1. Why weight? Variance modelling for designed RNA-seq experiments
Abstract:
Outlier samples are relatively common in RNA-seq experiments and the root cause of such variation is generally unknown. In small experiments, the analyst is left with the difficult decision of what to do: removing the offending sample may reduce variation, but at a cost of reducing power, which can limit our ability to detect biologically meaningful changes. A compromise is to use all of the available data, but to down-weight the observations from the outlier sample in the analysis. In this poster we describe a statistical approach that allows this by modelling heterogeneity at both the sample and observational level in the differential expression analysis. Using both simulations and real data, we tease apart scenarios where this strategy leads to a more powerful analysis. Our approach is implemented in the open-source limma package available from Bioconductor (
Matthew Ritchie
Walter + Eliza Hall Institute
ABiC, 11th October 2014

2. RNA-seq in genomics research
Use of high-throughput sequencing (‘next-’ or ‘second’-generation) technologies to study gene expression
Many applications: differential expression, transcript discovery, alternative splicing, allele-specific expression
Focus today is on adapting methods for differential expression analysis to work well on messy data sets
RNA-Sequencing is becoming increasingly popular for the study of differentially gene expression. The RNA-Seq technology sequences short reads and aligns them back to the genome. The number of reads which map to a particular gene, exon, or some other feature, is recorded giving data in the form of counts. Chip-seq data also comes in the form of counts, and although I’ll be talking about RNA-Seq data, the methods mentioned may also apply to Chip-Seq data.
Table of counts
Gene ID A1 A2 B1 B2
ENSG
478
619
4830
7165
ENSG 27 20 48 55
ENSG
132
200
560
408
ENSG 42 60
131 99
ENSG 21 29 52 44
ENSG
ENSG 4 9 7
ENSG 30 23
ENSG 46 63 54 53
ENSG
2256
2793
2702
2976 …
… tens of thousands more …

3. A tale of two experiments…

4. A tale of two experiments…
outlier

5. A tale of two experiments…
outlier
outlier

6. A tale of two experiments…
outlier
Remove samples 16% expt 1
30% data in expt 2
outlier?
outlier

7. Play the weighting game
High precision
Higher weight
Low precision
Lower weight
More variable observations given lower weight in differential expression analysis

8. Voom: Mean-Variance trend
Observational level weights – deal with trend in variability observed as abundance changes.
Available in the limma package from Bioconductor
Law et al. Genome Biology (2014)
Sqrt( standard deviation)
Lowess line
mean (log2 cpm)

9. Sample-specific weights
Assume: RNA-seq experiment has some replication
How well does each sample agree with the others ?
1. For each gene: Average(X)
Deviation = X – Average(X)
2. Average the Deviations for each sample
3. Penalise samples for disagreeing with others
Ritchie et al. BMC Bioinformatics (2006)

10. Modified algorithm to allow block/group structure
e.g. weights estimated separately for single low precision sample and remaining samples that are assigned higher (equal) weights

11. Simulating data with different fold-changes

12. Simulating data with outlier samples
Key:
Group 1: 1 2 3
Group 2: 4 5 6
Outlier: 6

13. Weighted analyses lead to fewer false discoveries
Take the top 200 genes from each simulation and tally up the number of
Key:
1. Voom weights
2. No weights
3. Samples weights
4. Block weights
5. Remove outlier

14. Weights improve power to detect differential expression
Key:
1. Voom weights
2. No weights
3. Samples weights
4. Block weights
5. Remove outlier

15. Better rankings for known genes regulated by Smchd1
Protocadherins
Voom
Sample
Weights
Block
Remove
Outlier
Experiment 1
Experiment 2
0.0581
0.0235
0.0707
Structural Maintenance of Chromosomes Hinge Domain containing 1 (Smchd1)
Has a role in X inactivation and in regulating monoallelic gene expression.
Genomic imprinting and regulation of the clustered protocadherins
Gene set testing using ROAST
Wu et al. Bioinformatics (2010)
Mould et al. Epigenetics & Chromatin (2013)
Gendrel et al. MCB (2013)

16. Better rankings for known genes regulated by Smchd1
Imprinted genes
Voom
Sample
Weights
Block
Remove
Outlier
Experiment 1
0.0231
Experiment 2
0.0826
0.0232
0.0355
Gene set testing using ROAST
Wu et al. Bioinformatics (2010)
Mould et al. Epigenetics & Chromatin (2013)
Gendrel et al. MCB (2013)

17. Summary: why weight?
Simulations show that combining voom with sample-level weights gives better results in terms of lowest numbers of false positives and improved power
Better results also obtained on real RNA-seq data
We allow flexibility in how the sample weights are assigned – sample-specific by default. Modelling of block/group-specific structure also possible voomWithQualityWeights()
The limma package can incorporate weights at each stage of the analysis (differential expression and gene set testing) to deliver a more powerful analysis

18. Other Applications
Human tumor samples
Single cell RNA-seq

19. Acknowledgments Aliaksei Holik Marie-Liesse Asselin-Labat
Stephen Wilcox
Shalin Naik
Jessica Tran
Ben Kile
Catherine Carmichael
QIMR-Berghofer
Graham Kay
Funding
Cynthia Liu Shian Su Andy Chen Gordon Smyth Toby Sargeant Natasha Jansz Kelan Chen Darcy Moore Jamie Gearing Huei San Leong Marnie Blewitt

21. A data hack for medical research problems.
The weekend brings together software developers, user experience designers, data analysts and visualisers working directly with researchers to create better analysis tools.
#healthhack