Calculate_StdNorm calculates the standardized d0 or fc titers
Calculate_StdNorm(dat, type, fcToOne = FALSE, idCol = "SubjectID", cols = grep(paste0(type, "_[AB]"), colnames(dat), value = TRUE))
| dat | Data frame containing |
|---|---|
| type | What should be standarized. Either "d0", or "fc". |
| fcToOne | Logical. Are titer fold changes allowed to be less than 1
or should these be changed to 1 before standardization?
Default is FALSE and no changes will be made. Only relevant
when |
| idCol | Name of column containing subject IDs |
| cols | column names containing the titer measurements for each strain |
A data frame like dat but with standarized columns added
This must be run on only 1 cohort at a time because titers will be normalized across all subjects. The median is used but unlike the original reference, the standard deviation is calculated rather than the maximum absolute deviation.
Tsang JS, et al. (2014) Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157(2):499-513.
## First Example