_selectID=2, mp_selectID =2,
                             LSOLDA_dat=LSOLDA_dat2,
                             nPredSubpop = length(unique(colData(mixedpop2)
                                [,1])),
                             Nodes_group ="#1b9e77")

LASSO_C3S2  <- reformat_LASSO(c_selectID=3, mp_selectID =2,
                             LSOLDA_dat=LSOLDA_dat3,
                             nPredSubpop = length(unique(colData(mixedpop2)
                                [,1])),
                             Nodes_group ="#e7298a")

LASSO_C4S2  <- reformat_LASSO(c_selectID=4, mp_selectID =2,
                             LSOLDA_dat=LSOLDA_dat4,
                             nPredSubpop = length(unique(colData(mixedpop2)
                                [,1])),
                             Nodes_group ="#00FFFF")

combined <- rbind(LASSO_C1S2,LASSO_C2S2,LASSO_C3S2, LASSO_C4S2 )
combined <- combined[is.na(combined$Value) != TRUE,]

nboots = 2
#links: source, target, value
#source: node, nodegroup
combined_D3obj <-list(Nodes=combined[,(nboots+3):(nboots+4)],
                     Links=combined[,c((nboots+2):(nboots+1),ncol(combined))])

library(networkD3)

Node_source <- as.vector(sort(unique(combined_D3obj$Links$Source)))
Node_target <- as.vector(sort(unique(combined_D3obj$Links$Target)))
Node_all <-unique(c(Node_source, Node_target))

#assign IDs for Source (start from 0)
Source <-combined_D3obj$Links$Source
Target <- combined_D3obj$Links$Target

for(i in 1:length(Node_all)){
   Source[Source==Node_all[i]] <-i-1
   Target[Target==Node_all[i]] <-i-1
}
# 
combined_D3obj$Links$Source <- as.numeric(Source)
combined_D3obj$Links$Target <- as.numeric(Target)
combined_D3obj$Links$LinkColor <- combined$NodeGroup

#prepare node info
node_df <-data.frame(Node=Node_all)
node_df$id <-as.numeric(c(0, 1:(length(Node_all)-1)))

suppressMessages(library(dplyr))
Color <- combined %>% count(Node, color=NodeGroup) %>% select(2)
node_df$color <- Color$color

suppressMessages(library(networkD3))
p1<-sankeyNetwork(Links =combined_D3obj$Links, Nodes = node_df,
                 Value = "Value", NodeGroup ="color", LinkGroup = "LinkColor", 
                 NodeID="Node", Source="Source", Target="Target", fontSize = 22)
p1
#saveNetwork(p1, file = paste0(path,'Subpopulation_Net.html'))

4.3 Plot the relationship between clusters in two samples

Here we look at one example use case to find relationship between clusters within one sample or between two sample

#run prediction for 3 clusters
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
row_cluster <-length(unique(colData(mixedpop2)[,1]))

c_selectID <- 1
#top 200 gene markers distinguishing cluster 1
genes = DEgenes$id[1:200]
LSOLDA_dat1 <- bootstrap_prediction(nboots = 2, mixedpop1 = mixedpop1,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop1,
                        cluster_mixedpop2 = cluster_mixedpop2)


c_selectID <- 2
genes = DEgenes$id[1:200]
LSOLDA_dat2 <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop1,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop1,
                        cluster_mixedpop2 = cluster_mixedpop2)

c_selectID <- 3
genes = DEgenes$id[1:200]
LSOLDA_dat3 <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop1,
                        mixedpop2 = mixedpop2, genes=genes, c_selectID, 
                        listData =list(),
                        cluster_mixedpop1 = cluster_mixedpop1,
                        cluster_mixedpop2 = cluster_mixedpop2)

#prepare table input for sankey plot

LASSO_C1S1  <- reformat_LASSO(c_selectID=1, mp_selectID = 1,
                             LSOLDA_dat=LSOLDA_dat1, nPredSubpop = row_cluster, 
                             Nodes_group = "#7570b3")

