clustering methods for scrna seq
play

Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F - PowerPoint PPT Presentation

Apr 06, 2024 •534 likes •742 views

Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome Mouse epithelium dataset 1 2 Cell Stem Cell, Fletcher et al, Deconstructing Olfactory Stem Cell Trajectories at

  1. Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  2. Mouse epithelium dataset 1 2 Cell Stem Cell, Fletcher et al, Deconstructing Olfactory Stem Cell Trajectories at Single Cell Resolution SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  3. Clustering methods hierarchical clustering k-means clustering Challenges What is the number of clusters? What is a cell type or the expected granularity? Scalability: in scRNA-Seq experiments the number of cells could be millions, tools developed for single-cell data don't scale well. SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  4. Create Seurat object library(Seurat) library(SingleCellExperiment) seuset <- CreateSeuratObject( raw.data = assay(sce), normalization.method = "LogNormalize", scale.factor = 10000, meta.data = as.data.frame(colData(sce)) ) seuset <- ScaleData(object = seuset) seuset An object of class seurat in project SeuratProject 100 genes across 94 samples. SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  5. Perform Clustering seuset <- FindClusters( object = seuset, reduction.type = "pca", dims.use = 1:10, resolution = 1.8, print.output = FALSE ) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  6. Plot Clusters PCAPlot( object = seuset, group.by = "ident", pt.shape = "publishedClusters") SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  7. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

  8. Differential expression analysis S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  9. Typical work�ow SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  10. DE methods Bulk RNA-Seq methods: edgeR DESeq2 Single-cell methods: Single Cell Differential Expression (SCDE) Model-based Analysis of Single-cell Transcriptomics (MAST) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  11. Fit zero-in�ated regression using MAST library(MAST) zlm <- zlm(~ celltype + cngeneson, sce) summary <- summary(zlm, doLRT = "celltype9") summary Fitted zlm with top 2 genes per contrast: ( log fold change Z-score ) primerid celltype9 celltype15 cngeneson Cyp2a5 -14.5 -7.6* 1.5 Gap43 21.9* 2.3 3.0 Ncam1 15.4 1.3 4.7* Stmn2 16.7 2.2 5.0* Stmn3 25.8* 1.1 1.9 Ugt2a1 -14.7 -8.0* 2.8 SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  12. Fit zero-in�ated regression using MAST # get summary table fit <- summary$datatable # pvalues and logFC fit <- merge(fit[contrast=='celltype9' & component=='H', .(primerid, `Pr(>Chisq)`)], fit[contrast=='celltype9' & component=='logFC', .(primerid, coef)], by='primerid') SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  13. Adjusted p-values # adjusted p-values fit[, padjusted:=p.adjust(`Pr(>Chisq)`, 'fdr')] # result table res = data.frame(gene = fit$primerid, pvalue = fit[,'Pr(>Chisq)'], padjusted = fit$padj, logFC = fit$coef) head(res) gene Pr..Chisq. padjusted logFC 1 1810011O10Rik 7.256038e-15 1.422753e-14 -10.767717 2 5730409K12Rik 1.446961e-24 1.607735e-23 12.544552 3 Actr1b 1.753458e-07 2.112600e-07 7.043411 4 Ado 2.940357e-08 3.630071e-08 7.980018 5 Ak1 3.579921e-17 9.944224e-17 11.003118 6 Anxa1 4.423790e-10 6.059987e-10 -7.718933 SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  14. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

  15. Visualization of DE genes S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  16. Volcano plot ggplot(res, aes(x=logFC, y=-log10(padjusted), color=mostDE)) + geom_point() + ggtitle("Volcano") + xlab("log2 FC") + ylab("-log10 adjusted p-value") SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  17. Heatmap library(NMF) norm <- assay(sce[mostDE, ], "logcounts") norm <- as.matrix(norm) aheatmap(norm, annCol = colData(sce)$publishedClusters) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  18. Typical work�ow https://hemberg-lab.github.io/scRNA.seq.course/index.html A step-by-step work�ow for low-level analysis of single-cell RNA-seq data (Lun et al). Bioconductor work�ow for single-cell RNA sequencing (Perraudeau et al). SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  19. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods 1 2 What is Clustering? What is Clustering? Typical

647 views • 18 slides
Cl Clustering t i A Categorization of Major Clustering Methods Partitioning Methods

Cl Clustering t i A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Cl Clustering t i A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Hierarchical Methods 1 2 What is Clustering?

617 views • 24 slides
Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Preview ! Introduction Lecture 10 ! Partitioning methods Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is Clustering? Examples of Clustering Applications ! Cluster: a collection of data objects !

