TargetTranslator BETA

You have reached the TargetTranslator, an interactive tool that enables the creation of gene signatures and exploration of their connections to different compounds.

TargetTranslator is currently in beta and new functionality will be added continuously.

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Insights are waiting, let's get started.

1. Select Data

Upload your own dataset and/or add data from the preprocessed ones.

If you upload your own data, make sure to follow our formatting guidelines.

Custom datasets:
Preprocessed datasets:
TCGA, Pan-Cancer
(Adrenocortical Cancer)
#Patients: 79
Download processed data
(Bladder Cancer)
#Patients: 427
Download processed data
(Breast Cancer)
#Patients: 1215
Download processed data
(Cervical Cancer)
#Patients: 309
Download processed data
(Bile Duct Cancer)
#Patients: 45
Download processed data
(Colon Cancer)
#Patients: 492
Download processed data
(Large B-cell Lymphoma)
#Patients: 48
Download processed data
(Esophageal Cancer)
#Patients: 196
Download processed data
(Glioblastoma)
#Patients: 166
Download processed data
(Head and Neck Cancer)
#Patients: 566
Download processed data
(Kidney Chromophobe)
#Patients: 91
Download processed data
(Kidney Clear Cell Carcinoma)
#Patients: 606
Download processed data
(Kidney Papillary Cell Carcinoma)
#Patients: 323
Download processed data
(Acute Myeloid Leukemia)
#Patients: 173
Download processed data
(Lower Grade Glioma)
#Patients: 529
Download processed data
(Liver Cancer)
#Patients: 423
Download processed data
(Lung Adenocarcinoma)
#Patients: 576
Download processed data
(Lung Squamous Cell Carcinoma)
#Patients: 552
Download processed data
(Mesothelioma)
#Patients: 87
Download processed data
(Ovarian Cancer)
#Patients: 309
Download processed data
(Pancreatic Cancer)
#Patients: 183
Download processed data
(Pheochromocytoma & Paraganglioma)
#Patients: 187
Download processed data
(Prostate Cancer)
#Patients: 550
Download processed data
(Rectal Cancer)
#Patients: 170
Download processed data
(Sarcoma)
#Patients: 265
Download processed data
(Melanoma)
#Patients: 473
Download processed data
(Stomach Cancer)
#Patients: 450
Download processed data
(Testicular Cancer)
#Patients: 139
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(Thyroid Cancer)
#Patients: 572
Download processed data
(Thymoma)
#Patients: 122
Download processed data
(Endometrioid Cancer)
#Patients: 555
Download processed data
(Uterine Carcinosarcoma)
#Patients: 57
Download processed data
(Ocular melanomas)
#Patients: 80
Download processed data

2. Define your risk groups

What would you consider a risk group? Mark all factors that makes sense to you. Together, they will be used to create the gene signature. The selected variables will be highlighted in the heatmap below, while deselected variables are toned down in a darker color. High values are represented with a higher intensity of the color.

[No dataset has been selected]

3. Algorithm settings

Is survival time a part of your definition of a risk group? Then it might be a good idea to take censoring effects into consideration. To do that, check the checkbox below and specify where to find the survival time and the censoring variable in your dataset.

Survival time:

Life status:

4. All is set, time for analysis

This is your configuration. Read through it to make sure it is correct.

Datasets (expression/clinical):
undefined
undefined
Risk group definition:
undefined
Survival
No
Find my targets

Time to dig into the results...

Rank Perturbation Score FDR Direction

Drug Scores

This table tells you what drugs are most likely to induce the change you wanted to investigate.

Example:

Let's say you have a variable x that can take on the values 0 and 1, corresponding to low and high risk respectively. Then the table will contain the drugs that will most likely change a high risk gene signature to a low risk gene signature (direction: positive) and vice versa.

Protein target enrichment

This table contains protein targets as defined by the STITCH database. The top ranking targets are those that seems to be enriched in the drug scoring table.

Each target has an associated empirical cumulative distribution functions (ECDF), visible when hovering the target row. This makes it easy to compare the ECDF of the target and the ECDF of all other targets.

Target D-value p-value Direction
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Documentation

link to FAQ

link to videos

link to heavy documentation

link to algorithm overview

Troubleshooting
Please make sure that you data is correctly formatted, i.e. with unique row and column identifiers.
If cox seems fishy, decrease the selected stratifications to below 20.

Make sure that your expression values are normally distributed, otherwise, try to log2 transform the data before submitting them for analysis.

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R package

download the r package

give link to cran repository

link to user guide for the package

link to publication

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About Us

link to the group website

some contact information