Rare Cancer Meta-Analysis, pt6: Finding related curated studies and cell types
This entry is part six in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I use Illumina’s BaseSpace Correlation Engine (CE) to analyze the 130 genes over-expressed in the more severe forms of pRCC.
I first use Partek Genomics Suite to create a summation table of all the scaled fold change information on our 130 genes (pt6.1), and then I import this file back into CE (pt6.2) for further analysis.
Rare Cancer Meta-Analysis, pt6.1: Creating a summation table of our genes using Partek GS
Rare Cancer Meta-Analysis, pt6.2: Importing our genes back into Correlation Engine
I then demonstrate how to compare our gene list to the millions of comparisons contained in the ~22,000 curated studies in the CE database.
Rare Cancer Meta-Analysis, pt6.3: Comparing our list to all other curated studies in CE
I also go over using the Body Atlas tab to identify correlated tissues and cell lines based on our cluster profile.
Rare Cancer Meta-Analysis, pt6.4: Using Body Atlas to identify similar tissues and cell types
A text file containing the list of all 130 clustered genes, along with their normalized fold change scores for each comparison, can be downloaded at this Google Drive link.
Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 2018 that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.
The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events.
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Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
-Michael Edwards PhD, Bioinfo Solutions LLC