# 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)
The purpose of this workflow is to solve the following task:
# load mixed population 1 (loaded from day_2_cardio_cell_sample dataset,
# named it as day2)
library(scGPS)
day2 <- day_2_cardio_cell_sample
mixedpop1 <- new_scGPS_object(ExpressionMatrix = day2$dat2_counts,
GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
# load mixed population 2 (loaded from day_5_cardio_cell_sample dataset,
# named it as day5)
day5 <- day_5_cardio_cell_sample
mixedpop2 <- new_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
# select a subpopulation
c_selectID <- 1
# load gene list (this can be any lists of user selected genes)
genes <- training_gene_sample
genes <- genes$Merged_unique
# load cluster information
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
#run training (running nboots = 3 here, but recommend to use nboots = 50-100)
LSOLDA_dat <- bootstrap_prediction(nboots = 3, mixedpop1 = mixedpop1,
mixedpop2 = mixedpop2, genes = genes, c_selectID = c_selectID,
listData = list(), cluster_mixedpop1 = cluster_mixedpop1,
cluster_mixedpop2 = cluster_mixedpop2, trainset_ratio = 0.7)
names(LSOLDA_dat)
#> [1] "Accuracy" "ElasticNetGenes" "Deviance"
#> [4] "ElasticNetFit" "LDAFit" "predictor_S1"
#> [7] "ElasticNetPredict" "LDAPredict" "cell_results"
# summary results LDA
sum_pred_lda <- summary_prediction_lda(LSOLDA_dat = LSOLDA_dat, nPredSubpop = 4)
# summary results Lasso to show the percent of cells
# classified as cells belonging
sum_pred_lasso <- summary_prediction_lasso(LSOLDA_dat = LSOLDA_dat,
nPredSubpop = 4)
# plot summary results
plot_sum <-function(sum_dat){
sum_dat_tf <- t(sum_dat)
sum_dat_tf <- na.omit(sum_dat_tf)
sum_dat_tf <- apply(sum_dat[, -ncol(sum_dat)],1,
function(x){as.numeric(as.vector(x))})
sum_dat$names <- gsub("ElasticNet for subpop","sp", sum_dat$names )
sum_dat$names <- gsub("in target mixedpop","in p", sum_dat$names)
sum_dat$names <- gsub("LDA for subpop","sp", sum_dat$names )
sum_dat$names <- gsub("in target mixedpop","in p", sum_dat$names)
colnames(sum_dat_tf) <- sum_dat$names
boxplot(sum_dat_tf, las=2)
}
plot_sum(sum_pred_lasso)
# summary accuracy to check the model accuracy in the leave-out test set
summary_accuracy(object = LSOLDA_dat)
#> [1] 59.61538 62.61682 67.46411
# summary maximum deviance explained by the model
summary_deviance(object = LSOLDA_dat)
#> $allDeviance
#> [1] "12.24" "7.67" "6.41"
#>
#> $DeviMax
#> dat_DE$Dfd Deviance DEgenes
#> 1 0 7.67 genes_cluster1
#> 2 1 7.67 genes_cluster1
#> 3 2 7.67 genes_cluster1
#> 4 3 7.67 genes_cluster1
#> 5 remaining DEgenes remaining DEgenes remaining DEgenes
#>
#> $LassoGenesMax
#> NULL
The purpose of this workflow is to solve the following task:
(skip this step if clusters are known)
# find clustering information in an expresion data using CORE
day5 <- day_5_cardio_cell_sample
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame("Cluster"=cluster, "cellBarcodes" = cellnames)
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = cellnames)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0), PCA=FALSE)
