From an expression profile of a set of tumor samples, in Gitools you can perform SLEA to assess the transcriptional status of modules (ie. pathways) per sample.

The identification of molecular biomarkers from expression data is a major objective in cancer research. It is clear that there is a benefit in pathway biomarkers (ie. measuring the activity of the pathway instead of individual genes). One easy way to analyze the transcriptional status of pathways (or other gene sets) is using Sample Level Enrichment Analysis (SLEA) in Gitools. This way you can assess the status of each pathway in each sample. This can be used to identify tumor subtypes and to correlate molecular features with clinical features.

It is easier to explain it with an example:

Using a dataset of 156 lung tumors and adjacent normal lung tissue samples (from Hou et al 2010), I did a SLEA with Gitools to find pathways that are significantly up or down-regulated in different samples. The result is a big heatmap with samples as columns and pathways as rows. Each cell contains the result of the enrichment analysis for a particular pathway in a sample. The interactive capabilities of the Gitools heatmap viewer helps to intuitively interpret the results. For example, in the following figure samples are ordered according to their clinical annotation and we can see very clear differences between normal and tumor samples for a selected list of pathways. For instance, apoptosis genes and genes involved in MAPK signaling pathway tend to have lower expression values compared to normal samples, while cell cycle genes tend to have higher expression values in the tumor samples represented in the dataset.

If you want to learn more and even try it yourself, you can follow this Gitools tutorial.

Heatmap of tumor samples as columns and pathways as rows. Enrichment is shown with colors from blue (down-regulation) to red (up-regulation) while gray values indicate no significant deviation form expected median value. Samples are ordered according to clinical annotation as follows: squamous cell carcinoma (SCC), adenocarcinoma (ADC), large cell carcinoma (LCC) and normal lung tissue samples (Normal)

As of now you can download the new version of Gitools – version 1.5.10 – which keeps up with the newest changes of the biological databases KEGG and Ensembl.

As some may have read or heard, KEGG has gone through some changes last year and so has the accessibility of its data. This change broke the functionality of the KEGG importer in Gitools. This is now fixed!

Additionally we also made an update for the Ensembl importer and added the new versions in the archive to make sure all data can be comfortably downloaded through Gitools.

That’s it for now, expect more news to come!

This time, we shall have three full days to cover the multiple features of IntOGen and Gitools. We will start with background lectures on integrative onco- and other genomics as well as on the statistical meaning of each of the analysis. Then we will cover, as a first in our course, some very practical aspects which shall encourage participants to use the tools on their own data, such as data formats and how to prepare data sets for the analyses in Gitools. There will be enough time to work on a number of hands-on exercises for analyses such as functional enrichment, combination of experiments, prioritization of disease-related genes, correlation, overlapping and clustering of experiments. For data from next generation sequencing, we will present Condel, a tool that classifies the point mutations you might find in your sequences. Visit the course page to see the detailed agenda and to register.

We are always curious to find out who are the people attending our courses, so we will ask participants to present themselves and their work and to let us know which is their favourite analysis during the course, and we are more than happy to receive follow-up questions by email weeks and weeks after the course from all over the world.

Attendees will learn how to handle and analyze genomic data to answer specific questions using tools developed in the Biomedical Genomics lab: IntOGen, Gitools and Condel. We will deal with questions such as:

• Which mutations are likely to affect the function of the protein and which are probably neutral?
• Which of the genes affected by those mutations are already known to be involved in cancer or other diseases?
• Which pathways or biological processes are affected by the transcriptomic alterations detected in my experiment?

The course will be held in the UPF (Universitat Pompeu Fabra) facilities in the Life and Health Sciences building, next to the PRBB in Barcelona, Spain. For more information on how to register please visit its webpage: Course on Bioinformatics for Integrative Genomics.

We encourage you to register asap if you are considering to come, since the number of places is limited. Please feel free to spread the word amongst your colleagues!

There has already been some publications were Gitools was used for obtaining results and creating figures, so the next logical step was to get our software published.

Now I am pleased to announce that today it has been published:

Perez-Llamas, C. & Lopez-Bigas, N. Gitools: Analysis and Visualisation of Genomic Data Using Interactive Heat-Maps. PLoS ONE 6, e19541 (2011)

But this is not the last step in Gitools evolution, I hope that more people start using it and this fosters its further development by reporting bugs and suggesting new features. We want Gitools to continue growing and being useful for researchers. In fact, I already have some plans for it, such as improvements to the heatmap editor or adding new data mining analysis.

