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https://github.com/mcol/hsstan
An R package for biomarker discovery using Bayesian models implemented in Stan
https://github.com/mcol/hsstan
bayesian feature-selection mcmc r-package
Last synced: about 2 months ago
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An R package for biomarker discovery using Bayesian models implemented in Stan
- Host: GitHub
- URL: https://github.com/mcol/hsstan
- Owner: mcol
- License: gpl-3.0
- Created: 2019-08-13T14:26:11.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-09-13T07:54:23.000Z (4 months ago)
- Last Synced: 2024-10-13T23:46:10.230Z (3 months ago)
- Topics: bayesian, feature-selection, mcmc, r-package
- Language: R
- Homepage:
- Size: 488 KB
- Stars: 6
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
README
# Hierarchical Shrinkage Stan Models for Biomarker Selection
[](https://cran.r-project.org/package=hsstan)
[](https://cran.r-project.org/package=hsstan)
The **hsstan** package provides linear and logistic regression models penalized
with hierarchical shrinkage priors for selection of biomarkers. Models are
fitted with [Stan](https://mc-stan.org), which allows to perform full Bayesian
inference ([Carpenter et al. (2017)](https://doi.org/10.18637/jss.v076.i01)).
It implements the horseshoe and regularized horseshoe priors ([Piironen and
Vehtari (2017)](https://doi.org/10.1214/17-EJS1337SI)), and the projection
predictive selection approach to recover a sparse set of predictive biomarkers
([Piironen, Paasiniemi and Vehtari (2020)](https://doi.org/10.1214/20-EJS1711)).
The approach is particularly suited to selection from high-dimensional panels
of biomarkers, such as those that can be measured by MSMS or similar technologies.
### Example
```r
library(hsstan)
data(diabetes)
## if possible, allow using as many cores as cross-validation folds
options(mc.cores=10)
## baseline model with only clinical covariates
hs.base <- hsstan(diabetes, Y ~ age + sex)
## model with additional predictors
hs.biom <- hsstan(diabetes, Y ~ age + sex, penalized=colnames(diabetes)[3:10])
print(hs.biom)
# mean sd 2.5% 97.5% n_eff Rhat
# (Intercept) 0.00 0.03 -0.07 0.07 4483 1
# age 0.00 0.04 -0.07 0.08 4706 1
# sex -0.15 0.04 -0.22 -0.08 5148 1
# bmi 0.33 0.04 0.25 0.41 4228 1
# map 0.20 0.04 0.12 0.28 3571 1
# tc -0.45 0.25 -0.94 0.04 3713 1
# ldl 0.28 0.20 -0.12 0.68 3674 1
# hdl 0.01 0.12 -0.23 0.25 3761 1
# tch 0.07 0.08 -0.06 0.25 4358 1
# ltg 0.43 0.11 0.22 0.64 3690 1
# glu 0.02 0.03 -0.03 0.10 3034 1
## behaviour of the sampler
sampler.stats(hs.base)
# accept.stat stepsize divergences treedepth gradients warmup sample
# chain:1 0.9497 0.5723 0 3 6320 0.09 0.08
# chain:2 0.9357 0.6480 0 3 5938 0.09 0.08
# chain:3 0.9455 0.6014 0 3 6112 0.09 0.08
# chain:4 0.9488 0.5932 0 3 6238 0.09 0.08
# all 0.9449 0.6037 0 3 24608 0.36 0.32
sampler.stats(hs.biom)
# accept.stat stepsize divergences treedepth gradients warmup sample
# chain:1 0.9821 0.0191 0 8 233656 5.04 4.28
# chain:2 0.9891 0.0158 1 8 255994 5.88 4.72
# chain:3 0.9908 0.0143 0 9 274328 5.77 5.14
# chain:4 0.9933 0.0121 0 9 344984 5.98 6.70
# all 0.9888 0.0153 1 9 1108962 22.67 20.84
## approximate leave-one-out cross-validation with Pareto smoothed
## importance sampling
loo(hs.base)
# Computed from 4000 by 442 log-likelihood matrix
# Estimate SE
# elpd_loo -622.4 11.4
# p_loo 3.4 0.2
# looic 1244.9 22.7
# ------
# Monte Carlo SE of elpd_loo is 0.0.
#
# All Pareto k estimates are good (k < 0.5).
loo(hs.biom)
# Computed from 4000 by 442 log-likelihood matrix
# Estimate SE
# elpd_loo -476.5 13.7
# p_loo 9.8 0.7
# looic 953.0 27.5
# ------
# Monte Carlo SE of elpd_loo is 0.1.
#
# All Pareto k estimates are good (k < 0.5).
## run 10-folds cross-validation
set.seed(1)
folds <- caret::createFolds(diabetes$Y, k=10, list=FALSE)
cv.base <- kfold(hs.base, folds=folds)
cv.biom <- kfold(hs.biom, folds=folds)
## cross-validated performance
round(posterior_performance(cv.base), 2)
# mean sd 2.5% 97.5%
# r2 0.02 0.00 0.01 0.03
# llk -623.14 1.67 -626.61 -620.13
# attr(,"type")
# [1] "cross-validated"
round(posterior_performance(cv.biom), 2)
# mean sd 2.5% 97.5%
# r2 0.48 0.01 0.47 0.50
# llk -482.86 3.76 -490.45 -476.56
# attr(,"type")
# [1] "cross-validated"
## projection predictive selection
sel.biom <- projsel(hs.biom)
print(sel.biom, digits=4)
# var kl rel.kl.null rel.kl elpd delta.elpd
# 1 Intercept only 0.352283 0.00000 NA -627.3 -155.84260
# 2 Initial submodel 0.333156 0.05429 0.0000 -619.8 -148.39729
# 3 bmi 0.138629 0.60648 0.5839 -533.1 -61.69199
# 4 ltg 0.058441 0.83411 0.8246 -492.5 -21.09681
# 5 map 0.035970 0.89789 0.8920 -482.7 -11.25515
# 6 hdl 0.010304 0.97075 0.9691 -473.9 -2.41192
# 7 tc 0.005292 0.98498 0.9841 -472.2 -0.72490
# 8 ldl 0.002444 0.99306 0.9927 -471.8 -0.38292
# 9 tch 0.001105 0.99686 0.9967 -471.5 -0.07819
# 10 glu 0.000000 1.00000 1.0000 -471.4 0.00000
```
### References
* [M. Colombo][mcol], A. Asadi Shehni, I. Thoma et al.,
Quantitative levels of serum N-glycans in type 1 diabetes and their
association with kidney disease,
[_Glycobiology_ (2021) 31 (5): 613-623](https://doi.org/10.1093/glycob/cwaa106).
* [M. Colombo][mcol], S.J. McGurnaghan, L.A.K. Blackbourn et al.,
Comparison of serum and urinary biomarker panels with albumin creatinin
ratio in the prediction of renal function decline in type 1 diabetes,
[_Diabetologia_ (2020) 63 (4): 788-798](https://doi.org/10.1007/s00125-019-05081-8).
* [M. Colombo][mcol], E. Valo, S.J. McGurnaghan et al.,
Biomarkers associated with progression of renal disease in type 1 diabetes,
[_Diabetologia_ (2019) 62 (9): 1616-1627](https://doi.org/10.1007/s00125-019-4915-0).
[mcol]: https://github.com/mcol