VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization,

Processing and Classification of Clinical and Genomic TCGA Data

Alba Garin-Muga

1,2 a

, Aurora María Sucre

1,2 b

, Jordi Torres

1 c

and Jon Kerexeta

1 d

Vicomtech, eHealth and Biomedical Applications Area, Donostia-San Sebastian 20014, Spain

Biodonostia, Bioengineering Area, eHealth Group, Donostia-San Sebastián 20014, Spain

Keywords: TCGA, Stratification, ML, Visualization, Clinical Data, Genomics

Abstract: The Cancer Genome Atlas (TCGA) is a collection of freely available data of several human cancer types.

TCGA contains over 2.5 petabytes of data, which includes, among others, clinical and genomic data. However,

the visualization of such data is cumbersome and tiring for non-expert users. VisualMLTCGA is an intuitive

and easy-to-use web tool that allows the automatic download and visualization of TCGA data and the

processing of genomic data using GATK. Additionally, the tool allows to create comprehensive decision trees

(DT) for prediction of outcomes from clinical and genomic TCGA data and other external datasets.

VisualMLTCGA offers a simple web tool to download, process and visualize TCGA data, suitable for

researchers and clinicians without any bioinformatics background.

1 INTRODUCTION

The Cancer Genome Atlas (TCGA) is a collaborative

project (http://cancergenome.nih.gov) that has

molecularly characterized over 20,000 primary

cancer and matched normal samples among 33 cancer

types. It is a joint initiative between the National

Cancer Institute and the National Human Genome

Research Institute born in 2006 that joined together

researchers from several fields of study from all over

the world.

The TCGA contains over 2.5 petabytes of

genomic, epigenomic, transcriptomic and proteomic

data. All this information can be accessed for anyone

to use, although some of the raw files require to apply

for consent. There are 33 types of cancer to study

chosen based on their poor prognosis, public health

impact and availability of samples meeting certain

standards (patient consent, quality and quantity,

among other criteria). Due to all the reasons, TCGA

is an excellent source of data for exploring clinical or

genomic information and characterizing relevant

genes or variations on disease.

https://orcid.org/0000-0002-7160-1191

https://orcid.org/0000-0002-4078-9275

https://orcid.org/0000-0003-4818-7620

https://orcid.org/0000-0002-6516-8619

Machine learning (ML) provides methods,

techniques and tools to solve diagnostic and

prognostic problems in healthcare. ML is widely

implemented to learn from input data and extract

relevant findings from health information. The

knowledge obtained from the ML algorithms can be

then represented in a decision tree. Decision trees are

tools for graphical decision analysis, that help

identify the conditional statements visually. In this

flowchart-like structure, each internal node represents

a condition (a test on a variable), each leaf node

represents the outcome and the branches from root to

leaf represent classification rules.

The information within reach in the TCGA can be

downloaded manually from the Genomic Data

Commons Data Portal (‘GDC’, n.d.) and analysed

using advanced data analysis tools such as R (R Core

Team, n.d.) or Python (Python Software Foundation,

n.d.). However, in order to perform ML on all the

data, they require programming skills and it can be

challenging for non-expert users.

Here, we present VisualMLTCGA, an easy-to-use

web tool for downloading, pre-processing,

visualization, processing and analysis of TCGA.

Garin-Muga, A., Sucre, A., Torres, J. and Kerexeta, J.

VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classiﬁcation of Clinical and Genomic TCGA Data.

DOI: 10.5220/0008951804130420

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 413-420

ISBN: 978-989-758-398-8; ISSN: 2184-4305

413

Additionally, external data can also be uploaded and

analysed. Users can pre-process clinical and genomic

data, call variants from genomic raw data using

GATK pipelines and extract the relevant features

using decision trees created from clinical and

genomic datasets for classification purposes. This

tool is suitable for researchers and clinicians without

any bioinformatics background.

2 RELATED WORK

Due to large amount of data ready for use in the

TCGA, there are several available tools that have

been developed to support data access and

visualization. Many of them are based on R, one of

the most popular programming languages among

bioinformaticians. TCGAbiolinks (Colaprico et al.,

2016), TCGA Assembler (Zhu, Qiu, & Ji, 2014) and

RTCGA Toolbox (Samur, 2014) are three of them,

being TCGAbiolinks the most versatile. However,

they do not include a graphical interface, which may

hamper their usability for non-experts. For this

reason, many webs that allow to explore TCGA data

have proliferated. In Zhang et al. (Zhang et al., 2018),

web-based tools for TCGA variant analysis are

surveyed. It includes a detailed list of main resources

divided into three main categories: global analysis,

target analysis and auxiliary analysis. However, many

of them analyse only genomic information. Web-

TCGA (Deng, Brägelmann, Schultze, & Perner,

2016) allows the molecular profiling of available

tumours performed in a web environment. However,

it does not allow to perform machine learning

analysis to data.

