In uzh/ezRun: An R meta-package for the analysis of Next Generation Sequencing Data

Started on r format(Sys.time(), "%Y-%m-%d %H:%M:%S")

library(SummarizedExperiment)
library(ggridges)
library(cowplot)
library(kableExtra)
library(pheatmap)
library(scater)
library(RColorBrewer)
library(plotly)
library(tidyverse)
library(SingleR)
library(AUCell)
library(HDF5Array)
library(ezRun)
library(clustree)

knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, knitr.table.format = "html")

sce <- loadHDF5SummarizedExperiment("sce_h5")
param = metadata(sce)$param
pvalue_allMarkers <- 0.05
output <- metadata(sce)$output
var_heigth <- 1

jsFile = system.file("extdata/enrichr.js", package="ezRun", mustWork=TRUE)
invisible(file.copy(from=jsFile, to=basename(jsFile), overwrite=TRUE))
cat(paste0("<SCRIPT language=\"JavaScript\" SRC=\"", basename(jsFile), "\"></SCRIPT>"))

Analysis results {.tabset}

Batch effects

We always look at our cells before deciding whether we need to perform integration. Te main goal of dataset integration is to identify shared cell states that are present across different datasets, in case they were collected from different individuals, experimental conditions, technologies, or even species. Large single-cell RNA sequencing projects usually need to generate data across multiple batches due to logistical constraints. This results in systematic differences in the observed expression in cells from different batches, which we refer to as “batch effects”. Batch effects are problematic as they can be major drivers of heterogeneity in the data, masking the relevant biological differences and complicating interpretation of the results. The UMAPs and the barplot below can help us to visualize if the clusters are balanced and composed by cells from the different batches. If we see clusters that are comprised of cells from a single batch, this indicates that cells of the same type are artificially separated due to technical differences between batches. In this case, we may also consider that there are cell types that are unique to each batch. If a cluster only contains cells from a single batch, one can always debate whether that is caused by technical differences or if there is truly a batch-specific subpopulation.

plotReducedDim(sce, dimred =  "UMAP_NOCORRECTED", colour_by = "Batch") + xlab("UMAP 1") + ylab("UMAP 2")
cellIdents_perBatch = data.frame(colData(sce)[,c("ident_noCorrected", "Batch")])
barplot = ggplot(data=cellIdents_perBatch, aes(x=cellIdents_perBatch[,1], fill=Batch)) + geom_bar(stat="Count")
barplot + labs(x="Cluster", y = "Number of cells", fill = "Batch")
cells_prop = cellsProportion(sce, groupVar1 = "ident_noCorrected", groupVar2 = "Batch")
kable(cells_prop,row.names=FALSE, format="html",caption="Cell proportions per batch") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "float_right")

#if there are only two samples these plots are the same as the previous ones
plotReducedDim(sce, dimred =  "UMAP_NOCORRECTED", colour_by = "Condition") + xlab("UMAP 1") + ylab("UMAP 2")
cellIdents_perCondition = data.frame(colData(sce)[,c("ident_noCorrected", "Condition")])
barplot = ggplot(data=cellIdents_perCondition, aes(x=cellIdents_perCondition[,1], fill=Condition)) + geom_bar(stat="Count")
barplot + labs(x="Cluster", y = "Number of cells", fill = "Condition")
cells_prop = cellsProportion(sce, groupVar1 = "ident_noCorrected", groupVar2 = "Condition")
kable(cells_prop,row.names=FALSE, format="html",caption="Cell proportions per condition") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "float_right")

