Clustering

What is clustering?

Graph-based clustering

Clustering algorithm

Rank neighbors

Creating subclusters

Clustering parameters

Visualizing clusters

Visualization parameters

Marker gene detection

Intro

findMarkers(), findAllMarkers(), findConservedMarkers()

Study design

Load data

Visualize by clusters and condition

findAllMarkers()

DefaultAssay 'RNA'

findConservedMarkers() for cluster 3

Visualize canonical markers in a FeaturePlot

RenameIdents

Annotating clusters and marker databases

Annotating rest of the clusters

Perform differential expression in CD16 Monocytes between conditions (findMarkers())

Visualize markers identified by findConservedMarkers() vs findMarkers()

Clustering of scRNA-seq data

Removal of technical noise

Finding variable genes

Measuring distance between clusters

Specifying the number of clusters

Representing the graph

Single-cell consensus clustering

Cutting the graph

Assessing differentially expressed genes

Validation of clusters

Transcriptomics 3 Course Overview

Course outline

Unsupervised machine learning methods

Principle Component Analysis (PCA)

Hierarchical clustering

Applications of clustering

Output of classification algorithms

Binary decision trees

Linear discriminant analysis

What is Transcriptomics 3?

1. Package Import

2. Data Import

3. Data QC and Inspection

4. Data Normalization

5. Data Clustering (PCA/UMAP)

6. Markers Identification

7. Putting all together

What is a marker gene?

Challenges in finding marker genes

Methods for bulk/total RNA-seq

Filtering out false positives

Bonferroni correction

Parameters for cluster marker gene detection

Marker gene results

Visualizing cluster marker genes

Clustering and detection of cluster marker genes

Filtering genes by adjusted p-value

Analysis steps for clustering cells and finding marker genes

What will you learn

Filtering out genes prior to statistical testing - why?

Find differentially expressed genes between clusters

How to retrieve marker genes for a particular cluster?

Visualize cluster marker genes - UMAP, USNE or PCA plot colored with marker gene expression - Violin plot

Reprogramming

Challenges of Integration

Cca Method

Harmony

Scanorama

Ram Usage

Cd16 Population Removal Data

Two Data Sets from Different Tissues

Summary

Clustering Visualization and Annotating the Single Cell Rna-Seq Clusters

Pca on the Expression Matrix

Cell Cycle

Clustering

Resolution

Pairwise Similarity

Marker Genes

intro

Preparing data

core scVI

marker genes

Differential expression

Introduction

Single cells

Modern scRNA-seq technologies

Other single cell assays

Deep representation learning in single cell genomics

scGen: predicting single-cell perturbation effects

Human cell atlas

Deep generative models for single-cell transcriptomics

Single-cell Variational Inference

Probabilistic annotation

Information constraints on Auto-Encoding Variational Bayes

Decision-making with Auto-Encoding Variational Bayes

Open-source scientific research

Welcome

Applying tags

Dataset preview

Filtering and plotting QC metrics

Total number of counts

Normalization

Neighbourhood graph generation

Clustering results

Grouping genes

Conclusion

Opening Remarks

Speaker Introduction

Gating strategy

Pathway analysis

Erythroid commitment

Evidence of myeloid commitment

Population 4 sub-clusters

Selection of cells for scRNA-seq

How many cells do you use for sorting?

Conclusion

What is scvi?

Downloading data

Adding cell-level metadata

What is a good scatter plot?

Subset and data object to highly variable genes

Nearest-neighbors graph

Pre-training the model

Comparison between groups

Combining data from two tissues

Cell converters

Celltypist transfer

scVI transfer

Integration

Dim reduction

Annotation