index.qmd

---
title: "Monte Carlo Cross Validation"
---

# Evidentiary and interpretable prediction

Binary and Multi-classification algorithm for adverse outcome detection, survival classification, and endpoint prediction (see references for details)

![](MCCV_prediction_scheme.png)

# Objectives of this project

1.  Build the *mccv* python package: easily implement and perform MCCV for learning and prediction tasks.
2.  Showcase accessibly to build, validate, and interpret MCCV classifiers.
3.  Demonstrate use in both python and R for diverse community implementations.

# Installation

    mkdir ~/my_directory #choose where to clone the mccv repository
    cd ~/my_directory
    git clone https://github.com/ngiangre/mccv.git
    cd mccv/
    python3 -m pip install .

# Usage

::: panel-tabset
# Python

```{python}
import pandas as pd
data = pd.read_csv('data/data.csv',index_col=0) # Feature column name is 'biomarker' and response column  name is 'status'
data.head()
```

```{python}
import mccv
mccv_obj = mccv.mccv(num_bootstraps=200)
mccv_obj.set_X( data.loc[:,['biomarker']] )
mccv_obj.set_Y( data.loc[:,['status']] )
mccv_obj.run_mccv()
mccv_obj.run_permuted_mccv()

#Output
for n in mccv_obj.mccv_data:
    print(n)
    mccv_obj.mccv_data[n].head()
for n in mccv_obj.mccv_permuted_data:
    print(n)
    mccv_obj.mccv_permuted_data[n].head()
```

# R

```{r}
if(!requireNamespace("readr")){install.packages("readr")}
library(readr)
data <- read_csv("data/data.csv",col_types = c("iid")) #set obs as integer, status as integer, and biomarker as double
head(data)
```

```{r}
if(!requireNamespace("reticulate")){install.packages("reticulate")}
mccv = reticulate::import('mccv')
mccv_obj = mccv$mccv(num_bootstraps = as.integer(200))

X = reticulate::r_to_py(data[,c('obs','biomarker')])
X = X$set_index(reticulate::r_to_py('obs'))

y = reticulate::r_to_py(data[,c('obs','status')])
y = y$set_index(reticulate::r_to_py('obs'))

mccv_obj$set_X(X)
mccv_obj$set_Y(y)
mccv_obj$run_mccv()
mccv_obj$run_permuted_mccv()

#Output
lapply(mccv_obj$mccv_data,head)
lapply(mccv_obj$mccv_permuted_data,head)
```
:::

# Contribute

Please do! Reach out to Nick directly (nick.giangreco\@gmail.com), make an issue, or make a pull request.

# License

This software is released under the MIT license, which can be found in LICENSE in the root directory of this repository.

# Citation

Giangreco, N.P., Lebreton, G., Restaino, S. et al. Alterations in the kallikrein-kinin system predict death after heart transplant. Sci Rep 12, 14167 (2022). <https://doi.org/10.1038/s41598-022-18573-2>

Giangreco et al. 2021. Plasma kallikrein predicts primary graft dysfunction after heart transplant. Journal of Heart and Lung Transplantation, 40(10), 1199-1211. <https://doi.org/10.1016/j.healun.2021.07.001>.