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Rare Cancer Meta-Analysis, pt.3: Formatting and visualizing our cancer meta-data

This entry is part three 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 these installments, I describe the exported excel file from the meta-analysis in Correlation Engine (CE) and discuss formatting these data to combine into one analysis. I also demonstrate how we can upload this information into the statistical/graphical platform Partek Genomics Suite for further visualization and statistical work. A link to the actual excel file containing the papillary meta-analysis can be accessed at this Google Drive link.


pt3.1: Describing the exported meta-data from CE

Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 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.

pt3.2: Importing and formatting the meta-data with Partek GS

The opinions expressed during these videos 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.


pt3.3: Visualizing the meta-data with a PCA plot

Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted.


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

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