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Structured data

Date Published
Programming Language

Differential Binding Analysis of ChIP-Seq peak data

Guide summary #

DiffBind: Differential binding analysis of ChIP-Seq peak data

Introduction #

Identifying statistically significantly differentially bound sites based on evidence of binding affinity between two groups

Processing overview #

DiffBind involves five phases

  1. Reading in peaksets: MACS와 같은 peak caller 프로그램의 결과 (eg. .csv, .xlsx)
  2. Occupancy analysis: merge replicates or other peak caller result
  3. Counting reads: using BAM files, make binding affinity matrix for each sample. With this matrix, samples can be re-clustered.
  4. Differential binding affinity analysis: identify statiscally significiantly differentially bound sites between sample groups. using edgeR make p-value and FDR
  5. Plotting and reporting: MA plot, correlation heatmap, PCA plots, Box plots

Example: obtaining differentially bound sites #

Dataset: ChIPs against the transcription factor ERa using 5 breast cancer cell lines (MCF-7 3 responsive to Tamoxifen, 2 resistant). There are at least 2 replicates. (Differential oestrogen receptor binding is associated with clinical outcome in breast cancer (Ross-Innes et al.) Nature, )

Peak caller is MAC

Reading in the peaksets #

Counting reads #

Establishing a contrast #

Performaing the differential analysis #

Retrieving the differentially bound sites #

Example: plotting #

Venn diagrams #

MA plots #

PCA plots #

Boxplots #

Heatmaps #

Example: differential binding analysis using a blocking factor #

Example: occupancy analysis and overlaps #

Overlap rates #

Deriving consensus peaksets #

A complete occupancy analysis: identifying sites unique to a sample group #

Comparision of occupancy and affinity based analyses #

Technical notes #

Loading peaksets #

Merging peaks #

edgeR analysis #

DESeq analysis #

DESeq2 analysis #

Vignette Data #

Using DiffBind and ChIPQC together #