To our knowledge, there is no available tool to

download, pre-process, analyse, create decision trees

and evaluate patients based on TCGA clinical and

genomic data. Additionally, there are not any

available tools to create decision trees from clinical

and genomic data and classify patients based on this

models, desired features when formulating the TCGA

analysis solution presented in this paper. Therefore,

there is an acceptable niche to develop this solution

in the field of cancer research tools.

3 VisualMLTCGA

For the implementation of VisualMLTCGA, the

Angular IO (‘Angular’, n.d.) web application

framework was chosen due to its robust components

that allow developers write readable, maintainable

and easy-to-use code. Regarding the user interface,

PrimeNG (‘PrimeNG’, n.d.) and ngx-admin

(Akveo/ngx-admin, 2016/2019) have been used.

PrimeNG is a set of rich UI components for Angular

and Ngx-admin is a frontend application template that

includes Bootstrap and TypeScript, among others.

For the backend, Python and R were used due to their

advantages in data processing and TCGAbiolinks (the

previously mentioned R package) was used to

automatically download and access TCGA data.

VisualMLTCGA solution has five main features:

(1) load TCGA data, (2) load clinical data, (3) load

genomic data, (4) build ML model and (5) classify

patient.

In the following subsections, each feature is

explained in detail.

Load Tcga Data

Using this functionality, users can explore the TCGA

projects along with the available data categories and

the file and case count. Once they choose one project,

they can download the clinical or genomic (simple

nucleotide variation) data (Figure 1). The download

and visualization of other types of data will be

developed in the immediate future.

When clinical data is downloaded, the raw data is

saved in the server. However, in order to create a

reliable dataset for machine learning and the

subsequent visualization, the data is cleaned. The

clinical data usually contains a high number of

variables but in many cases, they are not complete.

Therefore, they usually require prior pre-processing

in order to prepare the data for analysis. The filtering

of clinical data is done transparently to the user. The

cleaning processing discards the following

information:

▪ Variables that have more than 10% of null or

erroneous values,

▪ Patients that contain less than 50% of the

variables.

In addition, all the clinical data that exist for the

same patient is combined: demographic, diagnosis,

treatment, drug, radiation, etc. The filtering of clinical

data is done transparently to the user. Additionally,

for better visualization, only few features are

displayed (Figure 2). Users can now select to save the

pre-process data using the floppy disk icon or to

create a decision tree using the brain icon.

TCGA includes raw and processed genomic data

files, however raw sequencing files are not available

for public download. Mutation Annotation Format

(MAF) files are the only open access files containing

single nucleotide variant data. Therefore, in

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Figure 1: The TCGA Data Loader. All the data categories

available for each project are displayed.

Figure 2: TCGA BRCA Clinical Data Visualization.

Figure 3: TCGA BRCA Genomic Data Visualization.

VisualMLTCGA MAF files can be downloaded.

MAF is a tab-delimited text file with aggregated

mutation information extracted from variant call

format (VCF) files.

Once the user has selected to download the MAF

files from the TCGA project of interest, the tool starts

downloading and pre-processing the files. MAF files

generated following the four existing pipelines are

downloaded: varscan2 (Koboldt et al., 2012), muse

(Fan et al., 2016), somaticsniper (Larson et al., 2012),

mutect2 (Cibulskis et al., 2013). All the information

is combined and cleaned, and the clinical information

of the patients is included. The cleaning process is the

same as the one done to the clinical data. During pre-

processing, the data is prepared for the machine

learning process. To do so, in the case of genomic

data, instead of saving all the mutations associated for

a patient, we only select the 20 most frequent

mutations for the project to be displayed, as shown in

Figure 3. As mentioned before, the user can now select

to save the processed MAF files along with the

clinical information or to use the processed dataset to

create a decision tree.

Load External Clinical Data

Along with the TCGA data, we can load external data

into our tool. Therefore, users can use

VisualMLTCGA to pre-process and visualize any

clinical dataset stored in tabular text files. This

functionality may be of interest for non-expert users

to automatically clean and inspect data easily before

further processing. By way of example, we have

downloaded a public dataset from Kaggle and upload

it to VisualMLTCGA using the uploading icon.

Uploaded datasets can be removed from the server

anytime using the garbage-can icon.