To have a better idea of the things you can do with it you can visit these posts:

• Basic and intuitive analysis of microarray datasets using Gitools (Part 1)
• Basic and intuitive analysis of microarray datasets using Gitools (Part 2)

Gitools is an open source project and there is a site for people to submit new issues and ask for support at https://bg.upf.edu/forge/projects/gitools, there you will find a wiki were it is explained how to download the source code and compile it for those interested on it.

I hope you find it useful on your daily work and remember that your feedback is welcome to keep it improving and allow other people to benefit from it.

For this analysis I used the same dataset that was introduced in the previous post, namely a microarray dataset profiling 156 lung tumors and adjacent normal lung tissue samples by Hou et al. 2010. However, in this case I preprocessed the it to have a matrix of log2 ratios between tumor and normal samples.

As my objective was to identify genes that are significantly up-regulated in this experiment, I used Oncodrive. This is a simple statistical method that, given a matrix of alterations for genes and samples, assesses for each gene if it is altered in more samples than expected by chance. This is the method that we use in IntOGen to identify significantly altered genes. The result is a p-value per gene that indicates if the gene is significantly altered (up-regulated in this case).

I applied this method to the matrix of log2 ratios of Hou et al. 2010, considering up-regulation any fold change higher than 1.297. This is the optimal cutoff for this dataset obtained as explained in the supplementary material of IntOGen paper. As a results I obtained a Gitools matrix with a single column with a p-value per gene. I found, for example, that CSK2 and SOX4 genes are signifincantly up-regulated, and I know that those two genes are also up-regulated in other lung cancer experiments in IntOGen.

You can try to reproduce this same analysis following this step by step tutorial. Enjoy it!

In a next post I plan to explain how to compare if the genes identified as significantly up-regulated in this dataset are also up-regulated in other experiments from IntOGen.

For that, first I needed to find a nice dataset that would serve as an example in Gitools. Gunes pointed me to a recent paper by Hou et al. 2010. The authors of this paper identified interesting gene signatures in non-small cell lung cancer useful for histo-pathological classification and for prediction of clinical outcome. In this study, they profiled 156 lung tumors and adjacent normal lung tissue samples. This is a very interesting dataset and I decided to use it to prepare a series of step by step tutorials using each of the currently available analysis methods in Gitools, namely: Enrichment analysis, Correlations, Oncodrive, Combination of experiments and Overalps.

I started with a matrix of median-centered log-intensity values divided by standard deviation for the 156 samples. First I did pathway enrichment analysis to find pathways that are significantly up or down-regulated in different samples. I used the mean-z-score analysis, which measures the difference between the observed mean expression value in a set of genes as compared to the expected. Positive and negative z-scores indicate that genes in the module have significantly higher or lower expression values in the sample.

The result is a big heatmap with samples as columns and pathways as rows. Each cell contains the result of the z-score analysis for a particular pathway in a sample. The interactive capabilities of Gitools heatmap viewer helps to intuitively interpret the results. For example, after sorting the columns (samples) by histology, I could identify very clear differences between normal and tumour samples. For instance, cell cycle genes tend to have higher expression values in the tumour samples represented in the dataset. While apoptosis genes and genes involved in MAPK signaling pathway tend to have lower expression values compared to normal samples.

On the left, gene expression heatmap. On the right, pathway enrichment heatmap.

In a series of next posts I will explain the other analyses and the results obtained with this dataset.

This last Friday we published the code of Gitools software under an open source license (Apache 2.0). We have made the source code available through the subversion repository and we allow users to report bugs and suggestions using the Redmine project management web application at https://bg.upf.edu/forge/projects/gitools.

All Gitools users are welcome to interact with us using the Gitools project web page.

• To inform about bugs, suggest new features and get support go to https://bg.upf.edu/forge/projects/gitools/issues/new. Before you can submit tickets you need to register. If you are not registered yet you can do it by clicking on register button on the top right corner.
• To explore the source code go to https://bg.upf.edu/forge/projects/gitools/repository
• To download the source code and compile it follow the instructions detailed in https://bg.upf.edu/forge/projects/gitools/wiki

We encourage you to participate and give feedback about gitools to make it a better application.