vignettes/conclus_vignette.R
In PolinaPavlovich/CONCLUS: ScRNA-seq workflow CONCLUS - from CONsensus CLUSters to a meaningful CONCLUSion.
## ------------------------------------------------------------------------
# required R >= 3.4. In the current vignette, we used R 3.5.0.
library(conclus)
## ------------------------------------------------------------------------
# setting necessary parameters
# dataDirectory is output directory
dataDirectory <- "YourOutputDirectory"
experimentName <- "Bergiers"
## ------------------------------------------------------------------------
# please do not change the path and filenames
countMatrix <- read.delim(file.path(system.file("extdata", package = "conclus"),
"Bergiers_counts_matrix_filtered.tsv"),
stringsAsFactors = FALSE)
colData <- read.delim(file.path(system.file("extdata", package = "conclus"),
"Bergiers_colData_filtered.tsv"))
## ------------------------------------------------------------------------
# 1. Normalisation
sceObject <- conclus::normaliseCountMatrix(countMatrix, species = "mmu",
colData = colData)
## ------------------------------------------------------------------------
# checking what changed after the normalisation
dim(sceObject)
# show first columns and rows of the count matrix
SingleCellExperiment::counts(sceObject)[1:5,1:5]
# show first columns and rows of the normalized count matrix
Biobase::exprs(sceObject)[1:5,1:5]
# visualize first rows of metadata (coldata)
coldataSCE <- as.data.frame(SummarizedExperiment::colData(sceObject))
head(coldataSCE)
# visualize beginning of the rowdata containing gene information
rowdataSCE <- as.data.frame(SummarizedExperiment:::rowData(sceObject))
head(rowdataSCE)
## ----fig.height=5, fig.width=6-------------------------------------------
# 2. Test step (optional)
p <- conclus::testClustering(sceObject, dataDirectory, experimentName)
## ----fig.height=5, fig.width=6-------------------------------------------
# saved as "dataDirectory/test_clustering/test_tSNE.pdf"
p[[1]]
# saved as "dataDirectory/test_clustering/test_clustering.pdf"
p[[3]]
## ------------------------------------------------------------------------
initialisePath(dataDirectory)
# default parameters, can be selected by a user
PCs=c(4, 6, 8, 10, 20, 40, 50)
perplexities=c(30, 40)
randomSeed = 42
tSNEResults <- generateTSNECoordinates(sceObject, dataDirectory,
experimentName, PCs=PCs,
perplexities=perplexities,
randomSeed = randomSeed)
## ------------------------------------------------------------------------
ncol(tSNEResults)
# the third matrix of t-SNE coordinates with PC = 8 and perplixities = 30
# it is saved as "tsnes/Bergiers_tsne_coordinates_3_8PCs_30perp.tsv"
head(tSNEResults[1,3][[1]])
## ------------------------------------------------------------------------
SummarizedExperiment::colData(sceObject)$clusters = factor(c(rep(1, 100), rep(2, 200), rep(3, (ncol(sceObject)-300) ) ))
table(SummarizedExperiment::colData(sceObject)$clusters)
## ------------------------------------------------------------------------
epsilon=c(1.3, 1.4, 1.5)
minPoints=c(3, 4)
cores=14
message("Running dbscan using ", cores, " cores.")
dbscanResults <- conclus::runDBSCAN(tSNEResults, sceObject, dataDirectory,
experimentName, epsilon=epsilon,
minPoints=minPoints,
cores=cores)
## ------------------------------------------------------------------------
dim(dbscanResults)
dbscanResults[1:7, 1:10]
## ------------------------------------------------------------------------
clusteringMethod="ward.D2"
k=10 # parameter for cutree
message("Calculating cells similarity matrix.")
