In this article, we will show how to run IDclust on a SingleCellExperiment object of a single- cell RNA dataset of the mouse brain from “Joint profiling of histone modifications and transcriptome in single cells from mouse brain,Chenxu Zhu, Yanxiao Zhang, Yang Eric Li, Jacinta Lucero, . Margarita Behrens, Bing Ren, Nature Methods, 2021 Paired-Tag”

Classical analysis of scRNA dataset with ChromSCape

library(IDclust)
library(ChromSCape)

Data

Download, extract & format scRNA of the mouse brain (Zhu et al., 2021) from the GEO portal.

set.seed(47)
# Download dataset
temp = tempfile()
tempdir = tempdir()
download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE152020&format=file&file=GSE152020%5FPaired%2DTag%5FH3K27ac%5FDNA%5Ffiltered%5Fmatrix%2Etar%2Egz",
    temp, quiet = TRUE)
untar(temp, exdir = tempdir)

# Download metadata
annot = tempfile()
download.file("http://catlas.org/pairedTag/cellbrowser/Paired-tag/meta.tsv", annot,
    quiet = TRUE)
metadata = read.table(annot, sep = "\t", header = TRUE)
rownames(metadata) = metadata$Cell_ID
metadata = metadata[which(metadata$Target == "H3K27ac"), ]

features = read.table(file.path(tempdir, "04.Paired-Tag_H3K27ac_DNA_filtered_matrix",
    "bins.tsv"), row.names = NULL, header = F, sep = "\t")[, 1, drop = F]
write.table(features, file = file.path(tempdir, "04.Paired-Tag_H3K27ac_DNA_filtered_matrix",
    "features.tsv"), row.names = F, col.names = F, quote = F)

# Create SingleCellExperiment object
out = ChromSCape::read_sparse_matrix(file.path(tempdir, "04.Paired-Tag_H3K27ac_DNA_filtered_matrix"))
out$datamatrix = out$datamatrix[, match(metadata$Cell_ID, gsub(".*_", "", colnames(out$datamatrix)))]
scExp = ChromSCape::create_scExp(out$datamatrix, out$annot_raw)
SummarizedExperiment::colData(scExp) = cbind(SingleCellExperiment::colData(scExp),
    metadata)

# Subsample cells
scExp = scExp[, sample(ncol(scExp), 5000, replace = F)]

ChromSCape Analysis

We then run a ChromSCape LSI normalization and dimensionality reduction. We can plot the UMAP and color by the cell type.

scExp <- ChromSCape::find_top_features(scExp, n = 1e+05, keep_others = FALSE, verbose = FALSE)
scExp <- ChromSCape::feature_annotation_scExp(scExp, ref = "mm10")
scExp <- ChromSCape::normalize_scExp(scExp, type = "TFIDF")
scExp <- ChromSCape::reduce_dims_scExp(scExp, dimension_reductions = c("PCA", "UMAP"),
    n = 10, remove_PC = "Component_1", verbose = F)

ChromSCape::plot_reduced_dim_scExp(scExp, color_by = "Annotation", reduced_dim = "UMAP")

Classical Louvain clustering

We can run a classical Louvain clustering to see the clusters.

scExp <- ChromSCape::find_clusters_louvain_scExp(scExp)

ChromSCape::plot_reduced_dim_scExp(scExp, color_by = "cell_cluster", reduced_dim = "UMAP")

Iterative Differential Clustering

We can now run the Iterative Differential Clustering, that will re-process and re-cluster each cluster iteratively and find subclusters with significant differences between each other.

By default for a SingleCellExperiment object the processing_ChromSCape function is used for re-processing and the differential_ChromSCape function is used to find significant marker genes.

set.seed(47)
output_dir = "~/Tests/IDC_scExp/"
if (!dir.exists(output_dir)) dir.create(output_dir)
scExp = iterative_differential_clustering(scExp, output_dir = output_dir, plotting = FALSE,
    saving = TRUE, n_dims = 10, dim_red = "PCA", vizualization_dim_red = "UMAP",
    processing_function = processing_ChromSCape, differential_function = differential_ChromSCape,
    logFC.th = log2(1.5), qval.th = 0.01, quantile.activation = 0.7, min_frac_cell_assigned = 0.1,
    limit = 5, limit_by_proportion = NULL, starting.resolution = 0.1, starting.k = 100,
    resolution = 0.8, k = 100, verbose = FALSE)

