Introduction to differential binding Peter Humburg Statistician, - - PowerPoint PPT Presentation

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introduction to differential binding

Introduction to differential binding Peter Humburg Statistician, - - PowerPoint PPT Presentation

Jun 24, 2023 477 likes •769 views

DataCamp ChIP-seq Workflows in R CHIP - SEQ WORKFLOWS IN R Introduction to differential binding Peter Humburg Statistician, Macquarie University DataCamp ChIP-seq Workflows in R DataCamp ChIP-seq Workflows in R Comparing samples Two

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DataCamp ChIP-seq Workflows in R

Introduction to differential binding

CHIP-SEQ WORKFLOWS IN R

Peter Humburg

Statistician, Macquarie University

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

Comparing samples

Two groups of samples: Primary and treatment resistant tumor. Questions we would like to answer: Are samples from the same group generally similar? Are samples from different groups different? What are those differences?

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

PCA plots for ChIP-seq data

qc_result <- ChIPQC("sample_info.csv", "hg19") counts <- dba.count(qc_results, summits=250) plotPrincomp(counts)

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DataCamp ChIP-seq Workflows in R

Hierarchical clustering

Compute distance between samples Create dendogram Plot dendrogram

distance <- dist(t(coverage)) dendro <- hclust(distance) plot(dendro)

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DataCamp ChIP-seq Workflows in R

Heatmaps

Create heatmap from coverage data for peaks

dba.plotHeatmap(peaks, maxSites = peak_count, correlations = FALSE)

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DataCamp ChIP-seq Workflows in R

Let's practice!

CHIP-SEQ WORKFLOWS IN R

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DataCamp ChIP-seq Workflows in R

Testing for differential binding

CHIP-SEQ WORKFLOWS IN R

Peter Humburg

Statistician, Macquarie University

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DataCamp ChIP-seq Workflows in R

Comparing groups of ChIP-seq samples

Use statistical analysis of read counts to identify significant differences between groups. DiffBind package provides interface to analysis tools. Use either DESeq2 or edgeR.

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DataCamp ChIP-seq Workflows in R

Creating a shared peak set

Counting reads in peak set:

peak_counts <- dba.counts(qc_output, summits=250)

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DataCamp ChIP-seq Workflows in R

Establishing a contrast

Creating a contrast: Other supported categories: DBA_ID DBA_TISSUE DBA_FACTOR DBA_TREATMENT DBA_REPLICATE DBA_CALLER

peak_counts <- dba.contrast(peak_counts, categories = DBA_CONDITION)

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DataCamp ChIP-seq Workflows in R

Using controls

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DataCamp ChIP-seq Workflows in R

Running the analysis

bind_diff <- dba.analyze(peak_counts)

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DataCamp ChIP-seq Workflows in R

A first look at the results

PCA plot for differentially bound peaks Heatmap for differentially bound peaks

dba.plotPCA(bind_diff, DBA_Condition, contrast=1) dba.plotHeatmap(bind_diff, DBA_Condition, contrast=1)

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DataCamp ChIP-seq Workflows in R

Let's practice!

CHIP-SEQ WORKFLOWS IN R

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DataCamp ChIP-seq Workflows in R

A closer look at differential binding

CHIP-SEQ WORKFLOWS IN R

Peter Humburg

Statistician, Macquarie University

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DataCamp ChIP-seq Workflows in R

Creating MA plots with DiffBind

dba.plotMA(dba_object)

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

Creating Volcano plots with DiffBind

dba.plotVolcano(dba_object)

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

Creating Box plots with DiffBind

dba.plotBox(dba_object)

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DataCamp ChIP-seq Workflows in R

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DataCamp ChIP-seq Workflows in R

Let's practice!

CHIP-SEQ WORKFLOWS IN R