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monocle3_pipe.md

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Data Availability

Please download:

  1. emptyDroplets_doublet_filtered_tabulamuris_mtx.rds
  2. Tabulamuris_cmd.txt
  3. Tabulamuris_genes.txt

Tabule Muris Example

1. First we install all the necessary packages.

Additional information on possible errors during installation process can be found HERE

if(!require(monocle3))
{
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install(version = "3.10")
  
  BiocManager::install(c('BiocGenerics', 'DelayedArray', 'DelayedMatrixStats',
                         'limma', 'S4Vectors', 'SingleCellExperiment',
                         'SummarizedExperiment', 'batchelor', 'Matrix.utils'))
  
  install.packages("devtools")
  devtools::install_github('cole-trapnell-lab/leidenbase')
  devtools::install_github('cole-trapnell-lab/monocle3')
  
  devtools::install_github('cole-trapnell-lab/monocle3', ref="develop")
  
  library(monocle3)
}

2. We load into the R environment the downloaded Tabula Muris datasets

# Load Tabula Muris dataset (already filterd)
mtx=readRDS("/path/to/emptyDroplets_doublet_filtered_tabulamuris_mtx.rds")
cell_metadata=read.table("/path/to/Tabulamuris_cmd.txt", header=T,row.names=1)
gene_metadata=read.table("/path/to/Tabulamuris_genes.txt", header=T, row.names=1)

3. Gene Filtering and normazlization

Data normalization addresses the unwanted biases arisen by count depth variability while preserving true biological differences.

# Gene filtering and data normalization
cds <- new_cell_data_set(as(mtx, "sparseMatrix"),cell_metadata = cell_metadata,gene_metadata = gene_metadata)
rm(mtx);gc()

4. Data summarization & Dimensionality Reduction

Dimensionality reduction aims to condense the complexity of the data into a lower-dimensional space by optimally preserving its key properties.

# PCA for data summarization
cds <- preprocess_cds(cds, num_dim = 50)

# Dimensionality reduction with UMAP 
cds <- reduce_dimension(cds) #dimensionality reduction, default value is UMAP

5. Clustering Analysis: how to identify cellular sub-populations

As transcriptionally distinct populations of cells usually correspond to distinct cell types, a key goal of scRNA-seq consists in the identification of cell subpopulations based on their transcriptional similarity. Thus, organizing cells into groups (i.e. clusters) can allow for de novo detection of cell types or identification of different subpopulations in a single cell state.

# Identify clusters
cds = cluster_cells(cds, cluster_method="louvain") #cell clustering using louvain algorithm

6. Plots

plot_cells(cds)