Clustering

cat("We started by merging all the samples in one dataset and then used the SCtransform method from the Seurat package for normalizing, estimating the variance of the raw filtered data, and identifying the most variable genes. By default, SCtransform accounts for cellular sequencing depth, or nUMIs.")
if(identical(param$SCT.regress,"CellCycle")) {
  cat("We already checked cell cycle and decided that it does represent a major source of variation in our data, and this may influence clustering. Therefore, we regressed out variation due to cell cycle")
}
cat("As a result, SCTransform ranked the genes by residual variance and returned the 3000 most variant genes. Next, we performed PCA on the scaled data using the previously determined variable features. Taking as a distance metric the previously identified PCs, the cells clusters were then identified using a graph-based clustering approach where the cells are embedded in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar feature expression patterns, and then attempt to partition this graph into highly interconnected ‘communities’. The resolution is an important argument that sets the \"granularity\" of the downstream clustering and will need to be optimized for every individual experiment. Increased resolution values lead to a greater number of clusters, which is often required for larger datasets.\n")
cat("\n")
cat("The UMAPs below place similar cells together in low-dimensional space. The first UMAP represents cells according to the condition and the second one shows the graph-based common clusters that were found among the datasets.\n")

cat("After inspecting the datasets and observing that cells clustered by sample we decided to integrate samples using shared highly variable genes. Oftentimes, when clustering cells from multiple conditions there are condition-specific clusters and integration can help ensure the same cell types cluster together.\n We started by normalizing, estimating the variance of the raw filtered data, and identifying the most variable genes in each sample separately. For this, we used the SCtransform method from the Seurat package which accounts for cellular sequencing depth, or nUMIs by default.")
cat("\n")
if(identical(param$SCT.regress,"CellCycle")) {
  cat("We already checked cell cycle and decided that it does represent a major source of variation in our data, and this may influence clustering. Therefore, we regressed out variation due to cell cycle")
}
cat("\n")
cat("To integrate, we applied the following steps:")
cat("\n\n")
cat("**1. Perform canonical correlation analysis (CCA):** CCA identifies shared sources of variation between the conditions/groups. It is a form of PCA, in that it identifies the greatest sources of variation in the data, but only if it is shared or conserved across the conditions/groups (using the 3000 most variant genes from each sample).")
cat("\n\n")
cat("**2. Identify anchors or mutual nearest neighbors (MNNs) across datasets (sometimes incorrect anchors are identified):** MNNs are like good friends. For each cell in one sample, the cell's closest neighbor in the other sample is identified based on gene expression values as it's best neighbor.The reciprical analysis is performed, and if the two cells are 'best friends' in both directions, then those cells will be marked as anchors to 'anchor' the two datasets together.")
cat("\n\n")
cat("**3. Filter anchors to remove incorrect anchors:** Assess the similarity between anchor pairs by the overlap in their local neighborhoods (incorrect anchors will have low scores)")
cat("\n\n")
cat("**4. Integrate the conditions/datasets:** Use anchors and corresponding scores to transform the cell expression values, allowing for the integration of the different samples.")
cat("\n\n")
cat("If cell types are present in one dataset, but not the other, then the cells will still appear as a separate sample-specific cluster.")
cat("\n\n")
cat("Finally, the cells clusters were identified using a graph-based clustering approach where the cells are embedded in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar feature expression patterns, and then attempt to partition this graph into highly interconnected ‘communities’. The resolution is an important argument that sets the \"granularity\" of the downstream clustering and will need to be optimized for every individual experiment. Increased resolution values lead to a greater number of clusters, which is often required for larger datasets.")
cat("\n")
cat("\n")
cat("The UMAPs below place similar cells together in low-dimensional space after the integration of the datasets. The first UMAP represents cells according to the condition and the second UMAP shows the graph-based common clusters that were found among the datasets. The presence of visual clusters containing cells from the different datasets provides a comforting illusion that the integration was successful.")

