Welcome to GlioVis : a user friendly web application for data visualization and analysis to explore brain tumors expression datasets.
GlioVis is very easy to use:
For each dataset it is available a list of common "pre-defined" plots (see the table below) and a list of dataset-specific "user-defined" box plots. An overview of all the box plots for a given dataset can be found at "Explore/Summary/Plots".
Available datasets:
Currently only HGNC-approved "Gene Symbols" are supported.
Yes, all the plots can be downloaded in various formats: .pdf, .bmp, .jpeg, .tiff or .jpg.
Yes, it is highly recommended for reproducibility issues. For each plot there is a "Data" tab containing the actual data used to generate the plot. The data can also be downloaded at "Explore/Summary/Data".
SubtypeME: Classify tumor samples based on mRNA expression profiles.
EstimateME: Estimate of STromal and Immune cells in MAlignant Tumor samples (Yoshihara K. et al., 2013).
DeconvoluteME: Deconvolute gene expression profiles from heterogeneous tissue samples into cell-type-specific subprofiles.
Of course! If you do so, please include references for the dataset(s) you used and cite: "GlioVis data portal for visualization and analysis of brain tumor expression datasets" (Bowman R. et al., Neuro-Oncology 2017).
Please adhere to the TCGA publication guidelines when using TCGA data in your publications.
You can contact us by email or through the GlioVis user discussion group.
No great discovery was ever made without a bold guess.
Isaac Newton
Summary statistics
Tukey's Honest Significant Difference (HSD)
The table shows the difference between pairs, the 95% confidence interval and the p-value of the pairwise comparisons. ***p<0.001; **p<0.01; *p<0.05; ns, not significant.Pairwise t tests
Pairwise comparisons between group levels with corrections for multiple testing (p-values with Bonferroni correction).Kaplan-Meier estimator survival analysis
Calculating, please wait
Test for association/Correlation between paired samples
(Note: Correlation cannot be computed for groups with less then 4 samples)Correlate expression of a gene with all the genes in the dataset
Calculating, please wait
Calculating, please wait
Retrieving mutation data
Calculating, please wait
Calculating, please wait
Calculating, please wait
Gene ontology enrichment analysis
Calculating, please wait
Calculating, please wait
Calculating, please wait
KEGG enrichment analysis
Calculating, please wait
Calculating, please wait
Calculating, please wait
Calculating, please wait
Be patients, switching to another tab will crash GlioVis ...
Calculating, please wait
Be patients, switching to another tab will crash GlioVis ...
Calculating, please wait
Be patients, switching to another tab will crash GlioVis ...
Generate and compare subtype calls by SVM, K-NN and ssGSEA
Calculating, please wait
Be patients, switching to another tab will crash GlioVis ...
Calculating, please wait
Deconvolute gene expression profiles into cell-type-specific subprofiles
Calculating, please wait
Calculating, please wait
Calculating, please wait
mRNA analysis
Copy number
Survival
coxph
function from the ‘survival’ package. To generate HR plot we partially use some code previously describend in Cutoff finder.Mutations
CIMP status
CIMP status has been determined by Support Vector Machine (SVM), using TCGA GBM as training dataset.
Subtypes
- GBM
For the GBM subtype classification we use the 3 TCGA expression subtypes (Classical, Mesenchymal,Proneural) as defined in Wang Q. et al. 2017.
- LGG
For the LGG subtype classification we use the 3 TCGA molecular subtypes (IDH mutant with 1p/19q codeletion, IDH mutant without 1p/19q codeletion, IDH wild-type) as defined in NEJM, 2015. Important note: the GlioVis LGG classification identifies “molecular subtypes” using expression data. Such approach has to be considered very preliminary untill further validation.
Classification algorithms:
ksvm
function of the ‘kernlab’ package. As training dataset for the GBM we use the TCGA GBM samples described in Wang Q. et al. 2017. As training dataset for the LGG we use the TCGA LGG samples described in NEJM.knn3Train
function of the ‘caret’ package, using the subtypes as in the SVM calls.runSsGSEAwithPermutation
function of the ‘ssgsea.GBM.classification’ package. Not available yet for LGG samples.Version 0.20 (18/03/2016)
Code available on GitHub
GlioVis is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License, version 3, as published by the Free Software Foundation.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
© Massimo Squatrito (2015-2020)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
15-01-2020
New datasets
New features
26-09-2017
10-2-2017
DeconvoluteMe:
16-12-2016
09-11-2016
GlioVis paper has been published! Bowman R. et al. 2017
New features
21-09-2016
24-06-2016
Updates
New datasets
18-03-2016
02-02-2016
15-01-2016
New features
Updates
27-11-2015
New features
Updates
30-09-2015
14-08-2015
07-05-2015
Gliovis 0.1 is live!!