LASSO_C2S1  <- reformat_LASSO(c_selectID=2, mp_selectID = 1,
                             LSOLDA_dat=LSOLDA_dat2, nPredSubpop = row_cluster, 
                             Nodes_group = "#1b9e77")

LASSO_C3S1  <- reformat_LASSO(c_selectID=3, mp_selectID = 1,
                             LSOLDA_dat=LSOLDA_dat3, nPredSubpop = row_cluster, 
                             Nodes_group = "#e7298a")


combined <- rbind(LASSO_C1S1,LASSO_C2S1,LASSO_C3S1)

nboots = 2
#links: source, target, value
#source: node, nodegroup
combined_D3obj <-list(Nodes=combined[,(nboots+3):(nboots+4)],
                     Links=combined[,c((nboots+2):(nboots+1),ncol(combined))])
combined <- combined[is.na(combined$Value) != TRUE,]


library(networkD3)

Node_source <- as.vector(sort(unique(combined_D3obj$Links$Source)))
Node_target <- as.vector(sort(unique(combined_D3obj$Links$Target)))
Node_all <-unique(c(Node_source, Node_target))

#assign IDs for Source (start from 0)
Source <-combined_D3obj$Links$Source
Target <- combined_D3obj$Links$Target

for(i in 1:length(Node_all)){
   Source[Source==Node_all[i]] <-i-1
   Target[Target==Node_all[i]] <-i-1
}

combined_D3obj$Links$Source <- as.numeric(Source)
combined_D3obj$Links$Target <- as.numeric(Target)
combined_D3obj$Links$LinkColor <- combined$NodeGroup

#prepare node info
node_df <-data.frame(Node=Node_all)
node_df$id <-as.numeric(c(0, 1:(length(Node_all)-1)))

