Technical bias in sequencing data – Bias – Example 1: DNA sequencing
Technical bias in sequencing data – Bias – Example 1: DNA sequencing
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techbias
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Technical bias in sequencing data
Michael Love
Research Fellow
Irizarry group, DFCI/HSPH
Bias
thought experiment: measure potholes in Boston vs Cambridge
everyone's rulers are off by +1 cm Boston rulers left in the sun, stretched by +1 cm...sounds simple, but still we see datasets with 100% confounding of condition with experimental batch
"Sequencing bias"
- this talk: bias in use of sequencing as a quantitative assay
- there is also bias in sequencing, e.g. calling bases A,C,G,T
Example 1: DNA sequencing
- we are sequencing DNA for genotyping
- meanwhile, use data to find copy number
Copy number
relative to a reference genome
Reads stored in "Fastq" file
Align reads to reference genome
the local number of reads: "read depth"
Local read depth
changes in read depth relative to a reference:
Copy number
Bias
deviation of coverage from that expected from proporitions of molecules in the "pool"
Assumptions
- uniform rate of coverage
- or at least, sample / reference is ok
Amplification
involves polymerase copying DNA many times over
Amplification
- polymerase has a preference for certain number of C,G
- slightly from different experiment to experiment
Observing GC bias
partition the genome into windows count the number of reads calculate the ratio (C+G)/(A+C+G+T)Differential GC bias
Correction
- divide counts by prediction from model, \(\hat{\mu}\)
- then look at ratio of corrected counts
- alternatively, put \(\hat{\mu}\) in the model
Summary 1
- GC bias was different between sample and reference
- bias didn't cancel
- modeling on features we generated in silico helped
Example 2: RNA sequencing
- we want to quantify mRNA and compare across patients
- needed for research and as a marker in diagnostics
RNA sequencing protocol
Bias in RNA sequencing
deviation of coverage from expected given the proportion of molecules in the pool
a few sources of bias
- fragmentation and size selection
- random hexamer primers (for RT)
- PCR
(other steps are certainly also important)
fragmentation, size selection
fragmentation, size selection
sequence specific bias
- random hexamer priming biases read starts
- weight each read using the observed freq's
useful plot for identifying non-uniform coverage
linear model of the Poisson rate including sequence bias
Multiple isoforms per gene
model for estimating isoform abundances including fragmentation, size selection, sequence bias
Probability of a vector of read counts \(\vec{n}\), indexed by read type j:
\[ f_\theta(\vec{n}) = \prod_j f_{Pois}(n_j, \vec{\theta} \cdot \vec{a}_j ) \]
- \(\vec{\theta}\) are the isoform abundances
- \(\vec{a}_j\) are the rates, 0 if read \(j\) cannot be generated by the isoform
- rates include bias
Multiple isoforms per gene
likelihood of isoform abundances given fragment length distribution and sequence bias: used in Cufflinks
Aggregate bias at the gene level
- model gene counts on known covariates
- factor analysis
- add batch to model formula
conditional quantile normalization
Bias in batches
Surrogate variable analysis
SVA: Leek et al 2007, svaseq: Leek 2014
Add batch to the model
Per gene, model the mean for sample j, \(\mu_j\), as:
\[ log(\mu_j) = \beta_0 + \beta_{b} 1_{j \in B} + \beta_{t} 1_{j \in T} \]
where B is the second batch, T is the treated samples.
Summary 2
- At the read level:
- Include bias in model for isoform abundances
- At the gene level:
- Model on known covariates
- Remove unknown batch differences with factor analysis
- Remove any differences correlating with batch
count \(\sim\) \(\mathcal{L}\) (bias \(\cdot\) biology)
Bias and batch
- Accurate measurements necessary for research
- Way more important if used as markers