Qindex (version 0.1.6)

Qindex-package: Continuous and Dichotomized Index Predictors Based on Distribution Quantiles

Description

Continuous and dichotomized index predictors based on distribution quantiles.

Author

Maintainer: Tingting Zhan (ORCID) [copyright holder]

Authors:

• Misung Yi (ORCID) [copyright holder]

• Inna Chervoneva (ORCID) [copyright holder]

References

Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data. Misung Yi, Tingting Zhan, Amy P. Peck, Jeffrey A. Hooke, Albert J. Kovatich, Craig D. Shriver, Hai Hu, Yunguang Sun, Hallgeir Rui and Inna Chervoneva. BMC Bioinformatics, 2023. tools:::Rd_expr_doi("10.1186/s12859-023-05408-8")

Quantile index biomarkers based on single-cell expression data. Misung Yi, Tingting Zhan, Amy P. Peck, Jeffrey A. Hooke, Albert J. Kovatich, Craig D. Shriver, Hai Hu, Yunguang Sun, Hallgeir Rui and Inna Chervoneva. Laboratory Investigation, 2023. tools:::Rd_expr_doi("10.1016/j.labinv.2023.100158")

Examples

### Data Preparation

library(survival)
data(Ki67, package = 'Qindex.data')
Ki67c = within(Ki67[complete.cases(Ki67), , drop = FALSE], expr = {
  marker = log1p(Marker); Marker = NULL
  PFS = Surv(RECFREESURV_MO, RECURRENCE)
})
(npt = length(unique(Ki67c$PATIENT_ID))) # 592

### Step 1: Cluster-Specific Sample Quantiles

Ki67q = clusterQp(marker ~ . - tissueID - inner_x - inner_y | PATIENT_ID, data = Ki67c)
stopifnot(is.matrix(Ki67q$marker))
head(Ki67q$marker, n = c(4L, 6L))

set.seed(234); id = sort.int(sample.int(n = npt, size = 480L))
Ki67q_0 = Ki67q[id, , drop = FALSE] # training set
Ki67q_1 = Ki67q[-id, , drop = FALSE] # test set

### Step 2 (after Step 1)

## Step 2a: Linear Sign-Adjusted Quantile Indices
(fr = Qindex(PFS ~ marker, data = Ki67q_0))
stopifnot(all.equal.numeric(c(fr), predict(fr)))
integrandSurface(fr)
fr_1 = predict(fr, newdata = Ki67q_1)
integrandSurface(fr, newdata = Ki67q_1)

## Step 2b: Non-Linear Sign-Adjusted Quantile Indices
(nlfr = Qindex(PFS ~ marker, data = Ki67q_0, nonlinear = TRUE))
stopifnot(all.equal.numeric(c(nlfr), predict(nlfr)))
integrandSurface(nlfr)
nlfr_1 = predict(nlfr, newdata = Ki67q_1)
integrandSurface(nlfr, newdata = Ki67q_1)

## view linear and non-linear sign-adjusted quantile indices together
integrandSurface(fr, nlfr)

# \donttest{
### Step 2c: Optimal Dichotomizing
set.seed(14837); (m1 = optimSplit_dichotom(
  PFS ~ marker, data = Ki67q_0, nsplit = 20L, top = 2L)) 
predict(m1)
predict(m1, boolean = FALSE)
predict(m1, newdata = Ki67q_1)
# }

### Step 3 (after Step 1 & 2)
# \donttest{
Ki67q_0a = within.data.frame(Ki67q_0, expr = {
  FR = std_IQR(fr) 
  nlFR = std_IQR(nlfr)
  optS = std_IQR(marker[,'0.27'])
})
Ki67q_1a = within.data.frame(Ki67q_1, expr = {
  FR = std_IQR(fr_1)
  nlFR = std_IQR(nlfr_1)
  optS = std_IQR(marker[,'0.27']) 
})
# `optS`: use the best quantile but discard the cutoff identified by [optimSplit_dichotom]
# all models below can also be used on training data `Ki67q_0a`
# naive use
summary(coxph(PFS ~ NodeSt + Tstage + FR, data = Ki67q_1a))
summary(coxph(PFS ~ NodeSt + Tstage + nlFR, data = Ki67q_1a))
summary(coxph(PFS ~ NodeSt + Tstage + optS, data = Ki67q_1a))
# set.seed if necessary
summary(BBC_dichotom(PFS ~ NodeSt + Tstage ~ FR, data = Ki67q_1a))
# `NodeSt`, `Tstage`: predctors to be used as-is
# `FR` to be dichotomized
# set.seed if necessary
summary(BBC_dichotom(PFS ~ NodeSt + Tstage ~ nlFR, data = Ki67q_1a))
# set.seed if necessary
summary(BBC_dichotom(PFS ~ NodeSt + Tstage ~ optS, data = Ki67q_1a))
# }