The Kaggle dataset is a liver cancer (HCC,

hepatocellular carcinoma) dataset uploaded by the

University Hospital of Coimbra (Portugal)

. It

contains several demographic data, risk factors,

laboratory and overall survival features from 165 real

patients diagnosed with HCC. The dataset contains 49

features selected according to the EASL-EORTC

Clinical Practice Guidelines(‘EASL-EORTC Clinical

Practice Guidelines’, n.d.), which are the current

state-of-the-art on the management of HCC. Figure 4

shows the visualization of the dataset. At this point,

the user can create a decision tree using the brain icon

and use the generated model to classify new patients.

Load External Genomic Data

In addition to clinical data, users can load external

genomic data to VisualMLTCGA. They can either

load previously uploaded files or processed MAF

files downloaded from the TCGA, as well as new

files. VisualMLTCGA filters all the genomic files

available in the server to show them in the dropdown

menu. The tool supports raw file formats such as FQ

and processed file formats such as VCF or MAF.

Raw files are processed using the Genome

Analysis Toolkit (GATK) following the Best

Practices for Variant Discovery (‘GATK | BP Doc

#24216 | Pipeline Index’, n.d.). The GATK is a well-

known toolkit developed by the Broad Institute and

VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classiﬁcation of Clinical and Genomic TCGA Data

415

its Best Practices provide step-by-step

recommendations for performing variant discovery

analysis (‘GATK | BP Doc #11145 | Germline short

variant discovery (SNPs + Indels)’, n.d.). This

pipeline, after all the processing, returns a VCF file as

output.

Whether the user loads a raw or variant file

(VCF), the tool visualizes the variants in a table

format. An example table is shown in Figure 5. As

explained in the previous subsections, users can

create decision tree from the variant data using the

brain icon.

Figure 4: External clinical data loader.

Figure 5: External genomic data loader.

Build Ml Model

The previously explained features are used to

download or load data to the platform. However, in

order to exploit these data to obtain relevant

information, it can be analysed using machine

learning. For this purpose, we selected decision trees,

a supervised machine earning technique that can be

used for classification. They allow to predict the value

of a target variable based on the input data. The

prediction values are represented in a tree where each

leaf shows the probability of the target variable value

and the number of instances that support it.

For the creation of decision trees, we use the

“Build ML Model” option of the main menu or the

brain icon that is enabled after loading a dataset. In

the case of accessing from the main menu, there is a

dropdown menu to choose from all the datasets

available. The user can select from all the

downloaded datasets from the TCGA or the external

datasets loaded to the VisualMLTCGA. In order to

create the model, users need to select the relevant

variables for the classification and the outcome

variable to predict.

Five classification algorithms were implemented:

1. Generalized Linear Model Trees(Nummi, 2015):

It does a recursive partitioning based on the well-

known Generalized Linear Model (GLM)

method. It uses the variable with the highest

parameter instability to make the split. This

method was implemented in R using the

‘partykit’ package(Hothorn & Zeileis, 2014).

2. Ctree: This method uses a significance test to

select the variable for partitioning(Hothorn,

Hornik, Strobl, & Zeileis, 2019). The R ‘partykit’

package(Hothorn & Zeileis, 2014) was used.

3. CART: A gini index(Rutkowski, Jaworski,

Pietruczuk, & Duda, 2014) based function is

used for the tree partitioning. It was implemented

using the ‘rpart’ package(‘rpart’, n.d.) from R.

4. C4.5/J48: The partitioning is done selecting the

variable that maximizes the information gain

ratio(Salzberg, 1994). The method, named J48 in

WEKA, was implemented using

‘RWeka’(Hornik [aut et al., 2019) R package.

5. C5.0: This is an extension from the previous

method, made by introducing new features such

as boosting for improving the accuracy rate and

the construction of cost-sensitive trees(Quinlan,

1996). The R ‘C50’ package

20(p50)

was used.

In order to select the most appropriate method for

each dataset, the tool assesses the methods based on

evaluation metrics using the “Tree Statistics” option

in the dropdown menu. We can either choose one or

multiple methods to be tested, and the resulting

statistics are displayed. For each method, the AUC,

precision, recall, f-1 score and support are shown.

AUC (area under curve) is a bidimensional

representation of a classifier’s performance.

However, it can represent the performance as a

numerical value, and it is useful to compare

objectively the different methods. Precision is the

ratio of correctly predicted positive observations to

the total predicted positive observations. On the other

hand, recall (also known as sensitivity), is the ratio of

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correctly predicted positive observations to all the

observations in an actual outcome. The F1 score is the

weighted average of precision and recall. Finally,

support is the number of true instances for each label.