suppressMessages(library(dplyr))
n <- length(unique(node_df$Node))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "Set1"))
Color = getPalette(n)
node_df$color <- Color
suppressMessages(library(networkD3))
p1<-sankeyNetwork(Links =combined_D3obj$Links, Nodes = node_df,
                 Value = "Value", NodeGroup ="color", LinkGroup = "LinkColor",
                 NodeID="Node", Source="Source", Target="Target", fontSize = 22)
p1
#saveNetwork(p1, file = paste0(path,'Subpopulation_Net.html'))
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.0 RC (2022-04-21 r82226)
#>  os       Ubuntu 20.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2022-04-26
#>  pandoc   2.5 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package              * version    date (UTC) lib source
#>  annotate               1.75.0     2022-04-26 [2] Bioconductor
#>  AnnotationDbi        * 1.59.0     2022-04-26 [2] Bioconductor
#>  ape                    5.6-2      2022-03-02 [2] CRAN (R 4.2.0)
#>  aplot                  0.1.3      2022-04-01 [2] CRAN (R 4.2.0)
#>  assertthat             0.2.1      2019-03-21 [2] CRAN (R 4.2.0)
#>  Biobase              * 2.57.0     2022-04-26 [2] Bioconductor
#>  BiocGenerics         * 0.43.0     2022-04-26 [2] Bioconductor
#>  BiocParallel           1.31.0     2022-04-26 [2] Bioconductor
#>  Biostrings             2.65.0     2022-04-26 [2] Bioconductor
#>  bit                    4.0.4      2020-08-04 [2] CRAN (R 4.2.0)
#>  bit64                  4.0.5      2020-08-30 [2] CRAN (R 4.2.0)
#>  bitops                 1.0-7      2021-04-24 [2] CRAN (R 4.2.0)
#>  blob                   1.2.3      2022-04-10 [2] CRAN (R 4.2.0)
#>  brio                   1.1.3      2021-11-30 [2] CRAN (R 4.2.0)
#>  bslib                  0.3.1      2021-10-06 [2] CRAN (R 4.2.0)
#>  cachem                 1.0.6      2021-08-19 [2] CRAN (R 4.2.0)
#>  callr                  3.7.0      2021-04-20 [2] CRAN (R 4.2.0)
#>  caret                * 6.0-92     2022-04-19 [2] CRAN (R 4.2.0)
#>  class                  7.3-20     2022-01-16 [2] CRAN (R 4.2.0)
#>  cli                    3.3.0      2022-04-25 [2] CRAN (R 4.2.0)
#>  clusterProfiler      * 4.5.0      2022-04-26 [2] Bioconductor
#>  codetools              0.2-18     2020-11-04 [2] CRAN (R 4.2.0)
#>  colorspace             2.0-3      2022-02-21 [2] CRAN (R 4.2.0)
#>  cowplot                1.1.1      2020-12-30 [2] CRAN (R 4.2.0)
#>  crayon                 1.5.1      2022-03-26 [2] CRAN (R 4.2.0)
#>  data.table             1.14.2     2021-09-27 [2] CRAN (R 4.2.0)
#>  DBI                    1.1.2      2021-12-20 [2] CRAN (R 4.2.0)
#>  DelayedArray           0.23.0     2022-04-26 [2] Bioconductor
#>  dendextend             1.15.2     2021-10-28 [2] CRAN (R 4.2.0)
#>  desc                   1.4.1      2022-03-06 [2] CRAN (R 4.2.0)
#>  DESeq2                 1.37.0     2022-04-26 [2] Bioconductor
#>  devtools               2.4.3      2021-11-30 [2] CRAN (R 4.2.0)
#>  digest                 0.6.29     2021-12-01 [2] CRAN (R 4.2.0)
#>  DO.db                  2.9        2022-04-25 [2] Bioconductor
#>  DOSE                 * 3.23.0     2022-04-26 [2] Bioconductor
#>  downloader             0.4        2015-07-09 [2] CRAN (R 4.2.0)
#>  dplyr                * 1.0.8      2022-02-08 [2] CRAN (R 4.2.0)
