normalize2 {CODEX} | R Documentation |
Fits a Poisson log-linear model that normalizes the read depth data from whole exome sequencing. Includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. If the WES is designed under case-control setting, CODEX estimates the exon-wise Poisson latent factor using only the read depths in the control cohort, and then computes the sample-wise latent factor terms for the case samples by regression.
normalize2(Y_qc, gc_qc, K, normal_index)
Y_qc |
Read depth matrix after quality control procedure returned from
|
gc_qc |
Vector of GC content for each exon after quality control procedure returned
from |
K |
Number of latent Poisson factors. Can be an integer if optimal solution has been chosen or a vector of integers so that AIC, BIC, and RSS are computed for choice of optimal k. |
normal_index |
Indices of control samples. |
Yhat |
Normalized read depth matrix |
AIC |
AIC for model selection |
BIC |
BIC for model selection |
RSS |
RSS for model selection |
K |
Number of latent Poisson factors |
Yuchao Jiang yuchaoj@wharton.upenn.edu
Y_qc <- qcObjDemo$Y_qc gc_qc <- qcObjDemo$gc_qc normObj <- normalize2(Y_qc, gc_qc, K = 1:5, normal_index = seq(1, 45, 2)) Yhat <- normObj$Yhat AIC <- normObj$AIC BIC <- normObj$BIC RSS <- normObj$RSS K <- normObj$K