Selecting Between-sample ChIP-Seq Normalization Methods - Assumptions and Implications

Explore the intricacies of selecting between-sample ChIP-Seq normalization methods in this 44-minute conference talk by Jo Hardin at the Computational Genomics Summer Institute (CGSI) 2023. Delve into the assumptions underlying various normalization techniques for ChIP-Seq data analysis, drawing parallels with RNA-Seq normalization methods. Examine the importance of normalization in high-throughput sequencing, focusing on differential binding analysis in ChIP-Seq experiments. Learn about library size adjustments, background bin considerations, and the concept of "capital T truth" in data analysis. Explore various normalization approaches, including size factors, relative log expression, and technical condition adjustments. Understand the impact of symmetry and asymmetry in DNA binding on normalization choices. Evaluate normalization methods through simulations and false discovery rate assessments. Gain insights into the use of controls, biological experiments, and principal component analysis in ChIP-Seq data interpretation. Conclude with a comprehensive understanding of how to select appropriate normalization methods based on experimental design and data characteristics.

Intro
High throughput sequencing
What is ChIP sequencing
Differential binding
RNAseq
How much does normalization matter
Library size
Background bins
Road map
Capital T truth
Factoring
spiking controls
symmetry
Asymmetry
Size Factors
Relative Log Expression
Technical Conditions
Simulations
False Discovery Rate
Symmetrical DNA Binding
Background Bin
Entire Genome
Four Images
Technical Condition
Trend
Data
Principal Components
Summary
Controls
Biological Experiment
Conclusion