# to update the clustering information, users can ...
key_height <- CORE_cluster$optimalClust$KeyStats$Height
optimal_res <- CORE_cluster$optimalClust$OptimalRes
optimal_index = which(key_height == optimal_res)
clustering_after_outlier_removal <- unname(unlist(
CORE_cluster$Cluster[[optimal_index]]))
corresponding_cells_after_outlier_removal <- CORE_cluster$cellsForClustering
original_cells_before_removal <- colData(mixedpop2)[,2]
corresponding_index <- match(corresponding_cells_after_outlier_removal,
original_cells_before_removal )
# check the matching
identical(as.character(original_cells_before_removal[corresponding_index]),
corresponding_cells_after_outlier_removal)
#> [1] TRUE
# create new object with the new clustering after removing outliers
mixedpop2_post_clustering <- mixedpop2[,corresponding_index]
colData(mixedpop2_post_clustering)[,1] <- clustering_after_outlier_removal
(skip this step if clusters are known)
(SCORE aims to get stable subpopulation results by introducing bagging aggregation and bootstrapping to the CORE algorithm)
# find clustering information in an expresion data using SCORE
day5 <- day_5_cardio_cell_sample
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame("Cluster"=cluster, "cellBarcodes" = cellnames)
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = cellnames )
SCORE_test <- CORE_bagging(mixedpop2, remove_outlier = c(0), PCA=FALSE,
bagging_run = 20, subsample_proportion = .8)
dev.off()
#> null device
#> 1
##3.2.1 plot CORE clustering
p1 <- plot_CORE(CORE_cluster$tree, CORE_cluster$Cluster,
color_branch = c("#208eb7", "#6ce9d3", "#1c5e39", "#8fca40", "#154975",
"#b1c8eb"))
p1
#> $mar
#> [1] 1 5 0 1
#extract optimal index identified by CORE
key_height <- CORE_cluster$optimalClust$KeyStats$Height
optimal_res <- CORE_cluster$optimalClust$OptimalRes
optimal_index = which(key_height == optimal_res)
#plot one optimal clustering bar
plot_optimal_CORE(original_tree= CORE_cluster$tree,
optimal_cluster = unlist(CORE_cluster$Cluster[optimal_index]),
shift = -2000)
#> Ordering and assigning labels...
#> 2
#> 162335NA
#> 3
#> 162335423
#> Plotting the colored dendrogram now....
#> Plotting the bar underneath now....
##3.2.2 plot SCORE clustering
#plot all clustering bars
plot_CORE(SCORE_test$tree, list_clusters = SCORE_test$Cluster)
#plot one stable optimal clustering bar
plot_optimal_CORE(original_tree= SCORE_test$tree,
optimal_cluster = unlist(SCORE_test$Cluster[
SCORE_test$optimal_index]),
shift = -100)
#> Ordering and assigning labels...
#> 2
#> 162335NA
#> 3
#> 162335423
#> Plotting the colored dendrogram now....
#> Plotting the bar underneath now....
t <- tSNE(expression.mat=assay(mixedpop2))
#> Preparing PCA inputs using the top 1500 genes ...
#> Computing PCA values...
#> Running tSNE ...
p2 <-plot_reduced(t, color_fac = factor(colData(mixedpop2)[,1]),
palletes =1:length(unique(colData(mixedpop2)[,1])))
#> Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(count)` instead.
#> ℹ The deprecated feature was likely used in the cowplot package.
#> Please report the issue at <https://github.com/wilkelab/cowplot/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: Use of `reduced_dat_toPlot$Dim1` is discouraged.
#> ℹ Use `Dim1` instead.
#> Warning: Use of `reduced_dat_toPlot$Dim2` is discouraged.
#> ℹ Use `Dim2` instead.
p2
#load gene list (this can be any lists of user-selected genes)
genes <-training_gene_sample
genes <-genes$Merged_unique
#the gene list can also be objectively identified by differential expression
#analysis cluster information is requied for find_markers. Here, we use
#CORE results.
#colData(mixedpop2)[,1] <- unlist(SCORE_test$Cluster[SCORE_test$optimal_index])
suppressMessages(library(locfit))
DEgenes <- find_markers(expression_matrix=assay(mixedpop2),
cluster = colData(mixedpop2)[,1],
selected_cluster=unique(colData(mixedpop2)[,1]))
#the output contains dataframes for each cluster.
