Data Inference

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.

sset = sesameDataGet('EPIC.1.LNCaP')$sset
inferSex(sset)

## [1] "MALE"

inferSexKaryotypes(sset)

## [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.

inferEthnicity(sset)

## [1] "WHITE"

Age

SeSAMe provides age regression a la the Horvath 353 model.

betas <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
predictAgeHorvath353(betas)

## [1] 84.13913

Mean intensity

The mean intensity of all the probes characterize the quantity of input DNA and efficiency of probe hybridization.

meanIntensity(sset)

## [1] 3171.483

Copy Number

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.

ssets.normal <- sesameDataGet('EPIC.5.normal')
segs <- cnSegmentation(sset, ssets.normal)

To visualize segmentation in SeSAMe,

visualizeSegments(segs)

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.

betas.tissue <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
estimateLeukocyte(betas.tissue)

## [1] 0.2007592