In vallotlab/IDclust: Iterative Differential Clustering for single-cell

knitr::opts_chunk$set(
  warning = FALSE, message = FALSE, tidy = TRUE, cache=TRUE
)
options(timeout = max(1000, getOption("timeout")))

In this article, we will show how to run IDclust with your own custom functions. The two 'hackable' part of IDclust are the processing and the differential functions. There are a few default functions present in the package, however based on your preferences you are able to customize both step quite easily.

As an example, we will analyse a scRNA dataset with 'monocle3' instead of the default Seurat for preprocessing as well as differential analysis.

library(IDclust)
library(Seurat)
library(monocle3)

Data

Take example scRNA data 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".
We load the data and transform it into a cellDataset to analyze it with 'monocle3'.

set.seed(47)
data(Seu, package = "IDclust")

Defining a preprocessing function

At each step of the 'iterative_differential_clustering' function, a cluster is re-processed. This means that the counts are re-normalized and that a new dimensionality reduction is calculated. Today we want to replace the default function by the processing used in 'monocle3' package.

We start by studying the 'preprocess_Seurat' function. It takes in entry 3 arguments:
'Seurat' - A Seurat object 'n_dims' - An integer specifying the number of first dimensions to keep in the dimensionality reduction step. * 'dim_red' - The name of the slot to save the dimensionality reduction at each step in the reducedDimNames(scExp).

It returns a Seurat object with the cell dimensionality reduction (embedding) stored in the 'dim_red' slot of the reducedDimNames of the object.

We therefore define a function that process the data using 'monocle3' package and matches the criteria describe above. We will have to convert the object to a 'CellDataset' object and back to a Seurat object.

# From monocle3 tutorial - https://cole-trapnell-lab.github.io/monocle3/docs/clustering/

processing_monocle3 <- function(Seu, n_dims = 100, dim_red = "PCA"){

  # Transform the Seurat object into CDS
  gene_metadata = Seu@assays$[e-mail]
  gene_metadata$gene = rownames(gene_metadata)
  gene_metadata$gene_short_name = rownames(gene_metadata)
  cds = monocle3::new_cell_data_set(
    expression_data = Seu@assays$RNA@counts,
    cell_metadata = [e-mail],
    gene_metadata = gene_metadata
  )

  # Process the cds
  cds <- monocle3::preprocess_cds(cds, num_dim = n_dims)
  cds <- monocle3::reduce_dimension(cds, preprocess_method = dim_red,
                                    reduction_method = dim_red)

  # Transform back into a Seurat object and keep the metadata
  Seu = Seurat::CreateSeuratObject(
    counts(cds),
    meta.data = [e-mail],
    assay = "RNA"
  )

  Seurat::Idents(Seu) = Seu$seurat_clusters

  # Add the reduced dimension
  Seu@reductions[[dim_red]] = Seurat::CreateDimReducObject(
    embeddings = reducedDim(cds, dim_red),
    key = paste0(dim_red, "_"),
    assay = "RNA"
    ) 

  # Return the Seurat object
  return(Seu)
}

We can now test the function, make sure that the object returned is a Seurat object and that the embedding is present in the object.

Seu_monocle = processing_monocle3(Seu, n_dims = 100, dim_red = "PCA")
class(Seu_monocle)

Seurat::DimPlot(Seu_monocle)
Seurat::DimPlot(Seu, reduction = "pca")

Defining a differential function

We now define the differential function that will use 'top_markers' function from 'monocle3' package.

We start by studying the 'differential_Seurat' function. It takes in entry 3 mandatory arguments:

• 'Seurat' - A Seurat object
• 'by' - A character specifying the column of clusters to compare
• logFC.th - the log2 FoldChange threshold above which a gene is considered as significantly differential
• qval.th - the adjusted p.value threshold below which a gene is considered as significantly differential

The function returns a data.frame containing the marker genes for each cluster. It contains a 'cluster' column, a 'gene' column.

differential_monocle3 <- function(Seu, by = "IDcluster", logFC.th = log2(1.5), qval.th = 0.01){

  # Transform the Seurat object into CDS
  gene_metadata = Seu@assays$[e-mail]
  gene_metadata$gene = rownames(gene_metadata)
  gene_metadata$gene_short_name = rownames(gene_metadata)
  cds = monocle3::new_cell_data_set(
    expression_data = Seu@assays$RNA@counts,
    cell_metadata = [e-mail],
    gene_metadata = gene_metadata
  )

  # Differential analysis the cds
  res <- monocle3::top_markers(
    cds,
    group_cells_by = by,
    genes_to_test_per_group = 200,
    verbose = FALSE
  )

  # Rename the column concerning gene and cluster
  colnames(res)[1] = "gene"
  colnames(res)[3] = "cluster"

  # We just use the adjusted p.value threshold
  res = res[which(
    res$marker_test_q_value < qval.th
  ), ]

  # Return the Seurat object
  return(res)
}

We can now test the function, make sure that the object returned is a data.frame containing the marker genes of each cluster as well as the cluster column.

res = differential_monocle3(Seu, by = "seurat_clusters", qval.th = 0.01)

head(res)

Defining a differential function

We can finally use our two function to run the 'iterative_differential_clustering' function. To do so, we pass our monocle3 customized functions to preprocessing_function and differential_function, and we make sure the dim_red and n_dims arguments are correctly set.

We first pre-process our object with our custom function :

data("Seu")
Seu_monocle = processing_monocle3(Seu, n_dims = 100, dim_red = "PCA")

set.seed(47)
output_dir = "~/Tests/IDC_monocle/"
if(!dir.exists(output_dir)) dir.create(output_dir)

Seu_monocle = iterative_differential_clustering(
    Seu_monocle,
    output_dir = output_dir,
    plotting = FALSE,
    saving = TRUE,
    n_dims = 50,
    dim_red = "PCA",
    processing_function = processing_monocle3,
    differential_function = differential_monocle3,
    logFC.th = log2(1.5),
    qval.th = 0.01
)

A 'IDcluster' column was added to the object, which we can retrieve and add to the original object. We can now plot the clusters found this way on the UMAP:

Seu$IDcluster = Seu_monocle$IDcluster
Seurat::DimPlot(Seu, reduction = "umap", group.by = "IDcluster")

We can also plot the cluster network using the summary and tghe

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

vallotlab/IDclust documentation built on July 5, 2024, 3:26 p.m.