# Case study: Analyzing TCGA HCC dataset

In this example, we will use the RNA-Seq, miRNA, and DNA Methylation datsets from the TCGA HCC cancer dataset to perform subtype detection, identify subtype specific features, and fit supervised model that we will use to project the HCC samples using only the RNA-Seq OMIC layer. This real case dataset is available directly inside the `data` folder from the package.

## Dataset preparation

First, locate the data folder and the compressed matrices:

```bash

data

├── meth.tsv.gz

├── mir.tsv.gz

├── rna.tsv.gz

└── survival.tsv

```

Go to that folder (**cd ./data/**) and decompress these files using `gzip -d *.gz`.

Now, go back to the main folder (**cd ../**), and we are ready to instanciate a DeepProg instance.

```python

from simdeep.simdeep_boosting import SimDeepBoosting

from simdeep.config import PATH_THIS_FILE

from collections import OrderedDict

from os.path import isfile

# specify your data path

path_data = ‘./data/’

assert(isfile(path_data + "/meth.tsv"))

assert(isfile(path_data + "/rna.tsv"))

assert(isfile(path_data + "/mir.tsv"))

tsv_files = OrderedDict([

    ('MIR', 'mir.tsv'),

    ('METH', 'meth.tsv'),

    ('RNA', 'rna.tsv'),

])

# The survival file located also in the same folder

survival_tsv = 'survival.tsv'

assert(isfile(path_data + "survival.tsv"))

# More attributes

PROJECT_NAME = 'HCC_dataset' # Name

EPOCHS = 10 # autoencoder fitting epoch

SEED = 10045 # random seed

nb_it = 10 # Number of submodels to be fitted

nb_threads = 2 # Number of python threads used to fit survival model

```

We need also to specify the columns to use from the survival file:

```bash

head data/survival.tsv

Samples days    event

TCGA.2V.A95S.01 0       0

TCGA.2Y.A9GS.01 724     1

TCGA.2Y.A9GT.01 1624    1

TCGA.2Y.A9GU.01 1939    0

TCGA.2Y.A9GV.01 2532    1

TCGA.2Y.A9GW.01 1271    1

TCGA.2Y.A9GX.01 2442    0

TCGA.2Y.A9GY.01 757     1

TCGA.2Y.A9GZ.01 848     1

```

```python

survival_flag = {

    'patient_id': 'Samples',

    'survival': 'days',

    'event': 'event'}

```

Now we define a ray instance to distribute the fitting of the submodels

```python

import ray

ray.init(webui_host='0.0.0.0', num_cpus=3)

```

## Model fitting

We are now ready to instanciate a DeepProg instance and to fit a model

```python

# Instanciate a DeepProg instance

boosting = SimDeepBoosting(

    nb_threads=nb_threads,

    nb_it=nb_it,

    split_n_fold=3,

    survival_tsv=survival_tsv,

    training_tsv=tsv_files,

    path_data=path_data,

    project_name=PROJECT_NAME,

    path_results=path_data,

    epochs=EPOCHS,

    survival_flag=survival_flag,

    distribute=True,

    seed=SEED)

boosting.fit()

# predict labels of the training

boosting.predict_labels_on_full_dataset()

boosting.compute_clusters_consistency_for_full_labels()

boosting.evalutate_cluster_performance()

boosting.collect_cindex_for_test_fold()

boosting.collect_cindex_for_full_dataset()

boosting.compute_feature_scores_per_cluster()

boosting.write_feature_score_per_cluster()

```

## Visualisation and analysis

We should obtain subtypes with very significant survival differences, as we can see in the results located in the results folder

![HCC KM plot](./img/HCC_dataset_KM_plot_boosting_full.png)

Now we might want to project the training samples using only the RNA-Seq layer

```python

boosting.load_new_test_dataset(

    {'RNA': 'rna.tsv'},

    'test_RNA_only',

    survival_tsv,

)

boosting.predict_labels_on_test_dataset()

boosting.compute_c_indexes_for_test_dataset()

boosting.compute_clusters_consistency_for_test_labels()

```

We can use the visualisation functions to project our samples into a 2D space

```python

# Experimental method to plot the test dataset amongst the class kernel densities

boosting.plot_supervised_kernel_for_test_sets()

boosting.plot_supervised_predicted_labels_for_test_sets()

```

Results for unsupervised projection

![Unsupervised KDE plot](./img/HCC_dataset_KM_plot_boosting_full_kde_unsupervised.png)

Results for supervised projection

![Supervised KDE plot](./img/HCC_dataset_KM_plot_boosting_full_kde_supervised.png)