cellsSimilarityMatrix <- conclus::clusterCellsInternal(dbscanResults, sceObject, clusterNumber=k,
deepSplit=deepSplit, cores=cores,
clusteringMethod=clusteringMethod)[[2]]
sceObjectFiltered <- sceObject
print(table(SummarizedExperiment::colData(sceObjectFiltered)$clusters,
dnn=list("Cells distribuion by clusters")))
## ----fig.height=6, fig.width=7-------------------------------------------
colorPalette="default"
statePalette="default"
plotPDFcellSim = TRUE
orderClusters = FALSE
clustersNumber <- length(unique(SummarizedExperiment::colData(sceObjectFiltered)$clusters))
colorPalette <- conclus::choosePalette(colorPalette, clustersNumber)
# Plotting stability of clusters
conclus::plotCellSimilarity(sceObjectFiltered, cellsSimilarityMatrix, dataDirectory,
experimentName, colorPalette,
orderClusters = orderClusters,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
plotPDF = plotPDFcellSim,
returnPlot = TRUE)
## ------------------------------------------------------------------------
deepSplit = 0 # 0 to avoid cutreeDynamic, 1 to 4 to use it
deleteOutliers = FALSE
epsilon=c(1.3, 1.4, 1.5)
minPoints=c(3, 4)
cores=14
clusteringMethod="ward.D2"
k=10 # split the dendrogram with cutree function into 10 groups
clusteringResults <- conclus::runClustering(tSNEResults, sceObject, dataDirectory,
experimentName,
epsilon=epsilon, minPoints=minPoints,
k=k, deepSplit=deepSplit,
cores=cores,
clusteringMethod=clusteringMethod,
deleteOutliers = deleteOutliers,
PCs=PCs,
perplexities=perplexities,
randomSeed = randomSeed)
sceObjectFiltered <- clusteringResults[[1]]
cellsSimilarityMatrix <- clusteringResults[[2]]
print(table(SummarizedExperiment::colData(sceObjectFiltered)$clusters,
dnn=list("Cells distribuion by clusters")))
## ----fig.height=6, fig.width=7-------------------------------------------
colorPalette="default"
statePalette="default"
plotPDFcellSim = TRUE
orderClusters = FALSE
clustersNumber <- length(unique(SummarizedExperiment::colData(sceObjectFiltered)$clusters))
colorPalette <- conclus::choosePalette(colorPalette, clustersNumber)
# Plotting cluster stablility
conclus::plotCellSimilarity(sceObjectFiltered, cellsSimilarityMatrix, dataDirectory,
experimentName, colorPalette,
orderClusters = orderClusters,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
plotPDF = plotPDFcellSim,
returnPlot = TRUE)
## ------------------------------------------------------------------------
tSNEclusters <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "clusters", returnPlot = TRUE)
tSNEnoColor <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "noColor", returnPlot = TRUE)
if(any(colnames(SummarizedExperiment::colData(sceObjectFiltered)) %in% "state")){
tSNEstate <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "state", returnPlot = TRUE)
}
## ------------------------------------------------------------------------
tSNEclusters[[5]]
tSNEnoColor[[5]]
tSNEstate[[5]]
## ----fig.height=5.3, fig.width=6.5---------------------------------------
clustersSimilarityMatrix <-
conclus::calculateClustersSimilarity(cellsSimilarityMatrix,
sceObject = sceObjectFiltered,
clusteringMethod = "ward.D2")[[1]]
conclus::plotClustersSimilarity(clustersSimilarityMatrix,
sceObjectFiltered,
dataDirectory = dataDirectory,
experimentName = experimentName,
colorPalette = colorPalette,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
returnPlot = TRUE)
## ------------------------------------------------------------------------
conclus::rankGenes(sceObjectFiltered, clustersSimilarityMatrix, dataDirectory,
experimentName)
rankedGenesClus5 <- read.delim(file.path(dataDirectory, "marker_genes",
"Bergiers_cluster_5_genes.tsv"),
stringsAsFactors = FALSE)
head(rankedGenesClus5, n = 10)
## ------------------------------------------------------------------------
conclus::exportMatrix(cellsSimilarityMatrix, dataDirectory, experimentName,
"cellsSimilarityMatrix")
conclus::exportMatrix(clustersSimilarityMatrix, dataDirectory, experimentName,
"clustersSimilarityMatrix")