#>  dynamicTreeCut       * 1.63-1     2016-03-11 [2] CRAN (R 4.2.0)
#>  e1071                  1.7-9      2021-09-16 [2] CRAN (R 4.2.0)
#>  ellipsis               0.3.2      2021-04-29 [2] CRAN (R 4.2.0)
#>  enrichplot             1.17.0     2022-04-26 [2] Bioconductor
#>  evaluate               0.15       2022-02-18 [2] CRAN (R 4.2.0)
#>  fansi                  1.0.3      2022-03-24 [2] CRAN (R 4.2.0)
#>  farver                 2.1.0      2021-02-28 [2] CRAN (R 4.2.0)
#>  fastcluster            1.2.3      2021-05-24 [2] CRAN (R 4.2.0)
#>  fastmap                1.1.0      2021-01-25 [2] CRAN (R 4.2.0)
#>  fastmatch              1.1-3      2021-07-23 [2] CRAN (R 4.2.0)
#>  fgsea                  1.23.0     2022-04-26 [2] Bioconductor
#>  foreach                1.5.2      2022-02-02 [2] CRAN (R 4.2.0)
#>  fs                     1.5.2      2021-12-08 [2] CRAN (R 4.2.0)
#>  future                 1.25.0     2022-04-24 [2] CRAN (R 4.2.0)
#>  future.apply           1.9.0      2022-04-25 [2] CRAN (R 4.2.0)
#>  genefilter             1.79.0     2022-04-26 [2] Bioconductor
#>  geneplotter            1.75.0     2022-04-26 [2] Bioconductor
#>  generics               0.1.2      2022-01-31 [2] CRAN (R 4.2.0)
#>  GenomeInfoDb         * 1.33.0     2022-04-26 [2] Bioconductor
#>  GenomeInfoDbData       1.2.8      2022-04-25 [2] Bioconductor
#>  GenomicRanges        * 1.49.0     2022-04-26 [2] Bioconductor
#>  ggforce                0.3.3      2021-03-05 [2] CRAN (R 4.2.0)
#>  ggfun                  0.0.6      2022-04-01 [2] CRAN (R 4.2.0)
#>  ggplot2              * 3.3.5      2021-06-25 [2] CRAN (R 4.2.0)
#>  ggplotify              0.1.0      2021-09-02 [2] CRAN (R 4.2.0)
#>  ggraph                 2.0.5      2021-02-23 [2] CRAN (R 4.2.0)
#>  ggrepel                0.9.1      2021-01-15 [2] CRAN (R 4.2.0)
#>  ggtree                 3.5.0      2022-04-26 [2] Bioconductor
#>  glmnet                 4.1-4      2022-04-15 [2] CRAN (R 4.2.0)
#>  globals                0.14.0     2020-11-22 [2] CRAN (R 4.2.0)
#>  glue                   1.6.2      2022-02-24 [2] CRAN (R 4.2.0)
#>  GO.db                  3.15.0     2022-04-25 [2] Bioconductor
#>  GOSemSim               2.23.0     2022-04-26 [2] Bioconductor
#>  gower                  1.0.0      2022-02-03 [2] CRAN (R 4.2.0)
#>  graph                  1.75.0     2022-04-26 [2] Bioconductor
#>  graphite               1.43.0     2022-04-26 [2] Bioconductor
#>  graphlayouts           0.8.0      2022-01-03 [2] CRAN (R 4.2.0)
#>  gridExtra              2.3        2017-09-09 [2] CRAN (R 4.2.0)
#>  gridGraphics           0.5-1      2020-12-13 [2] CRAN (R 4.2.0)
#>  gtable                 0.3.0      2019-03-25 [2] CRAN (R 4.2.0)
#>  hardhat                0.2.0      2022-01-24 [2] CRAN (R 4.2.0)
#>  highr                  0.9        2021-04-16 [2] CRAN (R 4.2.0)
#>  htmltools              0.5.2      2021-08-25 [2] CRAN (R 4.2.0)
#>  htmlwidgets            1.5.4      2021-09-08 [2] CRAN (R 4.2.0)
#>  httr                   1.4.2      2020-07-20 [2] CRAN (R 4.2.0)
#>  igraph                 1.3.1      2022-04-20 [2] CRAN (R 4.2.0)
#>  ipred                  0.9-12     2021-09-15 [2] CRAN (R 4.2.0)
#>  IRanges              * 2.31.0     2022-04-26 [2] Bioconductor