plotReducedDim(sce, dimred="UMAP", colour_by="Batch")
cellIdents_perBatch = data.frame(colData(sce)[,c("ident", "Batch")])
barplot = ggplot(data=cellIdents_perBatch, aes(x=cellIdents_perBatch[,1], fill=Batch)) + geom_bar(stat="Count")
barplot + labs(x="Cluster", y = "Number of cells", fill = "Batch")
cells_prop = cellsProportion(sce, groupVar1 = "ident", groupVar2="Batch")
kable(cells_prop,row.names=FALSE, format="html",caption="Cell proportions") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "float_right")

plotReducedDim(sce, dimred="UMAP", colour_by="Condition")
cellIdents_perCondition = data.frame(colData(sce)[,c("ident", "Condition")])
barplot = ggplot(data=cellIdents_perCondition, aes(x=cellIdents_perCondition[,1], fill=Condition)) + geom_bar(stat="Count")
barplot + labs(x="Cluster", y = "Number of cells", fill = "Condition")
cells_prop = cellsProportion(sce, groupVar1 = "ident", groupVar2="Condition")
kable(cells_prop,row.names=FALSE, format="html",caption="Cell proportions") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "float_right")

plotReducedDim(sce, dimred="UMAP", colour_by="ident", text_by="ident", text_size=5) 

Clusters assessment

Segregation of clusters by various sources of uninteresting variation.

Once we have created the clusters we need to asses if the clustering was driven by technical artifacts or uninteresting biological variability, such as cell cycle, mitochondrial gene expression. We can explore whether the cells cluster by the different cell cycle phases. In such a case, we would have clusters where most of the cells would be in one specific phase. This bias could be taken into account when normalizing and transforming the data prior to clustering. We can also look at the total number of reads, genes detected and mitochondrial gene expression. The clusters should be more or less even but if we observe big differences among some of them for these metrics, we will keep an eye on them and see if the cell types we identify later can explain the differences.

sce$logRNA = log10(sce$nCount_RNA)
sce$logGenes = log10(sce$nFeature_RNA)

tryCatch({
  p1 = plotColData(sce, x="ident", y="CellCycle", colour_by="ident") + ggtitle("Cell cycle vs ident")
  p2 = plotReducedDim(sce, dimred="UMAP", colour_by="CellCycle", text_by="seurat_clusters", text_size=5, add_legend=TRUE)
  print(p1)
  print(p2)
}, error=function(e) print("Cell cycle was not calculated. Possible reasons: a bug in the app or using a different organism than human or mouse. Please, ask the developer."))
plotColData(sce, x="ident", y="nCount_RNA", colour_by="ident", add_legend=FALSE) + ggtitle("Number of UMIs vs ident")
plotReducedDim(sce, dimred="UMAP", colour_by="logRNA", text_by="seurat_clusters", text_size=5)
plotColData(sce, x="ident", y="nFeature_RNA", colour_by="ident", add_legend=FALSE) + ggtitle("Detected genes vs ident")
plotReducedDim(sce, dimred="UMAP", colour_by="logGenes", text_by="seurat_clusters", text_size=5)
plotColData(sce, x="ident", y="percent_mito", colour_by="ident", add_legend=FALSE) + ggtitle("Mito percentage vs ident")
plotReducedDim(sce, dimred="UMAP", colour_by="percent_mito", text_by="seurat_clusters", text_size=5)
plotColData(sce, x="ident", y="percent_ribo", colour_by="ident", add_legend=FALSE) + ggtitle("Ribo percentage vs ident")
plotReducedDim(sce, dimred="UMAP", colour_by="percent_ribo", text_by="seurat_clusters", text_size=5)
if (ezIsSpecified(param$controlSeqs)) {
  genesToPlot <- gsub("_", "-", param$controlSeqs)
  genesToPlot <- intersect(genesToPlot, rownames(sce))
  plotExpression(sce, genesToPlot, x="seurat_clusters", colour_by = "seurat_clusters", add_legend=FALSE)
  #plotReducedDim(sce, dimred="UMAP", colour_by=genesToPlot[1], text_by="seurat_clusters", text_size=5)
}

Clusters resolution

One of the most important parameters when clustering is k, the number of nearest neighbors used to construct the graph. This controls the resolution of the clustering where higher k yields a more inter-connected graph and broader clusters. Users can experiment with different values of k to obtain a satisfactory resolution. We recommend increasing the resolution when a rare population is expected. Below, it is shown a clustering tree that helps us to visualize the relationships between clusters at a range of resolutions. Each cluster forms a node in the tree and edges are constructed by considering the cells in a cluster at a lower resolution that end up in a cluster at the next highest resolution. By connecting clusters in this way, we can see how clusters are related to each other, which are clearly distinct and which are unstable. The size of each node is related to the number of cells in each cluster and the color indicates the clustering resolution. Edges are colored according to the number of cells they represent and the transparency shows the incoming node proportion, the number of cells in the edge divided by the number of samples in the node it points to.