## ------------------------------------------------------------------------
# Reminder where we stopped.
# 2. Test step (optional)
#testClustering(sceObject, dataDirectory, experimentName)
# 3. Running the analysis and saving the consensus clustering solution
sceObjectCONCLUS <- conclus::runCONCLUS(sceObject, dataDirectory, experimentName,
plotPDFcellSim = TRUE, # FALSE for > 2500 cells
k = 10,
cores = 14, # 14 for servers, 1 for PC
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
deleteOutliers = FALSE # TRUE takes more time
)
conclus::exportClusteringResults(sceObjectCONCLUS, dataDirectory, experimentName,
"clusters_table.tsv")
## ------------------------------------------------------------------------
# 4. Plotting heatmaps
genesNumber <- 10
markersClusters <- conclus::getMarkerGenes(dataDirectory, sceObjectCONCLUS,
experimentName = experimentName,
genesNumber = genesNumber)
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- T # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008","#7a0f09"))(100),
returnPlot = TRUE,
width = 7.5, height = 6.5)
## ------------------------------------------------------------------------
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- F # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008","#7a0f09"))(100),
returnPlot = TRUE)
## ------------------------------------------------------------------------
clustersTable <- read.delim(file.path(dataDirectory, "output_tables", paste0(experimentName, "_clusters_table.tsv")),
stringsAsFactors = FALSE)
clustersTable$clusters[clustersTable$clusters == "9"] <- "newCluster"
clustersTable$clusters[clustersTable$clusters == "10"] <- "newCluster"
write.table(clustersTable, file.path(dataDirectory, "output_tables", paste0(experimentName, "_clusters_table.tsv")),
quote = FALSE, sep = "\t")
## ------------------------------------------------------------------------
# 5. Correcting clustering manually (optional)
sceObjectCONCLUS <- conclus::addClusteringManually(fileName = "clusters_table.tsv",
dataDirectory = dataDirectory,
experimentName = experimentName,
sceObject = sceObjectCONCLUS,
columnName = "clusters")
# 5.1 Redo the analysis with manual clustering (optional)
sceObjectCONCLUS <- conclus::runCONCLUS(sceObjectCONCLUS, dataDirectory, experimentName,
preClustered = TRUE,
tSNEalreadyGenerated = TRUE, # to use t-SNE coords from dataDirectory/tsnes
tSNEresExp = experimentName,
cores = 14, # 14 for servers, 1 for PC
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"))
## ------------------------------------------------------------------------
genesNumber <- 10
markersClusters <- getMarkerGenes(dataDirectory, sceObjectCONCLUS,
experimentName = experimentName,
genesNumber = genesNumber)
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- T # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008", "#921912"))(100),
returnPlot = TRUE,
width = 7, height = 5.5)
## ------------------------------------------------------------------------
# 6. Plot gene expression in a selected tSNE plot
conclus::plotGeneExpression("Ccl3", experimentName, dataDirectory,
sceObject = sceObjectCONCLUS,
tSNEpicture = 10, returnPlot = TRUE)
conclus::plotGeneExpression("Lama1", experimentName, dataDirectory,
sceObject = sceObjectCONCLUS,
tSNEpicture = 10, returnPlot = TRUE)
tSNEstate[[10]]
## ------------------------------------------------------------------------
# 7. getGenesInfo example
result <- getGenesInfo(markersClusters, groupBy = "clusters",
getUniprot = FALSE) # please change to getUniprot = TRUE
# 7.1 save the result
outputDir <- file.path(dataDirectory, "/marker_genes/getGenesInfo")
dir.create(outputDir, showWarnings=F)
write.table(result, file = file.path(outputDir,
"Bergiers_markersClusters_top10_clusters9_genesInfo.csv"),
quote = FALSE, sep = ";", row.names = FALSE)
## ------------------------------------------------------------------------
# 8. saveGenesInfo example
saveMarkersLists(experimentName, dataDirectory)
## ------------------------------------------------------------------------
saveGenesInfo(dataDirectory, sep = ";", header = TRUE,
startFromFile = 9, getUniprot = FALSE) # please change to getUniprot = TRUE
## ------------------------------------------------------------------------
saveRDS(sceObjectCONCLUS, file.path(dataDirectory, paste0("output_tables/",
experimentName, "_sceObjectAfterCONCLUS.rds")))
## ------------------------------------------------------------------------
sessionInfo()
PolinaPavlovich/CONCLUS documentation built on May 10, 2019, 2:42 p.m.
