1. ARIES Methylation Pre-processing and Clean up
Geoff Woodward

2. Overview Initial QC Normalisation Batch Correction Data
MWAS (Methylome Wide Assoc. Study)
Results

3. Initial QC Probe p-value confidence in detection overall QC indicator
background
-ve controls
overall QC indicator
High background
Low signal
Poor stringency

4. Initial QC: Control Probes
Mixture of dependent/independent
Sample independent
Staining (Biotin/DNP)
Hybridisation (synthetic target)
Extension (hairpin)
Sample dependent
Bisulfite conversion (HindIII site)
G/T mismatch (non-spec.)
Specificity & Non-polymorphic
Negative

5. Initial QC: LIMS

6. LIMS Control DashBoard
Real time
Jscript/JSON
Zoom & scroll
All Illumina controls probes
+ve & -ve
Area
Max
Median
Min

7. Intial QC: MDS Start pre-processing What’s affecting the data?
Failures
controls

8. Initial QC: MDS
Remove Controls/Failures
Remove Sex Chromosomes

9. Sample Confirmation Genotyping 65 SNP probes Kmeans clustering
Call genotype
Cross reference with SNP data
Calculate % match
Fully automated in pipeline
Stored in LIMS

10. Normalisation Why? Quantile? Not appropriate:
Cancer vs. Control – not req.
More sensitive differences...
Quantile?
Rank & scale according to ref dist. (av.)
Not appropriate:
Type I & II assays differ
Medians – opposite ends of β scale
SD (across reps.) smaller in Type I probes
Interrogate different subsets of the genome
Type II > proportion in open-sea
Type I > proportion in gene promoters

11. Normalisation: Method 1
Subset Within Array Normalisation (minfi)
To address differences in dist:
No. of CpGs in probe body indicates density/loc.
Dist. more similar in these groups
Approach
Reference quantiles:
N random type I & II selected for each group
Split meth/unmeth channels
Linear interpolation fit probes to ref.
Doesn’t treat type I & II separately
BUT does decrease difference

12. Normalisation: Method 2
Touleimat & Tost
To address differences:
CpG region
Shore / Shelf / Island / Open-sea
Treat Type I & II separately
Approach:
reference quantiles
Type I used “anchors” for each region
More reliable / lower SD
estimate target quantiles
Fit type II to target

13. Normalisation: Method 3
Dasen (wateRmelon)
Under review
Separate QN of
methylated Type I
unmethylated Type I
methylated Type II
unmethylated Type II intensities.
Both directions

14. Normalisation: Comparison
wateRmelon metrics:
Imprinted DMRs
237 probes within iDMRs
iDMR e=50% meth.
SE = SD / √ N
SD of all 237 probes
N = number of samples
iDMRs
Raw
Dasen
Tost
Swan

15. Normalisation: Comparison
SNP probes
63 highly polym. SNP probes
K-means clustering into 3 genotypes
SE like measure for each group AA AB BB
Raw
9.025 e-05
1.910 e-04
5.145 e-05
Dasen
1.669 e-04
2.047 e-04
2.321 e-05
Tost
8.253 e-05
5.242 e-04
1.541 e-04
Swan Na na

16. Normalisation: Comparison
wateRmelon metrics:
X-Chromosome Inactivation
11,232 probes
T-test all probes for sex differences
ROC analysis
using p-val for sex diff.
1 – AUC
0 being the perfect predictor & best sex separation
X-Inact.
Raw
0.0947
Dasen
0.0889
Tost
0.0892
Swan
0.4952

17. Comparison: Density Plots
Metrics are great but how do they really effect the data?
All
typeI
typeII

18. Comparison: Density Plots
Normalised distributions
All typeI typeII

19. Comparison: Scatter Plot
Pepsi Plot – you’ll see why!
Raw (x) vs. Normalised (y)
typeI typeII
SWAN
Tost
dasen

20. Comparison: Scatter Plot

21. Batch Correction: Exp. Design
Bisulphite Conversion
Excess of samples > 48
Redundant controls
QC and PCR
MSA4 Plate
Well dictates chip position (Robot)
Randomised
Min. 4 of each time point
Max 1 control
Mix of gender
Infinium 450k Chips
12 arrays per chip
Throughput doubled

22. Batch Correction: Metadata
LIMS tracking
Every process
All consumables
~20
Formamide to hyb. Buffers
> 1000 used so far!
All equipment
Fridge/centrifuge/PCR block

23. Batch Correction ComBat What are we seeing? Correction
Bisulphite batch
Correction
Many algorithms available
SVD/SVA/DWD
Gene expression
ComBat
Chen C, Grennan K, Badner J, Zhang D, Gershon E, et al. (2011)
Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six
Batch Adjustment Methods. PLoS ONE 6(2): e doi: /journal.pone
Empirical Bayesian framework
Create a model matrix
Supply batch var
Standardise gene-wise
Least squares approach
Fits L/S model – find priors
Adjust to empirical parametric priors

24. Batch Correction Example data Batch correct Tost norm. data
use M values
Convert back to β
Values can escape 0-1 limit
Scale
0.02% of probes
Dist. unaffected.

25. Batch Correction: BEFORE

26. Batch Correction: AFTER

27. Datasets ARIES pre-release: Filtered probes SNP probes Age group n
Cord
584 F7
598
TF3 (15) 64
F17
280
Antenatal
394
FOM
329

28. MWAS
Choice of servers:
Epi-garrod
BlueCrystal

29. Epi-garrod Request account via IT-services for:
epi-garrod.bris.ac.uk
Relatively quiet server in the dept.
No queuing system
Check htop before running jobs
Cord data requires ~15% RAM

30. Epi-garrod Data: Permissions for this folder SAN
Accessible from multiple servers
/mnt/sscm3/ARIES_DATA/…
Permissions for this folder
You must be a member of the aries group

31. Blue Crystal Request an account via: Queuing handled Data:
Queuing handled
Data:
/gpfs/cluster/smed/alspac-shared/aries/…
Again, permissions required:
Member of aries group

32. Files ALN_dasen_<<time_code>>_betas.Rdata
ALN_tost_<<time_code>>_betas.Rdata
<<time_code>>_manifest.Rdata
fdata.Rdata
MWAS.r

33. ALN_dasen_<<time_code>>_betas.Rdata

34. <<time_code>>_manifest.Rdata

35. Fdata_new.RData

36. CpGassoc
CRAN
Tests for association between an independent variable and methylation
Option to include additional covariates
Assesses significance with:
Holm (step-down Bonferroni)
FDR methods

37. MWAS.r

38. MWAS.r continued...

39. MWAS.r continued...

40. Manhattan / QQ Replicated the following studies results: Gene hits:
450K Epigenome-Wide Scan Identifies Differential DNA Methylation  in Newborns Related to Maternal Smoking during Pregnancy. Bonnie R. Joubert, et.al., 
Gene hits:
GFI1, AHRR, MYO1G, CYP1A1
"CYP1A1 plays a key role in the aryl hydrocarbon receptor signaling pathway, which mediates the detoxification of the components of tobacco smoke." - Joubert, et.al.,

41. Results file

42. BlueCrystal .bashrc

43. Any Questions?