Monocle3 Tutorial Welcome! Download all data, slides, and scripts - - PowerPoint PPT Presentation

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Monocle3 Tutorial Welcome! Download all data, slides, and scripts - - PowerPoint PPT Presentation

Sep 18, 2023 131 likes •569 views

Monocle3 Tutorial Welcome! Download all data, slides, and scripts at: http://sta ff .washington.edu/hpliner/ Pace and content: We will move fairly fast - we hope experienced R users will be able to keep up, but expect newer users will follow the

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Monocle3 Tutorial

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Welcome!

Download all data, slides, and scripts at: http://staff.washington.edu/hpliner/ Pace and content: We will move fairly fast - we hope experienced R users will be able to keep up, but expect newer users will follow the presentation and explore the code at their own pace Ask questions!

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Acknowledgements

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Introducing Monocle3

  • Performs cell type and trajectory analysis simultaneously
  • Optimized for large datasets
  • Advanced differential expression tools

Brand new package (released yesterday!), help us with development! Update often, submit bug reports (including re website) via github, ask question via google group

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Monocle3? Monocle? Monocle 3-alpha?

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Warning!

Monocle3 is fully independent Please remember that cds’ generated in monocle/monocle 2 will not work in monocle3

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Monocle3

Trajectory analysis Cluster analysis

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Find more info on the Monocle3 website

Website: https://cole-trapnell-lab.github.io/ monocle3/monocle3_docs/

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Monocle3 features

  • Cluster, classify and count cells
  • Construct single-cell trajectories
  • Perform differential expression
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Monocle3 versus Monocle 2

Monocle3 Monocle 2 loading library(monocle3) library(monocle) Data structure base SingleCellExperiment ExpressionSet UMAP ✔ ✘ tSNE ✔ ✔ Differential expression with trajectories Graph-based BEAM/single branch

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Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Pre-process

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Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Reduce dimension (UMAP)

McInnes & Healy 2018

Pre-process

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Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Reduce dimension (UMAP)

McInnes & Healy 2018

Pre-process Cluster

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Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Reduce dimension (UMAP)

McInnes & Healy 2018

Pre-process Cluster Learn graph

Wolf et al 2018

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Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Reduce dimension (UMAP)

McInnes & Healy 2018

Pre-process Cluster Learn graph

Wolf et al 2018

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Trajectory 1 Trajectory 2

Monocle3

http://cole-trapnell-lab.github.io/monocle3/

Reduce dimension (UMAP)

McInnes & Healy 2018

Pre-process Cluster Learn graph

Wolf et al 2018

Trajectory analysis

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Workflow steps

Pre-process data Cluster cells Compare clusters Non-linear dimension reduction High dimension noisy scRNA-seq dataset Normalization +PCA t-SNE UMAP Identify top markers Targeted contrasts

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Software & installation

  • Monocle3 runs in the R statistical computing environment
  • Needs R version 3.5 or higher
  • To install from the Trapnell Lab GitHub
  • To test the installation

library(monocle3) devtools :: install_github('cole-trapnell-lab/monocle3')

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  • C. elegans L2 data

Cao, Packer, Science 2017

  • C. elegans group, Universiteit Utrecht
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Import C. elegans data

http://staff.washington.edu/hpliner/

cds <- readRDS(‘~/Downloads/worm_l1_cds.rds’)

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Import your own data

  • To create your own cds object, use gene x cell matrix (mat),

gene data frame (gene_meta) and cell data frame (cell_meta):

  • Monocle3 can also import data from 10x experiments directly into

cds objects

cds <- new_cell_dataset(mat, cell_meta, gene_meta) cds <- load_cellranger_data("cell_ranger_output")

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Accessor functions for cds

  • exprs/counts: A numeric matrix of expression values, where rows

are genes and columns are cells

  • pData/colData: An object where rows are cells and columns are

cell attributes such as cell type, culture condition, etc

  • fData/rowData: An object where rows are features (e.g. genes)

and columns are gene attributes such as biotype, gc content, etc

colData(cds) rowData(cds) counts(cds)

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cell_type culture_cond Size_Factor cell_1 cell_2 cell_3 cell_n

colData(cds) rowData(cds)

biotype gc_content gene_1 gene_2 gene_3 gene_n

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Pre-process & batch

Cluster cells Compare clusters Non-linear dimension reduction Pre-process data High dimension noisy scRNA-seq dataset Normalization +PCA t-SNE UMAP Identify top markers Targeted contrasts

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Preprocess the data

  • Preprocess data with initial dimensionality reduction

cds <- preprocess_cds(cds, num_dim = 100)

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Reduce dimension

Pre-process data Cluster cells Compare clusters High dimension noisy scRNA-seq dataset Normalization +PCA Non-linear dimension reduction t-SNE UMAP Identify top markers Targeted contrasts

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Reduce dimensions

cds <- reduce_dimension(cds) plot_cells(cds)

Reduce dimension Pre-process

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Cluster cells

Pre-process data Compare clusters Non-linear dimension reduction High dimension noisy scRNA-seq dataset Cluster cells Normalization +PCA t-SNE UMAP Identify top markers Targeted contrasts

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Next we cluster the cells

  • The cluster_cells function of monocle3 allows users to group similar

cells according to global expression profiles

  • In addition, partitions (super-clusters) are calculated for dividing

distinct trajectories

  • You can access values using the following:

cds <- cluster_cells(cds) head(partitions(cds, reduction_method = "UMAP")) head(clusters(cds, reduction_method = "UMAP"))

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Group cells by partitions

cds <- cluster_cells(cds) plot_cells(cds, color_cells_by="partition", group_cells_by="partition")

Reduce dimension Pre-process Cluster

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cds <- cluster_cells(cds) plot_cells(cds)

Group cells by clusters

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Compare clusters

Pre-process data Cluster cells Non-linear dimension reduction High dimension noisy scRNA-seq dataset Normalization +PCA t-SNE UMAP Compare clusters Identify top markers Targeted contrasts

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\

Reduce dimension Pre-process Cluster Learn graph

Compare expression between clusters Find cluster markers

Gene1 Gene2

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cds_subset <- choose_cells(cds)

Compare selected clusters

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gene_fits <- fit_models(cds_subset[1:100,], model_formula_str = "~cluster") fit_coefs <- coefficient_table(gene_fits) head(fit_coefs)

Compare selected clusters

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Find cluster markers

marker_genes <- top_markers(cds) tops_sig <- subset(marker_genes, marker_test_q_value < .05)

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Annotating by cell type

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Annotating by cell type

plot_cells(cds, color_by="cao_cell_type")

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Simplifying future workflow with Garnett

generate_garnett_marker_file(marker_genes)

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Further analyses with Monocle3

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Trajectory analysis Graph-based differential expression

Further analyses with Monocle3

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Thank you! Questions?

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