Based on all the information, users can select the most

appropriate algorithm among the five implemented to

generate the decision tree.

Once we select the most suitable algorithm for the

dataset, we can generate the tree. By way of example,

we selected the Brain Lower Grade Glioma (LGG)

project of TCGA. First, we calculated the evaluation

metrics for the five algorithms. CART method has the

highest AUC (0.58) along with glmtree (0.56) and

C4.5/J48 (0.56). Table 1 shows the metrics for CART

method.

Table 1: Evaluation metrics for CART method using the

LGG genomic data.

level

AUC

score

precision

recall

support

Dead

0.57

0.72

0.76

0.68

171

Alive

0.57

0.41

0.36

0.46

Therefore, the CART algorithm was used to

generate the tree. In Figure 6, the generated tree is

shown along with the tree edition tools. The colours

describe the outcome value for each node of the tree,

ranging from green (alive) to red (dead). By clicking

in each node, we can visualize and edit the node

(either partially or completely) and update the model

accordingly. The probability that outcome will

happen based on each condition is shown. The

features shown in the tree are the ones relevant to

predict the outcome. For example, if a patient has

IDH1 mutated, there is a 77% probability for the

patient to remain alive. However, if, in addition to this

feature, the patient’s tumour site is C71.9 (Brain,

NOS), the age of initial diagnosis is more than 37

years and the tumour histology is 9401/3 (anaplastic

astrocytoma) or 9450/3 (oligodendroglioma, NOS),

the probability to remain alive decreases to 33%.

The tree can be easily modified using the tools

provided. This feature is useful for domain experts,

which could improve the automatically generated

classification based on their experience. Users can

create new branches, delete existing ones, edit the

conditions that are evaluated, edit the outcome of the

nodes (the probability of the outcome at a given

node).

Figure 6: The generated tree using the CART method for the LGG genomic TCGA data.

VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classiﬁcation of Clinical and Genomic TCGA Data

417

Figure 7: Users can classify patients based on previously created models. In the example, the results for an LGG patient are

shown.

Classify Patient

Once you create the ML model, new patients can be

classified according to the model. Therefore, we can

predict the outcome and classify the new patients

based on the information contained in the ML model.

To do so, users must enter the values of the relevant

variables, which are then considered according to

their pre-defined weight to predict the outcome of

patients based on the model. In our case, we have

selected the survival as outcome. Therefore, the tool

shows the probability of survival of the new patient

according to the model. The generated decision tree

is shown again, but in this case, the fulfilled

conditions are highlighted in blue.

We have used the LGG TCGA model and when

building the ML model, the following features were

selected as relevant to predict to outcome: IDH1,

ATRX and TTN genes, icd_o_3_site,

icd_o_3_histology and age_at_initial_pathologic

diagnosis. We introduced the data from two patients,

the first one with the following features: IDH1 YES,

ATRX YES, TTN NO, icd_o_3_site C71.9,

icd_o_3_histology 9401/3 and

age_at_initial_pathologic_diagnosis 40 (YES

meaning that the gene is mutated). This patient has a

33% probability to remain alive and, as shown in

Figure 7, the user can view the fulfilled conditions in

the tree. However, if the same patient had been at least

three years younger, the probability to remain alive

would be 83% according to the chosen model.

Finally, the pie charts shown in the Figure 7 represent

the probabilities for the outcomes for each of the

nodes executed in the tree for the classified patient.

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4 CONCLUSIONS

In this paper, we propose VisualMLTCGA, an easy-

to-use web tool for download, pre-processing,

visualization, processing and analysis of TCGA data.

Along with TCGA data, external data can also be

uploaded and analysed. Finally, relevant features can

be extracted from clinical and genomic datasets using

decision trees for classification purposes.

After analysing different TCGA processing and

visualization applications, we did not find any

existing tool that combined downloading, pre-

processing, processing and visualization of clinical

and genomic data, such as the VisualMLTCGA does.

Additionally, VisualMLTCGA includes the creation

of decision trees as a usable feature. Due to all these

reasons, this tool is suitable for researchers and

clinicians without bioinformatics background.

Nevertheless, the tool is currently being validated

and the potential modifications that arise from the

feedback captured on this phase will be the first part

of the future work. Additionally, we will include the

possibility of downloading other type of data from the

TCGA such as Copy Number Variation or DNA

Methylation data. Furthermore, we expect to include

several machine learning algorithms such as Random

Forest, K-Neighbours or SVC.

ACKNOWLEDGEMENTS

This project has received funding from the Regional

Council of Gipuzkoa through the Science,

Technology and Innovation program.

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