301 views • 8 slides
scRNA-seq Differential expression analysis methods Olga Dethlefsen NBIS, National Bioinformatics

scRNA-seq Differential expression analysis methods Olga Dethlefsen NBIS, National Bioinformatics

scRNA-seq Differential expression analysis methods Olga Dethlefsen NBIS, National Bioinformatics Infrastructure Sweden October 2017 Olga (NBIS) scRNA-seq de October 2017 1 / 34 Outline Introduction: what is so special about DE with

749 views • 35 slides
Spectral Clustering Spectral Clustering? Spectral methods Methods using eigenvectors of

Spectral Clustering Spectral Clustering? Spectral methods Methods using eigenvectors of

Spectral Clustering Spectral Clustering? Spectral methods Methods using eigenvectors of some matrices Involve eigen-decomposition (or spectral decomposition) Seungjin Choi Spectral clustering methods: Algorithms that cluster data

501 views • 10 slides
Some Clustering Methods on Some Clustering Methods on Some Clustering Methods on Dissimilarity

Some Clustering Methods on Some Clustering Methods on Some Clustering Methods on Dissimilarity

Some Clustering Methods on Some Clustering Methods on Some Clustering Methods on Dissimilarity or Similarity Matrices: Dissimilarity or Similarity Matrices: Dissimilarity or Similarity Matrices: Uncovering Clusters in WEB Content, Structure

876 views • 34 slides
Clustering: evolution of methods to meet new challenges C. Biernacki ee Clustering, Orange

Clustering: evolution of methods to meet new challenges C. Biernacki ee Clustering, Orange

Introduction Methods &amp; questions Model-based clustering Illustrations Challenges Clustering: evolution of methods to meet new challenges C. Biernacki ee Clustering, Orange Labs, October 20 th 2015 Journ 1/54 Introduction

1.13k views • 54 slides
Single-cell transcriptomics (scRNA-seq) Eukaryotic Single Cell Genomics facility Applications for

Single-cell transcriptomics (scRNA-seq) Eukaryotic Single Cell Genomics facility Applications for

Henrik Gezelius May 21, 2018 Single-cell transcriptomics (scRNA-seq) Eukaryotic Single Cell Genomics facility Applications for scRNA-sequencing Heterogeneity analysis Cell type identification Lineage tracing, cellular states in

2.1k views • 24 slides
Feature selection, dimensionality reduction, clustering, marker gene identification, and

Feature selection, dimensionality reduction, clustering, marker gene identification, and

Feature selection, dimensionality reduction, clustering, marker gene identification, and visualization in scRNA-Seq Cihan Oguz Bioinformatics Analyst NIAID Collaborative Bioinformatics Resource (NCBR) Leidos Biomedical Research, Inc.

867 views • 25 slides
Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering K-means Mixture of Gaussians Expectation Maximization Variational Methods 2 Clustering 3 K- means clustering What is clustering?

1.32k views • 66 slides
Correlation Clustering Bounding and Comparing Methods Beyond ILP Micha Elsner and Warren Schudy

Correlation Clustering Bounding and Comparing Methods Beyond ILP Micha Elsner and Warren Schudy

Correlation Clustering Bounding and Comparing Methods Beyond ILP Micha Elsner and Warren Schudy Department of Computer Science Brown University May 26, 2009 Document clustering rec.motorcycles soc.religion.christian 2 Document clustering:

802 views • 59 slides
PCA and clustering STEPHANIE J. SPIELMAN, PHD BIO5312, FALL 2017 Exploratory methods for high-

PCA and clustering STEPHANIE J. SPIELMAN, PHD BIO5312, FALL 2017 Exploratory methods for high-

PCA and clustering STEPHANIE J. SPIELMAN, PHD BIO5312, FALL 2017 Exploratory methods for high- dimensional data Principal components analysis (PCA) Note there are many similar methods, e.g. linear discriminant analysis Clustering

610 views • 47 slides
scRNA-seq Differential expression analyses Olga Dethlefsen olga.dethlefsen@nbis.se NBIS,

scRNA-seq Differential expression analyses Olga Dethlefsen olga.dethlefsen@nbis.se NBIS,

scRNA-seq Differential expression analyses Olga Dethlefsen olga.dethlefsen@nbis.se NBIS, National Bioinformatics Infrastructure Sweden May 2018 Olga (NBIS) scRNA-seq DE May 2018 1 / 43 Outline Outline Olga (NBIS) scRNA-seq DE May 2018

991 views • 54 slides
Categorical Data Clustering Using Statistical Methods and Neural Networks . Kudov 1 , H.

Categorical Data Clustering Using Statistical Methods and Neural Networks . Kudov 1 , H.