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## ------------------------------------------------------------------------
# required R >= 3.4. In the current vignette, we used R 3.5.0.
library(conclus)
## ------------------------------------------------------------------------
# setting necessary parameters
# dataDirectory is output directory
dataDirectory <- "YourOutputDirectory"
experimentName <- "Bergiers"
## ------------------------------------------------------------------------
# please do not change the path and filenames
countMatrix <- read.delim(file.path(system.file("extdata", package = "conclus"),
"Bergiers_counts_matrix_filtered.tsv"),
stringsAsFactors = FALSE)
colData <- read.delim(file.path(system.file("extdata", package = "conclus"),
"Bergiers_colData_filtered.tsv"))
## ------------------------------------------------------------------------
# 1. Normalisation
sceObject <- conclus::normaliseCountMatrix(countMatrix, species = "mmu",
colData = colData)
## ------------------------------------------------------------------------
# checking what changed after the normalisation
dim(sceObject)
# show first columns and rows of the count matrix
SingleCellExperiment::counts(sceObject)[1:5,1:5]
# show first columns and rows of the normalized count matrix
Biobase::exprs(sceObject)[1:5,1:5]
# visualize first rows of metadata (coldata)
coldataSCE <- as.data.frame(SummarizedExperiment::colData(sceObject))
head(coldataSCE)
# visualize beginning of the rowdata containing gene information
rowdataSCE <- as.data.frame(SummarizedExperiment:::rowData(sceObject))
head(rowdataSCE)
## ----fig.height=5, fig.width=6-------------------------------------------
# 2. Test step (optional)
p <- conclus::testClustering(sceObject, dataDirectory, experimentName)
## ----fig.height=5, fig.width=6-------------------------------------------
# saved as "dataDirectory/test_clustering/test_tSNE.pdf"
p[[1]]
# saved as "dataDirectory/test_clustering/test_clustering.pdf"
p[[3]]
## ------------------------------------------------------------------------
initialisePath(dataDirectory)
# default parameters, can be selected by a user
PCs=c(4, 6, 8, 10, 20, 40, 50)
perplexities=c(30, 40)
randomSeed = 42
tSNEResults <- generateTSNECoordinates(sceObject, dataDirectory,
experimentName, PCs=PCs,
perplexities=perplexities,
randomSeed = randomSeed)
## ------------------------------------------------------------------------
ncol(tSNEResults)
# the third matrix of t-SNE coordinates with PC = 8 and perplixities = 30
# it is saved as "tsnes/Bergiers_tsne_coordinates_3_8PCs_30perp.tsv"
head(tSNEResults[1,3][[1]])
## ------------------------------------------------------------------------
SummarizedExperiment::colData(sceObject)$clusters = factor(c(rep(1, 100), rep(2, 200), rep(3, (ncol(sceObject)-300) ) ))
table(SummarizedExperiment::colData(sceObject)$clusters)
## ------------------------------------------------------------------------
epsilon=c(1.3, 1.4, 1.5)
minPoints=c(3, 4)
cores=14
message("Running dbscan using ", cores, " cores.")
dbscanResults <- conclus::runDBSCAN(tSNEResults, sceObject, dataDirectory,
experimentName, epsilon=epsilon,
minPoints=minPoints,
cores=cores)
## ------------------------------------------------------------------------
dim(dbscanResults)
dbscanResults[1:7, 1:10]
## ------------------------------------------------------------------------
clusteringMethod="ward.D2"
k=10 # parameter for cutree
message("Calculating cells similarity matrix.")
