1. Differential Principal Component Analysis (dPCA) for ChIP-seq
Hongkai Ji
Department of Biostatistics
The Bloomberg School of Public Health
Johns Hopkins University

2. Functional Genomics Locations and Functions
Maston, Evans & Green, Annu Rev Genomics Hum Genet, 2006, 7: 29-59

3. Transcription Factor (TF)
ChIP-seq
Transcription Factor (TF)
Gene
motif

4. Motivation: how to compare multiple ChIP profiles between two biological conditions?
Cell Type 1
Cell Type 2

5. Data Structure … … Intensities for locus g, marker m, replicate k :
(H3K4me3)
(H3K27me3)
Marker 2 …
Marker M
(Myc)
Rep 1
Rep K1
Cell Type 1
Marker 1
(H3K4me3)
(H3K27me3)
Marker 2 …
Marker M
(Myc)
Rep 1
Rep K2
Cell Type 2
Intensities for locus g, marker m, replicate k :
xgmk ~ G(x; μ1gm, σ2)
Intensities for locus g, marker m, replicate k :
ygmk ~ G(x; μ2gm, σ2)
Locus 1
Locus 2
Locus G …

6. Modeling True Difference
0 * 0
* 0 0
* * . 0
* 0 . *

7. Bayesian Perspective

8. Goals of Analysis 1. Estimate 2. Infer …
0 * 0
* 0 0
* * . 0
* 0 . *
Goals of Analysis
1. Estimate …
2. Infer
0 * 0
* 0 0
* * . 0
* 0 . *
(2.a) Rank loci according to each component (based on ugi);
(2.b) Test ugi = 0?

9. Example: K562 vs. Huvec ENCODE Data
G = 138,328 MYC motif sites in human genome; M = 18 data sets.

10. Biological meaning of PCs

11. PC1 predicts MYC differential binding better than using each marker individually

12. Example: K562 vs. Huvec ENCODE Data
G = 138,328 MYC motif sites in human genome; M = 25 data sets.
PC1: 50%
FDR<5%: 65252
H3K4me1
H3K4me2
H3K4me3
H3K9me1
H3K27me3
H3K36me3
H4K20me1
H3K4me3
H3K27me3
H3K36me3
DNase
FAIRE
H3K9ac
H3K27ac
CTCF
Pol2
Input
CTCF
Input
CTCF
Input
Jun
Max
Pol2
Input
PC2: 14%
FDR<5%: 47960

13. Other Examples

14. Implications TF
Cell type 1
Cell type 2 TF

15. Example: K562 vs. Huvec ENCODE Data
G = human promoters; M = 16 markers.
H3K4me1
H3K4me2
H3K4me3
H3K9me1
H3K27ac
H3K27me3
H3K36me3
H4K20me1
H3K4me3
H3K27me3
H3K36me3
CTCF
H3K9ac
Input
CTCF
Input

16. PC1 predicts RNA-seq differential expression
Cor =

17. False Discovery Rate (FDR)
0 * 0
* 0 0
* * . 0
* 0 . *

18. Simulation

19. Simulation

20. Summary
dPCA provides a way to concisely summarize differences between two cell types.
Differential genes along the major PC have biological meaning.
Future directions include modeling the signal shapes, multiple conditions, non-linearity, and establishing convergence rate.