Introduction Clustering Statistical methods Neural Networks Experiments Conclusion Categorical Data Clustering Using Statistical Methods and Neural Networks . Kudov 1 , H. Rezankov 2 , D. Hsek 1 , V. Snel 3 P 1 Institute of

929 views • 33 slides
Bayesian Methods for Graph Clustering P. Latouche, E. Birmel e Laboratoire Statistique et

Bayesian Methods for Graph Clustering P. Latouche, E. Birmel e Laboratoire Statistique et

Bayesian Methods for Graph Clustering P. Latouche, E. Birmel e Laboratoire Statistique et G enome (UMR CNRS 8071, INRA 1152) Journ ees MAS, August 2008 P. Latouche (Stat. &amp; G enome) Bayesian Methods for Graph Clustering

485 views • 22 slides
Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering? Aykut Erdem May 2016 Hacettepe University Last time K-Means An iterative clustering algorithm - Initialize: Pick K random points as cluster

932 views • 64 slides
Team Dynamics &amp; Management Team Dynamics &amp; Management Team structures Why teams?

Team Dynamics &amp; Management Team Dynamics &amp; Management Team structures Why teams?

Team Dynamics &amp; Management Team Dynamics &amp; Management Team structures Why teams? Belbins team roles Handys team roles Team life-cycle Elements of management Management versus leadership Leadership 2

971 views • 59 slides
Uncertainty in Spoken Uncertainty in Spoken Multimodal - speakers have intentions - speech,

Uncertainty in Spoken Uncertainty in Spoken Multimodal - speakers have intentions - speech,

Baltic HLT Oct 8 2010 Natural Interaction Natural Interaction Goal oriented Uncertainty in Spoken Uncertainty in Spoken Multimodal - speakers have intentions - speech, gesture, - task Dialogue Management face, posture Dialogue

435 views • 5 slides
Expressive Gesture Model for Storytelling Humanoid Agent Le Quoc Anh, Catherine Pelachaud

Expressive Gesture Model for Storytelling Humanoid Agent Le Quoc Anh, Catherine Pelachaud

Expressive Gesture Model for Storytelling Humanoid Agent Le Quoc Anh, Catherine Pelachaud Telecom ParisTech Overview Objective: Build a model of expressive gestures GVLEX project (Gesture and Voice for expressive reading):

617 views • 20 slides
Did Homo erectus use an RRG grammar? RR RRG -2019 2019 Daniel L. Everett Bentley University

Did Homo erectus use an RRG grammar? RR RRG -2019 2019 Daniel L. Everett Bentley University

8/15/19 Did Homo erectus use an RRG grammar? RR RRG -2019 2019 Daniel L. Everett Bentley University Language is a biocultural behaviour. Thus research into its origins is necessarily an interdisciplinary exercise. Models of language

691 views • 40 slides
Highlights of Integrated Genomic Characterization of Papillary Thyroid Carcinoma Plus

Highlights of Integrated Genomic Characterization of Papillary Thyroid Carcinoma Plus

Highlights of Integrated Genomic Characterization of Papillary Thyroid Carcinoma Plus Poster #100 Tom Giordano and Gad Getz, on behalf of the THCA AWG Simple model of thyroid cancer progression 80-85% TCGA Loss of differentiation

451 views • 26 slides
Primary Mediastinal B-cell Lymphomas To Radiate or Not to Radiate Wyndham H. Wilson, MD, PhD

Primary Mediastinal B-cell Lymphomas To Radiate or Not to Radiate Wyndham H. Wilson, MD, PhD

Primary Mediastinal B-cell Lymphomas To Radiate or Not to Radiate Wyndham H. Wilson, MD, PhD Primary Mediastinal B-cell Lymphomas Molecular Subtype of DLBCL R-CHOP is Not Adequate for PMBL The British Columbia experience 153 patients

519 views • 30 slides
State of Play Moderated by: Sen Senator r Nancy Todd (C (CO) Sen Senator r Ju Juli lie Go

State of Play Moderated by: Sen Senator r Nancy Todd (C (CO) Sen Senator r Ju Juli lie Go

Wednesday, August 12, 2020 State of Play Moderated by: Sen Senator r Nancy Todd (C (CO) Sen Senator r Ju Juli lie Go Gonzales (C (CO) Presid ident Pro Tem empore Wes estern Reg egional Dir Director Immediate Pas Im ast t Ch

652 views • 15 slides
PARENTING DURING COVID- 19 A GUIDE TO MANAGING (CHILD) ANX IETY PRESENTED BY THE CENTER FOR

PARENTING DURING COVID- 19 A GUIDE TO MANAGING (CHILD) ANX IETY PRESENTED BY THE CENTER FOR

PARENTING DURING COVID- 19 A GUIDE TO MANAGING (CHILD) ANX IETY PRESENTED BY THE CENTER FOR ANXIETY AND RELATED DISORDERS AT BOSTON UNIVERSITY AGENDA ACTIONS : Creating behavioral change THOUGHTS : Developing more balanced thoughts

775 views • 52 slides

More recommend