cellsSimilarityMatrix <- conclus::clusterCellsInternal(dbscanResults, sceObject, clusterNumber=k,
deepSplit=deepSplit, cores=cores,
clusteringMethod=clusteringMethod)[[2]]
sceObjectFiltered <- sceObject
print(table(SummarizedExperiment::colData(sceObjectFiltered)$clusters,
dnn=list("Cells distribuion by clusters")))
## ----fig.height=6, fig.width=7-------------------------------------------
colorPalette="default"
statePalette="default"
plotPDFcellSim = TRUE
orderClusters = FALSE
clustersNumber <- length(unique(SummarizedExperiment::colData(sceObjectFiltered)$clusters))
colorPalette <- conclus::choosePalette(colorPalette, clustersNumber)
# Plotting stability of clusters
conclus::plotCellSimilarity(sceObjectFiltered, cellsSimilarityMatrix, dataDirectory,
experimentName, colorPalette,
orderClusters = orderClusters,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
plotPDF = plotPDFcellSim,
returnPlot = TRUE)
## ------------------------------------------------------------------------
deepSplit = 0 # 0 to avoid cutreeDynamic, 1 to 4 to use it
deleteOutliers = FALSE
epsilon=c(1.3, 1.4, 1.5)
minPoints=c(3, 4)
cores=14
clusteringMethod="ward.D2"
k=10 # split the dendrogram with cutree function into 10 groups
clusteringResults <- conclus::runClustering(tSNEResults, sceObject, dataDirectory,
experimentName,
epsilon=epsilon, minPoints=minPoints,
k=k, deepSplit=deepSplit,
cores=cores,
clusteringMethod=clusteringMethod,
deleteOutliers = deleteOutliers,
PCs=PCs,
perplexities=perplexities,
randomSeed = randomSeed)
sceObjectFiltered <- clusteringResults[[1]]
cellsSimilarityMatrix <- clusteringResults[[2]]
print(table(SummarizedExperiment::colData(sceObjectFiltered)$clusters,
dnn=list("Cells distribuion by clusters")))
## ----fig.height=6, fig.width=7-------------------------------------------
colorPalette="default"
statePalette="default"
plotPDFcellSim = TRUE
orderClusters = FALSE
clustersNumber <- length(unique(SummarizedExperiment::colData(sceObjectFiltered)$clusters))
colorPalette <- conclus::choosePalette(colorPalette, clustersNumber)
# Plotting cluster stablility
conclus::plotCellSimilarity(sceObjectFiltered, cellsSimilarityMatrix, dataDirectory,
experimentName, colorPalette,
orderClusters = orderClusters,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
plotPDF = plotPDFcellSim,
returnPlot = TRUE)
## ------------------------------------------------------------------------
tSNEclusters <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "clusters", returnPlot = TRUE)
tSNEnoColor <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "noColor", returnPlot = TRUE)
if(any(colnames(SummarizedExperiment::colData(sceObjectFiltered)) %in% "state")){
tSNEstate <- conclus::plotClusteredTSNE(sceObjectFiltered, dataDirectory, experimentName,
PCs=PCs, perplexities=perplexities, colorPalette = colorPalette,
columnName = "state", returnPlot = TRUE)
}
## ------------------------------------------------------------------------
tSNEclusters[[5]]
tSNEnoColor[[5]]
tSNEstate[[5]]
## ----fig.height=5.3, fig.width=6.5---------------------------------------
clustersSimilarityMatrix <-
conclus::calculateClustersSimilarity(cellsSimilarityMatrix,
sceObject = sceObjectFiltered,
clusteringMethod = "ward.D2")[[1]]
conclus::plotClustersSimilarity(clustersSimilarityMatrix,
sceObjectFiltered,
dataDirectory = dataDirectory,
experimentName = experimentName,
colorPalette = colorPalette,
statePalette = statePalette,
clusteringMethod = clusteringMethod,
returnPlot = TRUE)
## ------------------------------------------------------------------------
conclus::rankGenes(sceObjectFiltered, clustersSimilarityMatrix, dataDirectory,
experimentName)
rankedGenesClus5 <- read.delim(file.path(dataDirectory, "marker_genes",
"Bergiers_cluster_5_genes.tsv"),
stringsAsFactors = FALSE)
head(rankedGenesClus5, n = 10)
## ------------------------------------------------------------------------
conclus::exportMatrix(cellsSimilarityMatrix, dataDirectory, experimentName,
"cellsSimilarityMatrix")
conclus::exportMatrix(clustersSimilarityMatrix, dataDirectory, experimentName,
"clustersSimilarityMatrix")