## ChromSCape::differential_activation - Calculating differential activation for A1 .
## ChromSCape::differential_activation - Calculating differential activation for A2 .
## ChromSCape::differential_activation - Calculating differential activation for B1 .
## ChromSCape::differential_activation - Calculating differential activation for B2 .
## ChromSCape::differential_activation - Calculating differential activation for B3 .
## ChromSCape::differential_activation - Calculating differential activation for B4 .
## ChromSCape::differential_activation - Calculating differential activation for B5 .
## ChromSCape::differential_activation - Calculating differential activation for B1 .
## ChromSCape::differential_activation - Calculating differential activation for B2 .
## ChromSCape::differential_activation - Calculating differential activation for B3 .
## ChromSCape::differential_activation - Calculating differential activation for B4 .
## ChromSCape::differential_activation - Calculating differential activation for B5 .
## ChromSCape::differential_activation - Calculating differential activation for C1 .
## ChromSCape::differential_activation - Calculating differential activation for C2 .
## ChromSCape::differential_activation - Calculating differential activation for C1 .
## ChromSCape::differential_activation - Calculating differential activation for C2 .
## ChromSCape::differential_activation - Calculating differential activation for C3 .
## ChromSCape::differential_activation - Calculating differential activation for C1 .
## ChromSCape::differential_activation - Calculating differential activation for C2 .
## ChromSCape::differential_activation - Calculating differential activation for C3 .
## ChromSCape::differential_activation - Calculating differential activation for C1 .
## ChromSCape::differential_activation - Calculating differential activation for C2 .
## ChromSCape::differential_activation - Calculating differential activation for C1 .
## ChromSCape::differential_activation - Calculating differential activation for C2 .
## ChromSCape::differential_activation - Calculating differential activation for C3 .
## ChromSCape::differential_activation - Calculating differential activation for C4 .
## ChromSCape::differential_activation - Calculating differential activation for D1 .
## ChromSCape::differential_activation - Calculating differential activation for D2 .

We can now read in the output ‘IDC_summary’ object and plot the cluster hierarchies compared to the author clusters. On this plot, each node is a cluster. The colors represent the distribution of author cluster within each cluster. Link between nodes represent a hierarchy in the iteration. The width of the edges is proportional to the number of genes found.

IDC_summary = qs::qread(file.path(output_dir, "IDC_summary.qs"))
plot_cluster_network(scExp, IDC_summary = IDC_summary, color_by = "Annotation", node_size_factor = 4,
    legend = FALSE)

##     Annotation Annotation_color
## 1   Astro_Myoc        deeppink2
## 2   Astro_Nnat   paleturquoise4
## 3          CA1          tomato4
## 4         CA23            gold3
## 5          CGE           violet
## 6           CT    palegoldenrod
## 7           DG  darkolivegreen4
## 8  Endothelial          salmon1
## 9    Ependymal    mediumpurple2
## 10         L23            plum3
## 11          L4    darkseagreen3
## 12          L5     mediumorchid
## 13          L6           azure4
## 14   Microglia      aquamarine2
## 15          NP          orchid3
## 16  Oligo_MFOL        peachpuff
## 17   Oligo_MOL       steelblue2
## 18         OPC       chocolate4
## 19          PT            snow2
## 20       Pvalb           ivory4
## 21         Sst     deepskyblue3
## 22   Subiculum     lightyellow2

A ‘IDcluster’ column was added to the SingleCellExperiment object, which we can now project the cluster found this way on the UMAP.

ChromSCape::plot_reduced_dim_scExp(scExp, reduced_dim = "UMAP", color_by = "IDcluster",
    annotate_clusters = F)

We can also plot particular marker genes in the cluster network by changing the ‘color_by’ parameter to a gene present in the SingleCellExperiment object.

plot_cluster_network(scExp, IDC_summary = IDC_summary, color_by = "Foxg1", threshold_to_define_feature_active = 2,
    legend = FALSE)

##      Foxg1 Foxg1_color
## 1   Active         red
## 2 Inactive      grey85

Tutorial for scEpigenomics object (SingleCellExperiment)

Pacome Prompsy

5/17/2022

Classical analysis of scRNA dataset with ChromSCape

Data

ChromSCape Analysis

Classical Louvain clustering

Iterative Differential Clustering