#>  iterators              1.0.14     2022-02-05 [2] CRAN (R 4.2.0)
#>  jquerylib              0.1.4      2021-04-26 [2] CRAN (R 4.2.0)
#>  jsonlite               1.8.0      2022-02-22 [2] CRAN (R 4.2.0)
#>  KEGGREST               1.37.0     2022-04-26 [2] Bioconductor
#>  knitr                  1.38       2022-03-25 [2] CRAN (R 4.2.0)
#>  labeling               0.4.2      2020-10-20 [2] CRAN (R 4.2.0)
#>  lattice              * 0.20-45    2021-09-22 [2] CRAN (R 4.2.0)
#>  lava                   1.6.10     2021-09-02 [2] CRAN (R 4.2.0)
#>  lazyeval               0.2.2      2019-03-15 [2] CRAN (R 4.2.0)
#>  lifecycle              1.0.1      2021-09-24 [2] CRAN (R 4.2.0)
#>  listenv                0.8.0      2019-12-05 [2] CRAN (R 4.2.0)
#>  locfit               * 1.5-9.5    2022-03-03 [2] CRAN (R 4.2.0)
#>  lubridate              1.8.0      2021-10-07 [2] CRAN (R 4.2.0)
#>  magrittr               2.0.3      2022-03-30 [2] CRAN (R 4.2.0)
#>  MASS                   7.3-57     2022-04-22 [2] CRAN (R 4.2.0)
#>  Matrix                 1.4-1      2022-03-23 [2] CRAN (R 4.2.0)
#>  MatrixGenerics       * 1.9.0      2022-04-26 [2] Bioconductor
#>  matrixStats          * 0.62.0     2022-04-19 [2] CRAN (R 4.2.0)
#>  memoise                2.0.1      2021-11-26 [2] CRAN (R 4.2.0)
#>  ModelMetrics           1.2.2.2    2020-03-17 [2] CRAN (R 4.2.0)
#>  munsell                0.5.0      2018-06-12 [2] CRAN (R 4.2.0)
#>  networkD3            * 0.4        2017-03-18 [2] CRAN (R 4.2.0)
#>  nlme                   3.1-157    2022-03-25 [2] CRAN (R 4.2.0)
#>  nnet                   7.3-17     2022-01-16 [2] CRAN (R 4.2.0)
#>  org.Hs.eg.db         * 3.15.0     2022-04-25 [2] Bioconductor
#>  parallelly             1.31.1     2022-04-22 [2] CRAN (R 4.2.0)
#>  patchwork              1.1.1      2020-12-17 [2] CRAN (R 4.2.0)
#>  pillar                 1.7.0      2022-02-01 [2] CRAN (R 4.2.0)
#>  pkgbuild               1.3.1      2021-12-20 [2] CRAN (R 4.2.0)
#>  pkgconfig              2.0.3      2019-09-22 [2] CRAN (R 4.2.0)
#>  pkgload                1.2.4      2021-11-30 [2] CRAN (R 4.2.0)
#>  plyr                   1.8.7      2022-03-24 [2] CRAN (R 4.2.0)
#>  png                    0.1-7      2013-12-03 [2] CRAN (R 4.2.0)
#>  polyclip               1.10-0     2019-03-14 [2] CRAN (R 4.2.0)
#>  prettyunits            1.1.1      2020-01-24 [2] CRAN (R 4.2.0)
#>  pROC                   1.18.0     2021-09-03 [2] CRAN (R 4.2.0)
#>  processx               3.5.3      2022-03-25 [2] CRAN (R 4.2.0)
#>  prodlim                2019.11.13 2019-11-17 [2] CRAN (R 4.2.0)
#>  proxy                  0.4-26     2021-06-07 [2] CRAN (R 4.2.0)
#>  ps                     1.7.0      2022-04-23 [2] CRAN (R 4.2.0)
#>  purrr                  0.3.4      2020-04-17 [2] CRAN (R 4.2.0)
#>  qvalue                 2.29.0     2022-04-26 [2] Bioconductor
#>  R6                     2.5.1      2021-08-19 [2] CRAN (R 4.2.0)
#>  rappdirs               0.3.3      2021-01-31 [2] CRAN (R 4.2.0)
#>  RColorBrewer           1.1-3      2022-04-03 [2] CRAN (R 4.2.0)
#>  Rcpp                   1.0.8.3    2022-03-17 [2] CRAN (R 4.2.0)
#>  RcppArmadillo          0.11.0.0.0 2022-04-04 [2] CRAN (R 4.2.0)
#>  RcppParallel           5.1.5      2022-01-05 [2] CRAN (R 4.2.0)
#>  RCurl                  1.98-1.6   2022-02-08 [2] CRAN (R 4.2.0)