if(param$batchCorrection == TRUE) {
   clustree::clustree(sce, prefix = "integrated_snn_res.")
} else {
  clustree::clustree(sce, prefix = "SCT_snn_res.")
}

Cluster markers

posMarkers = read_tsv("pos_markers.tsv")
posMarkers$cluster = as.factor(posMarkers$cluster)
posMarkers$gene = as.factor(posMarkers$gene)

cat("We found positive markers that defined clusters compared to all other cells via differential expression. The test we used was the Wilcoxon Rank Sum test. Genes with an average, at least 0.25-fold difference (log-scale) between the cells in the tested cluster and the rest of the cells and an adjusted p-value < 0.05 were declared as significant.")

cat(paste0("We found positive markers that defined clusters compared to all other cells via differential expression using a logistic regression test and including in the model the ", param$DE.regress, " as the batch effect. Genes with an average, at least 0.25-fold difference (log-scale) between the cells in the tested cluster and the rest of the cells and an adjusted p-value < 0.05 were declared as significant."))

caption ="Expression differences of cluster marker genes"
ezInteractiveTableRmd(posMarkers, digits=4, title=caption)

Marker plots

Here, we use a heatmap and a dotplot to visualize simultaneously the top 5 markers in each cluster. Be aware that some genes may be in the top markers for different clusters.

top5 <- posMarkers %>% group_by(cluster) 
top5 <- slice_max(top5, n = 5, order_by = avg_log2FC)
var_heigth <- nrow(top5)*0.2

plotHeatmap(sce, features=unique(top5$gene), center=TRUE, zlim=c(-2, 2),
            colour_columns_by = "ident", 
            order_columns_by = "ident",
            cluster_rows=TRUE,
            fontsize_row=8)
plotDots(sce, features=top5$gene, group="ident")

Cells annotation

The most challenging task in scRNA-seq data analysis is the interpretation of the results. Once we have obtained a set of clusters we want to determine what biological state is represented by each of them. To do this, we have implemented 2 different approaches which are explained below. However, these methods should be complemented with the biological knowledge from the researcher.

Using cluster markers

This approach consists in performing a gene set enrichment analysis on the marker genes defining each cluster. This identifies the pathways and processes that are (relatively) active in each cluster based on the upregulation of the associated genes compared to other clusters. For this, we use the tool Enrichr.

genesPerCluster <- split(posMarkers$gene, posMarkers$cluster)
jsCall = paste0('enrich({list: "', sapply(genesPerCluster, paste, collapse="\\n"), '", popup: true});')
enrichrCalls <- paste0("<a href='javascript:void(0)' onClick='", jsCall, 
                         "'>Analyse at Enrichr website</a>")
enrichrTable <- tibble(Cluster=names(genesPerCluster),
                         "# of posMarkers"=lengths(genesPerCluster),
                         "Enrichr link"=enrichrCalls)
kable(enrichrTable, format="html", escape=FALSE,
        caption=paste0("GeneSet enrichment: genes with pvalue ", pvalue_allMarkers)) %>%
kable_styling("striped", full_width = F, position = "left")

Using reference data

Another strategy for annotation is to compare the single-cell expression profiles with previously annotated reference datasets. Labels can then be assigned to each cell in our uncharacterized test dataset based on the most similar reference sample(s). This annotation can be performed on single cells or instead, it may be aggregated into cluster-level profiles prior to annotation. To do this, we use the SingleR method (Aran et al. 2019) for cell type annotation. This method assigns labels to cells based on the reference samples with the highest Spearman rank correlations and thus can be considered a rank-based variant of k-nearest-neighbor classification. To reduce noise, SingleR identifies marker genes between pairs of labels and computes the correlation using only those markers. It also performs a fine-tuning step for each cell where the calculation of the correlations is repeated with just the marker genes for the top-scoring labels. This aims to resolve any ambiguity between those labels by removing noise from irrelevant markers for other labels. SingleR contains several built-in reference datasets, mostly assembled from bulk RNA-seq or microarray data of sorted cell types. These built-in references are often good enough for most applications, provided that they contain the cell types that are expected in the test population.

