Cancers are typically classified depending on their tissue of origin. However, novel large-scale genomic studies are providing more detailed molecular characterizations of tumors, and thus bring about the possibility of a more accurate classification based on their molecular profiling. Recently, our group has participated in the pan-cancer integrated subtypes study, published online today in Cell, in which a molecular taxonomy of cancer has been addressed by using the comprehensive multi-platform assays provided by the TCGA consortium for 12 diverse cancer types. This study represents an unprecedented effort to classify cancer by refining the molecular portrait of human malignancies. Read the rest of this entry »
We’re pleased to announce another incremental release of Gitools, version 2.2. Amongst the many improvements (listed at the bottom of this post) we’d like to highlight the effort that we put into improving performance, specifically with genomic data: mutual exclusion and co-occurrence statistics coupled with a new feature called “data events” – which helps to get a quick grasp of the data.
We’d like to communicate two things: We have released a new version of Gitools which brings new capabilities. With these the new Gitools 2.1.0 in the suitcase, Nuria and myself are traveling to Heidelberg, to give a tutorial session at the Vizualizing Biological data conference (VIZBI).
Two years ago I attended the VIZBI 2012 to present our latest work on and with Gitools, a tool we use to explore interactive heatmaps of genomics data. The days spent in Heidelberg were fruitful and refreshing and after all also were an inspiration to write the review about Visualizing multidimensional cancer genomics data.
Yesterday the paper describing TCGA Pan-Cancer Project was published in Nature Genetics. We’ve had the opportunity to participate in this exciting project and here I would like to explain our experience and contribution to it.
We have been interested for quite a while in the study of patterns of genomics alterations in cancer across tumor types. Thus a project like the TCGA Pan-Cancer provided a unique opportunity to apply our tools and expertise to a unique collection of data.
In the past few years we have developed computational methodologies to identify cancer drivers by analyzing the patterns of somatic mutations across tumors (i.e OncodriveFM and OncodriveCLUST) as well as tools to facilitate the visual exploration of multidimensional cancer genomics datasets (i.e. Gitools, IntOGen, see our review on this topic if you are interested in this), we now had the opportunity to apply those tools to TCGA Pan-Cancer data.
As some already may know, Gitools has been integrated with GenomeSpace. Together with the GenomeSpace team we have set up a tutorial that demonstrates the power behind the GenomeSpace approach. The tutorial may be found here and helps the user to extract data from ArrayExpress, normalize it on GenePattern and then load and browse it in Gitools. All these steps are being done without the need to manually download any tool nor data set. You can sign up for GenomeSpace and try it right away.
We have started to distribute Gitools in the version 1.8.x. This latest version include important new features in terms of communication of Gitools with other tools (i.e. IGV, GenomeSpace, GSEA, excel). Now the user will get more flexibility for loading data in Gitools. These are the most important improvements in that aspect:
- Send matrices from IGV (Integrative Genomics Viewer) directly to Gitools
- Load matrices from GenomeSpace
- .GCT file support for matrix files (format used by GSEA).
- Import matrices from Excel data sheets
Go download latest version
We have been preparing a new version of Gitools with many improvements, amongst which there is a new IGV search, the use of categorical scales and new data aggregation methods that can be used to annotate the heatmap.
Gitools is an interactive heatmap viewer which can also perform various analysis over the data. Heatmaps in Gitools can be multidimensional, with various values per cell, which is very practical for cancer genomics data analysis and visualization (read more).
Let us introduce the new features step by step.
As you may have read in the last post, Günes and Nuria presented the Sample Level Enrichment Analysis (SLEA) as a methodology to analyse the transcription level of each sample for groups of genes (like for example pathways, gene signatures, etc.)
It is an easy way to stratify the samples into subgroups and/or relate the transcription level status of modules to clinical data. So this last week we have prepared a further video tutorial to show you how to perform SLEA easily with Gitools and gain more insight into your data.
Watch the video below or read the instructions in the fourth step of the Case Study: “Study multi-dimensional cancer data with Gitools”.
With this video tutorial we also release a new version of Gitools, version 1.6.2 so it is possible to have multi-value data matrices as input data for the enrichment analysis. Also we got rid of some bugs.
Download the latest version at www.gitools.org