#>  reactome.db            1.79.0     2022-04-25 [2] Bioconductor
#>  ReactomePA           * 1.41.0     2022-04-26 [2] Bioconductor
#>  recipes                0.2.0      2022-02-18 [2] CRAN (R 4.2.0)
#>  remotes                2.4.2      2021-11-30 [2] CRAN (R 4.2.0)
#>  reshape2               1.4.4      2020-04-09 [2] CRAN (R 4.2.0)
#>  rlang                  1.0.2      2022-03-04 [2] CRAN (R 4.2.0)
#>  rmarkdown              2.14       2022-04-25 [2] CRAN (R 4.2.0)
#>  rpart                  4.1.16     2022-01-24 [2] CRAN (R 4.2.0)
#>  rprojroot              2.0.3      2022-04-02 [2] CRAN (R 4.2.0)
#>  RSQLite                2.2.12     2022-04-02 [2] CRAN (R 4.2.0)
#>  Rtsne                  0.16       2022-04-17 [2] CRAN (R 4.2.0)
#>  S4Vectors            * 0.35.0     2022-04-26 [2] Bioconductor
#>  sass                   0.4.1      2022-03-23 [2] CRAN (R 4.2.0)
#>  scales                 1.2.0      2022-04-13 [2] CRAN (R 4.2.0)
#>  scatterpie             0.1.7      2021-08-20 [2] CRAN (R 4.2.0)
#>  scGPS                * 1.11.0     2022-04-26 [1] Bioconductor
#>  sessioninfo            1.2.2      2021-12-06 [2] CRAN (R 4.2.0)
#>  shadowtext             0.1.2      2022-04-22 [2] CRAN (R 4.2.0)
#>  shape                  1.4.6      2021-05-19 [2] CRAN (R 4.2.0)
#>  SingleCellExperiment * 1.19.0     2022-04-26 [2] Bioconductor
#>  stringi                1.7.6      2021-11-29 [2] CRAN (R 4.2.0)
#>  stringr                1.4.0      2019-02-10 [2] CRAN (R 4.2.0)
#>  SummarizedExperiment * 1.27.0     2022-04-26 [2] Bioconductor
#>  survival               3.3-1      2022-03-03 [2] CRAN (R 4.2.0)
#>  testthat               3.1.4      2022-04-26 [2] CRAN (R 4.2.0)
#>  tibble                 3.1.6      2021-11-07 [2] CRAN (R 4.2.0)
#>  tidygraph              1.2.1      2022-04-05 [2] CRAN (R 4.2.0)
#>  tidyr                  1.2.0      2022-02-01 [2] CRAN (R 4.2.0)
#>  tidyselect             1.1.2      2022-02-21 [2] CRAN (R 4.2.0)
#>  tidytree               0.3.9      2022-03-04 [2] CRAN (R 4.2.0)
#>  timeDate               3043.102   2018-02-21 [2] CRAN (R 4.2.0)
#>  treeio                 1.21.0     2022-04-26 [2] Bioconductor
#>  tweenr                 1.0.2      2021-03-23 [2] CRAN (R 4.2.0)
#>  usethis                2.1.5      2021-12-09 [2] CRAN (R 4.2.0)
#>  utf8                   1.2.2      2021-07-24 [2] CRAN (R 4.2.0)
#>  vctrs                  0.4.1      2022-04-13 [2] CRAN (R 4.2.0)
#>  viridis                0.6.2      2021-10-13 [2] CRAN (R 4.2.0)
#>  viridisLite            0.4.0      2021-04-13 [2] CRAN (R 4.2.0)
#>  withr                  2.5.0      2022-03-03 [2] CRAN (R 4.2.0)
#>  xfun                   0.30       2022-03-02 [2] CRAN (R 4.2.0)
#>  XML                    3.99-0.9   2022-02-24 [2] CRAN (R 4.2.0)
#>  xtable                 1.8-4      2019-04-21 [2] CRAN (R 4.2.0)
#>  XVector                0.37.0     2022-04-26 [2] Bioconductor
#>  yaml                   2.3.5      2022-02-21 [2] CRAN (R 4.2.0)
#>  yulab.utils            0.0.4      2021-10-09 [2] CRAN (R 4.2.0)
#>  zlibbioc               1.43.0     2022-04-26 [2] Bioconductor
#> 
#>  [1] /tmp/RtmpkjCPYf/Rinst2e0f2214339d01
#>  [2] /home/biocbuild/bbs-3.16-bioc/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────