singler.results.single <- metadata(sce)$singler.results$singler.results.single
singler.results.cluster <- metadata(sce)$singler.results$singler.results.cluster
singler.single.labels <- singler.results.single$labels
singler.cluster.labels<- singler.results.cluster$labels[match(colData(sce)[,"ident"], rownames(singler.results.cluster))]
var_heigth = length(unique(singler.single.labels))*0.5

cat("The two heatmaps below display the scores for all individual cells (left) and each original cluster (right) across all reference labels, which allows users to inspect the confidence of the predicted labels across the dataset. The bar Labels on the top shows the actual assigned label.\n")
cat("\n")
#while (dev.cur()>1) dev.off()
plotScoreHeatmap(singler.results.single)
plotScoreHeatmap(singler.results.cluster, clusters=rownames(singler.results.cluster))

cat("The single cell (top) and cluster (bottom) annotations are also shown on the UMAPs. Place the mouse over the cells to get information such as their UMAP coordinates, original cluster, the cells name and the label assigned by SingleR. You can also zoom in specific areas of the UMAP by dragging and drop with the mouse.\n")

cellInfo <- tibble(Cells=colnames(sce), Cluster=colData(sce)[,"seurat_clusters"],
                     SingleR.labels.cluster=singler.cluster.labels, SingleR.labels.single=singler.single.labels)  %>%
    left_join(as_tibble(reducedDim(sce, "UMAP"), rownames="Cells"))

nrOfLabels_cluster <- length(unique(cellInfo$SingleR.labels.cluster))
nrOfLabels_single <- length(unique(cellInfo$SingleR.labels.single))

if(nrOfLabels_single <= 9){
  colsLabels <- brewer.pal(nrOfLabels_single, "Set1")
}else{
  colsLabels <- colorRampPalette(brewer.pal(9, "Set1"))(nrOfLabels_single)
}

x <- list(title="UMAP_1", zeroline=FALSE)
y <- list(title="UMAP_2", zeroline=FALSE)

p1 <- plot_ly(cellInfo, x = ~UMAP_1, y = ~UMAP_2, color=~SingleR.labels.single,
        text = ~paste("Cluster: ", Cluster, 
                      "\nCell: ", Cells,
                      "\nSingleR.labels.cluster: ", SingleR.labels.single),
        type = 'scatter', mode = "markers", marker=list(size=5, opacity=0.5),
        colors=colsLabels) %>% layout(xaxis=x, yaxis=y)

p2 <- plot_ly(cellInfo, x = ~UMAP_1, y = ~UMAP_2, color=~SingleR.labels.cluster,
        text = ~paste("Cluster: ", Cluster, 
                      "\nCell: ", Cells,
                      "\nSingleR.labels.cluster: ", SingleR.labels.cluster),
        type = 'scatter', mode = "markers", marker=list(size=5, opacity=0.5),
        colors=colsLabels) %>%layout(xaxis=x, yaxis=y)
p1
p2

Using gene sets

We can also use sets of marker genes that are highly expressed in each cell. This does not require matching of individual cells to the expression values of the reference dataset, which is faster and more convenient when only the identities of the markers are available. In this case, we use sets of gene markers for individual cell types taken from the CellMarkers database which contains an accurate resource of cell markers for various cell types in tissues of human and mouse (Zhang X., Lan Y., Xu J., Quan F., Zhao E., Deng C., et al. (2019). CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–d728. 10.1093/nar/gky900). We use the AUCell package (Aibar et al. (2017) SCENIC: single-cell regulatory network inference and clustering. Nature Methods. doi: 10.1038/nmeth.4463) to identify marker sets that are highly expressed in each cell. AUCell uses the “Area Under the Curve” (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The AUC estimates the proportion of genes in the gene-set that are highly expressed in each cell. Cells expressing many genes from the gene-set will have higher AUC values than cells expressing fewer. Finally, it assigns cell type identity to each cell in the test dataset by taking the marker set with the top AUC as the label for that cell.

