Functionalities
SeSAMe implements stricter QC and preprocessing standards: comprehensive probe quality masking, bleed-through correction in background subtraction, nonlinear dye bias correction, stricter nondetection calling and control for bisulfite conversion based on C/T-extension probes. The package also provides convenient, performant implementations of typical analysis steps, such as the inference of gender, age, ethnicity (based on both internal SNP probes and channel-switching Type-I probes) directly from the data. This allows users to infer these common covariates if such information is not provided, and to check for potential sample swaps when it is provided. SeSAMe also provides functionality for calling differential methylation and segmented copy number.
Read IDATs into SigSet list
A simple list of “SigSet”s are returned. One can also just provide a vector of file paths prefixes (excluding _Red.idat and _Grn.idat, one prefix for a pair of IDATs) and call readIDATpair directly.
Background subtraction
Like many other Infinium Methylation-targeted software, SeSAMe implements the background subtraction based on normal-exponential deconvolution using out-of-band probes noob (Triche et al. 2013) and optionally with extra bleed-through subtraction. Signal bleed-through happens when measurement from one channel affects the measurement in the other channel. SeSAMe’s noobsb further removes residual background by regressing out the green-to-red and red-to-green relationship using Type-I probes.
Type-I channel inference
Sometimes Type-I channel spec is inaccurate in the manifest. We can infer the channel using data.
Dye bias correction
Dye bias refers to the difference in signal intensity between the two color channel. SeSAMe offers two flavors of dye bias correction: linear scaling (dyeBiasCorr) and nonlinear scaling (dyeBiasCorrTypeINorm). Linear scaling equalize the mean of all probes from the two color channel.
library(sesame)
sset.dbLinear <- dyeBiasCorr(sset)
qqplot(
slot(sset.dbLinear, 'IR'), slot(sset.dbLinear, 'IG'),
xlab='Type-I Red Signal', ylab='Type-I Grn Signal',
main='Linear Correction', cex=0.5)
abline(0,1,lty='dashed')
Residual dye bias can be corrected using nonlinear quantile interpolation with Type-I probes.
Under this correction, Type-I Red probes and Type-I Grn probes have the same distribution of signal.
qqplot(
slot(sset.dbNonlinear, 'IR'), slot(sset.dbNonlinear, 'IG'),
xlab='Type-I Red Signal', ylab='Type-I Grn Signal',
main='Nonlinear Correction', cex=0.5)
abline(0,1,lty='dashed')
Note that linear scaling does not shift beta values of Type-I probes while nonlinear scaling does shift beta values of Type-I probes.
Get betas
Beta values are defined as methylated signal/(methylated signal + unmethylated signal). It can be computed using getBetas function. The output is a named vector with probe ID as name. There are two options for getBetas that affects probe masking. The first is quality.mask=TRUE/FALSE which switches probe quality masking. The quality masking includes mapping issues, SNPs and non-uniqueness, and is described in Zhou et al 2017. nondetection.mask = TRUE/FALSE is used to switch masking of nondetection based on detection P-value. Both masks are recommended to ensure data quality and defaulted to TRUE.
## cg00000029 cg00000103 cg00000109 cg00000155 cg00000158 cg00000165
## 0.8237945 NA 0.8125637 0.9152265 0.9105163 0.8196466Beta values for Type-I probes can also be obtained by summing up the two in-band channel and out-of-band channel. This rescues probes with SNP hitting the extension base and hence switching color channel. More details can be found in Zhou et al 2017.
For such probes, extra SNP allele frequencies can be derived by summing up methylated and umethylated alleles.
Sample/experiment QC
SeSAMe implements inference of sex, age, ethnicity. These are valuable information for checking the integrity of the experiment and detecting sample swaps.
Sex
Sex is inferred based on our curated X-linked probes and Y chromosome probes excluding pseudo-autosomal regions.
## [1] "MALE"## [1] "XaY"Ethnicity
Ethnicity is inferred using a random forest model trained based on both the built-in SNPs (rs probes) and channel-switching Type-I probes.
## [1] "WHITE"Age
SeSAMe provides age regression a la the Horvath 353 model.
## [1] 84.13913Mean intensity
The mean intensity of all the probes characterize the quantity of input DNA and efficiency of probe hybridization.
## [1] 3171.483Bisulfite conversion control using GCT scores
Infinium platforms are intrinsically robust to incomplete bisulfite conversion as non-converted probes would fail to hybridize to the target. Residual incomplete bisulfite conversion can be quantified using GCT score based on C/T-extension probes. Details of this method can be found in Zhou et al. 2017. The closer the score to 1.0, the more complete the bisulfite conversion.
## [1] 1.067894Probe retrieval and \(\beta\)-value visualization
To visualize all probes from a gene
To visualize probes from arbitrary region
To visualize by probe names
CNV
SeSAMe performs copy number variation in three steps: 1) normalizes the signal intensity using a copy-number-normal data set; 2) groups adjacent probes into bins; 3) runs DNAcopy internally to group bins into segments.
To visualize segmentation in SeSAMe,
Cell Composition Deconvolution
SeSAMe estimates leukocyte fraction using a two-component model.This function works for samples whose targeted cell-of-origin is not related to white blood cells.
## [1] 0.2197238