R/predictAge.R
predictAge.RdpredictAge Multiplies the coefficients from one of three
epigenetic gestational age clocks, by the corresponding CpGs in a supplied
betas data.frame.
predictAge(betas, type = "RPC")An n by m dataframe of methylation values on the beta scale (0, 1), where the CpGs are arranged in rows, and samples in columns. Should contain all CpGs used in each clock
One of the following: "RPC" (Robust), "CPC", (Control) or "RRPC" (Refined Robust).
A vector of length m, containing inferred gestational age.
Predicts gestational age using one of 3 placental gestational age clocks: RPC, CPC, or refined RPC. Requires placental DNA methylation measured on the Infinium 27K/450k/EPIC methylation array. Ensure as many predictive CpGs are present in your data, otherwise accuracy may be impacted.
It's recommended that you have all predictive CpGs, otherwise accuracy may vary.
# Load placenta DNAm data
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
data(plBetas)
data(plPhenoData)
plPhenoData %>%
mutate(inferred_ga = predictAge(plBetas, type = "RPC"))
#> 558 of 558 predictors present.
#> # A tibble: 24 × 8
#> sample_id sex disease gestation_wk ga_RPC ga_CPC ga_RRPC inferred_ga
#> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GSM1944936 Male preeclampsia 36 38.5 38.7 38.7 38.5
#> 2 GSM1944939 Male preeclampsia 32 33.1 34.2 32.6 33.1
#> 3 GSM1944942 Female preeclampsia 32 34.3 35.1 33.3 34.3
#> 4 GSM1944944 Male preeclampsia 35 35.5 36.7 35.5 35.5
#> 5 GSM1944946 Female preeclampsia 38 37.6 37.6 36.6 37.6
#> 6 GSM1944948 Female preeclampsia 36 36.8 38.4 36.7 36.8
#> 7 GSM1944949 Female preeclampsia 37 38.2 38.1 37.7 38.2
#> 8 GSM1944950 Male preeclampsia 35 35.9 38.0 35.1 35.9
#> 9 GSM1944951 Female normal/heal… 39 40.2 41.0 39.6 40.2
#> 10 GSM1944952 Male normal/heal… 38 39.7 39.6 39.5 39.7
#> # ℹ 14 more rows