cells_AUC <- metadata(sce)$cells_AUC
cells_assignment <- AUCell_exploreThresholds(cells_AUC, plotHist=FALSE, assign=TRUE) 
cellsAssigned <- lapply(cells_assignment, function(x) x$assignment)
assignmentTable <- reshape2::melt(cellsAssigned, value.name="cell")
colnames(assignmentTable)[2] <- "geneSet"
tras_cells_AUC <- t(assay(cells_AUC))
full.labels <- colnames(tras_cells_AUC)[max.col(tras_cells_AUC)]
tab <- table(full.labels, colData(sce)[,"seurat_clusters"])
var_heigth <- nrow(tab)*0.5

cat("We can explore the cell assignment results using different plots. Below, we show a heatmap that represents the number of cells (in log scale) from each cluster that were assigned to the different cell types. After calculating an AUC score for each cell and cell type, we assign cell type identity by taking the cell type with the top AUC as the label for that cell. Some cell types may be missing because no cells obtained their top AUC score for it.")
cat("\n\n")

if (nrow(tab) > 2)
  pheatmap(log10(tab+10), color=viridis::viridis(100))

cat("The plots below show for every cell type:\n")
cat('\n')
cat("1) The distribution of the AUC values in the cells. The ideal situation will be a bi-modal distribution, in which most cells in the dataset have a low “AUC” compared to a population of cells with a higher value.  The size of the gene-set will also affect the results. With smaller gene-genes (fewer genes), it is more likely to get cells with AUC = 0. While this is the case of the “perfect markers” it is also easier to get it by chance with small datasets. The vertical bars correspond to several thresholds that could be used to consider a gene-set ‘active’. The thickest vertical line indicates the threshold selected by default: the highest value to reduce the false positives.\n")
cat('\n')
cat("2) The t-SNE can be colored based on the AUC scores. To highlight the cluster of cells that are more likely of the cell type according to the signatures, we split the cells into cells that passed the assignment threshold (colored in blue), and cells that didn’t (colored in gray).\n")
cat('\n')
cat("3) The last TSNE represents the AUC scores values. The darker a cell is the higher AUC score it obtained, i.e. the cell is more enriched in that cell type.")
cat('\n')
cellsTsne <- reducedDims(sce)$TSNE
filtered_cells_AUC <- cells_AUC[,colSums(assay(cells_AUC))>0]

AUCell_plotTSNE(tSNE=cellsTsne, exprMat=counts(sce), cellsAUC=cells_AUC)

Interactive explorer

The iSEE (Interactive SummarizedExperiment Explorer) explorer provides a general visual interface for exploring single cell data. iSEE allows users to simultaneously visualize multiple aspects of a given data set, including experimental data, metadata, and analysis results. Dynamic linking and point selection facilitate the flexible exploration of interactions between different data aspects. [Rue-Albrecht K, Marini F, Soneson C, Lun ATL (2018). “iSEE: Interactive SummarizedExperiment Explorer.” F1000Research, 7, 741. doi: 10.12688/f1000research.14966.1.]

The iSEE shiny app can be accessed through this link iSEE explorer{target="_blank"}

Data availability

Mean expression of every gene across the cells in each cluster

geneMeanPerCluster

Mean expression of every gene across all the cells

geneMeans

Positive markers of each cluster

posMarkers

The final Single Cell Experiment Object is here

Parameters

param[c("npcs", "resolution", "batchCorrection", "SCT.regress", "DE.method", "DE.regress", "tissue")]

SessionInfo

sessionInfo()

uzh/ezRun documentation built on May 4, 2024, 3:23 p.m.