Easy Access to TCGA Data in R

Today I discovered that the Memorial Sloan Kettering folks at cBioPortal have made it super easy to access The Cancer Genome Atlas (TCGA) data in R.  No need to download massive amounts of data, extract needed files, and link data types together. Rather, they have a public data portal that R can easily interact with using the cgdsr library.  Data queries can be made for all TCGA cancer tissue types across a multitude of different data types (ex: genotyping, sequencing, copy number, mutation, methylation). Clinical data is even available for several cancer samples.

To install the library, open your R terminal and type: install.packages("cgdsr"). Alternately, to install on a UNIX cluster account, see this link.

The main tools you will use are:
getCancerStudies- shows all the studies included in cBioPortal (not limited to TCGA data) and includes descriptions. Use this to find a study you are interested in and its respective study id.
getCaseLists- divides study into case sets based on available data for samples. Use this to select the cases you are interested in querying.
getGeneticProfiles- shows available genetic data for a study.  Use this to select the type of data you are interested in querying.
getProfileData- performs the query on the case samples and genetic data of interest.
getClinicalData- extracts clinical data for a list of cases.

Here is an example script that can be used as a scaffold to design queries of the TCGA (as well as data from the BROAD and other places). It can be used to access all types of genetic data stored in the cBioPortal repository, but this particular example focuses on finding mutations within the BRCA1 gene in lung cancer cases.

Is there a GWAS on that?

A great online resource to find whether a genome-wide association study (GWAS) has been published on a certain trait or disease is the National Human Genome Research Institute (NHGRI) webpage where they maintain a curated catalog of published GWAS.  Here trained curators are constantly scanning PubMed publications and other genomic resources looking for association studies linking a genomic position (usually a tagging SNP) to a disease trait of interest.  Details listed include study size, population, locus, risk allele, odds ratio, p-value, and other pertinent statistics.  Recently, the NHGRI in collaboration with the European Bioinformatics Institute (EBI) released an interactive version of the GWAS catalog called the GWAS Diagram Browser.  This provides a great way to visualize and filter many of the genome-wide significant findings from genome-wide associations studies.  Highlights include filtering by disease, time series views, and some useful downloads.

Another noteworthy resource is the HuGE Navigator GWAS Interagator.  This is a search tool similar to the NHGRI GWAS catalog, but more focused on a search terms.  Handy links are provided to other resources.  Of particular interest are links to visualize the variants in the UCSC Browser.

I am sure other GWAS resources exist as well, but these were the two main ones that first came to mind for me.  If you know other great GWAS resources capable of linking a genomic marker with a disease, please share in the comments below.

Where to Find 2014 Cancer Statistics and Estimates

With cancer accounting for approximately 1 in 4 US deaths it is important to know a good resource for cancer-related statistics.  Luck for us, the 2014 Cancer Facts and Figures has just been released by the American Cancer Society.  This is an excellent resource on the distribution of cancer by age, sex, race, type, and geographic location in the United States.  It includes cancer specific information on new cases, deaths, signs and symptoms, risk factors, prevention, early detection, treatment, and survival.  This is a wonderful, up-to-date resource for getting cancer-related estimates on prevalence, incidence, mortality, and survival in the US as well as look at trends and statistics from years past.  In addition, the website includes a wealth of other resources including tables, slides, and figures that are handy visual aids for presentations.

The Health Hazards of Sitting

Anyone who spends considerable amounts to time managing, analyzing, and interpreting data ultimately spends much of it sitting at a computer.  While you may be familiar with the typical stiff neck or sore buttocks from spending hours at a desk, there are a number of other health risks you may not be aware.  Such negative health impacts include increased risk of heart disease, diabetes, colon cancer, muscle degeneration, and soft bones.  A recent piece in the Washington Post highlights these health hazards and presents some exercises and recommendations to avoid these negative health outcomes.  Such preventative measures include taking breaks to stretch and walk as well as changing sitting posture.  Anyways, a small health tidbit to add to the Genome Toolbox to hopefully make you more productive and potentially prevent some negative health outcomes.

Worldwide Cancer Statistics

When drafting manuscripts and preparing presentations it is often nice to display relevant cancer statistics to give the audience a perspective of the scope of the disease.  GLOBOCAN is an excellent tool that provides a variety of worldwide cancer statistics.  Estimated 2008 cancer incidence, mortality, prevalence, and disability-adjusted life years (DALYs) are available for 184 countries and 27 cancer types. Although the figures are far from polished, they do provide just about any cancer statistic I could think of.  Highlights include interactive maps, sex stratified analyses, and age-adjusted rates.