#the data frame contains all genes, sorted by p-values
names(DEgenes)
#> [1] "baseMean" "log2FoldChange" "lfcSE" "stat"
#> [5] "pvalue" "padj" "id"
#you can annotate the identified clusters
DEgeneList_1vsOthers <- DEgenes$DE_Subpop1vsRemaining$id
#users need to check the format of the gene input to make sure they are
#consistent to the gene names in the expression matrix
#the following command saves the file "PathwayEnrichment.xlsx" to the
#working dir
#use 500 top DE genes
suppressMessages(library(DOSE))
suppressMessages(library(ReactomePA))
suppressMessages(library(clusterProfiler))
genes500 <- as.factor(DEgeneList_1vsOthers[seq_len(500)])
enrichment_test <- annotate_clusters(genes, pvalueCutoff=0.05, gene_symbol=TRUE)
#the enrichment outputs can be displayed by running
clusterProfiler::dotplot(enrichment_test, showCategory=10, font.size = 6)
The purpose of this workflow is to solve the following task:
#select a subpopulation, and input gene list
c_selectID <- 1
#note make sure the format for genes input here is the same to the format
#for genes in the mixedpop1 and mixedpop2
genes = DEgenes$id[1:500]
#run the test bootstrap with nboots = 2 runs
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
LSOLDA_dat <- bootstrap_prediction(nboots = 2, mixedpop1 = mixedpop1,
mixedpop2 = mixedpop2, genes = genes,
c_selectID = c_selectID,
listData = list(),
cluster_mixedpop1 = cluster_mixedpop1,
cluster_mixedpop2 = cluster_mixedpop2)
#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 9.62566844919786 93.048128342246 LDA for subpop 1 in target mixedpop2
#> 2 91.4285714285714 80 LDA for subpop 2 in target mixedpop2
#> 3 18.0451127819549 90.2255639097744 LDA for subpop 3 in target mixedpop2
#> 4 37.5 85 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 58.8235294117647 44.3850267379679 ElasticNet for subpop1 in target mixedpop2
#> 2 97.8571428571428 92.1428571428571 ElasticNet for subpop2 in target mixedpop2
#> 3 75.187969924812 47.3684210526316 ElasticNet for subpop3 in target mixedpop2
#> 4 82.5 67.5 ElasticNet for subpop4 in target mixedpop2
# summary maximum deviance explained by the model during the model training
summary_deviance(object = LSOLDA_dat)
#> $allDeviance
#> [1] "44.27" "74.37"
#>
#> $DeviMax
#> dat_DE$Dfd Deviance DEgenes
#> 1 0 74.37 genes_cluster1
#> 2 1 74.37 genes_cluster1
#> 3 4 74.37 genes_cluster1
#> 4 6 74.37 genes_cluster1
#> 5 7 74.37 genes_cluster1
#> 6 12 74.37 genes_cluster1
#> 7 14 74.37 genes_cluster1
#> 8 15 74.37 genes_cluster1
#> 9 18 74.37 genes_cluster1
#> 10 23 74.37 genes_cluster1
#> 11 26 74.37 genes_cluster1
#> 12 31 74.37 genes_cluster1
#> 13 33 74.37 genes_cluster1
#> 14 34 74.37 genes_cluster1
#> 15 35 74.37 genes_cluster1
#> 16 40 74.37 genes_cluster1
#> 17 42 74.37 genes_cluster1
#> 18 43 74.37 genes_cluster1
#> 19 44 74.37 genes_cluster1
#> 20 46 74.37 genes_cluster1
#> 21 47 74.37 genes_cluster1
#> 22 remaining DEgenes remaining DEgenes remaining DEgenes
#>
#> $LassoGenesMax
#> NULL
# summary accuracy to check the model accuracy in the leave-out test set
summary_accuracy(object = LSOLDA_dat)
#> [1] 67.85714 68.30357
Here we look at one example use case to find relationship between clusters within one sample or between two sample
#run prediction for 3 clusters
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
#cluster_mixedpop2 <- as.numeric(as.vector(colData(mixedpop2)[,1]))
c_selectID <- 1
#top 200 gene markers distinguishing cluster 1
genes = DEgenes$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$Links, Nodes = node_df,
Value = "Value", NodeGroup ="color", LinkGroup = "LinkColor",
NodeID="Node", Source="Source", Target="Target", fontSize = 22)
p1
Here we look at one example use case to find relationship between clusters within one sample or between two sample
#run prediction for 3 clusters
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
row_cluster <-length(unique(colData(mixedpop2)[,1]))
c_selectID <- 1
#top 200 gene markers distinguishing cluster 1
genes = DEgenes$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
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.1 (2024-06-14)
#> os Ubuntu 24.04.1 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2024-10-29
#> pandoc 2.7.3 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
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