## ------------------------------------------------------------------------
# Reminder where we stopped.
# 2. Test step (optional)
#testClustering(sceObject, dataDirectory, experimentName)
# 3. Running the analysis and saving the consensus clustering solution
sceObjectCONCLUS <- conclus::runCONCLUS(sceObject, dataDirectory, experimentName,
plotPDFcellSim = TRUE, # FALSE for > 2500 cells
k = 10,
cores = 14, # 14 for servers, 1 for PC
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
deleteOutliers = FALSE # TRUE takes more time
)
conclus::exportClusteringResults(sceObjectCONCLUS, dataDirectory, experimentName,
"clusters_table.tsv")
## ------------------------------------------------------------------------
# 4. Plotting heatmaps
genesNumber <- 10
markersClusters <- conclus::getMarkerGenes(dataDirectory, sceObjectCONCLUS,
experimentName = experimentName,
genesNumber = genesNumber)
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- T # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008","#7a0f09"))(100),
returnPlot = TRUE,
width = 7.5, height = 6.5)
## ------------------------------------------------------------------------
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- F # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008","#7a0f09"))(100),
returnPlot = TRUE)
## ------------------------------------------------------------------------
clustersTable <- read.delim(file.path(dataDirectory, "output_tables", paste0(experimentName, "_clusters_table.tsv")),
stringsAsFactors = FALSE)
clustersTable$clusters[clustersTable$clusters == "9"] <- "newCluster"
clustersTable$clusters[clustersTable$clusters == "10"] <- "newCluster"
write.table(clustersTable, file.path(dataDirectory, "output_tables", paste0(experimentName, "_clusters_table.tsv")),
quote = FALSE, sep = "\t")
## ------------------------------------------------------------------------
# 5. Correcting clustering manually (optional)
sceObjectCONCLUS <- conclus::addClusteringManually(fileName = "clusters_table.tsv",
dataDirectory = dataDirectory,
experimentName = experimentName,
sceObject = sceObjectCONCLUS,
columnName = "clusters")
# 5.1 Redo the analysis with manual clustering (optional)
sceObjectCONCLUS <- conclus::runCONCLUS(sceObjectCONCLUS, dataDirectory, experimentName,
preClustered = TRUE,
tSNEalreadyGenerated = TRUE, # to use t-SNE coords from dataDirectory/tsnes
tSNEresExp = experimentName,
cores = 14, # 14 for servers, 1 for PC
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"))
## ------------------------------------------------------------------------
genesNumber <- 10
markersClusters <- getMarkerGenes(dataDirectory, sceObjectCONCLUS,
experimentName = experimentName,
genesNumber = genesNumber)
orderClusters <- T # F will apply hierarchical clustering to all cells
orderGenes <- T # F will apply hierarchical clustering to all genes
meanCentered <- T # F to show normalized counts
conclus::plotCellHeatmap(markersClusters, sceObjectCONCLUS, dataDirectory,
experimentName,
paste0("clusters",
length(levels(SummarizedExperiment::colData(sceObjectCONCLUS)$clusters)),
"_meanCentered",meanCentered,
"_orderClusters",orderClusters,
"_orderGenes",orderGenes,"_top",
genesNumber, "markersPerCluster"),
meanCentered = meanCentered,
colorPalette = RColorBrewer::brewer.pal(10, "Paired"),
orderClusters = orderClusters,
orderGenes = orderGenes,
fontsize_row = 4,
statePalette = c("bisque", "cadetblue2",
"coral1", "cornflowerblue"),
color = colorRampPalette(c("#023b84","#4b97fc",
"#FEE395",
"#F4794E", "#D73027",
"#a31008", "#921912"))(100),
returnPlot = TRUE,
width = 7, height = 5.5)
## ------------------------------------------------------------------------
# 6. Plot gene expression in a selected tSNE plot
conclus::plotGeneExpression("Ccl3", experimentName, dataDirectory,
sceObject = sceObjectCONCLUS,
tSNEpicture = 10, returnPlot = TRUE)
conclus::plotGeneExpression("Lama1", experimentName, dataDirectory,
sceObject = sceObjectCONCLUS,
tSNEpicture = 10, returnPlot = TRUE)
tSNEstate[[10]]
## ------------------------------------------------------------------------
# 7. getGenesInfo example
result <- getGenesInfo(markersClusters, groupBy = "clusters",
getUniprot = FALSE) # please change to getUniprot = TRUE
# 7.1 save the result
outputDir <- file.path(dataDirectory, "/marker_genes/getGenesInfo")
dir.create(outputDir, showWarnings=F)
write.table(result, file = file.path(outputDir,
"Bergiers_markersClusters_top10_clusters9_genesInfo.csv"),
quote = FALSE, sep = ";", row.names = FALSE)
## ------------------------------------------------------------------------
# 8. saveGenesInfo example
saveMarkersLists(experimentName, dataDirectory)
## ------------------------------------------------------------------------
saveGenesInfo(dataDirectory, sep = ";", header = TRUE,
startFromFile = 9, getUniprot = FALSE) # please change to getUniprot = TRUE
## ------------------------------------------------------------------------
saveRDS(sceObjectCONCLUS, file.path(dataDirectory, paste0("output_tables/",
experimentName, "_sceObjectAfterCONCLUS.rds")))
## ------------------------------------------------------------------------
sessionInfo()
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