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Rare Cancer Meta-Analysis, pt.4: Clustering our meta-data with Partek Genomics Suite

Updated: Apr 3, 2018

This entry is part four 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 Partek Genomics Suite to display the meta-data as a heat map and employ hierarchical clustering to find connected genes in the dataset based on its normalized gene scores across all 17 different comparisons. A text file containing the papillary meta-analysis used in this video can be accessed at this Google Drive link.

pt4.1: Review and introduction to heat maps and clustering

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.

pt4.2: Filtering the meta-data for visualization and clustering using 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.

pt4.3: Running hierarchical clustering analysis and formatting our heat map using Partek GS

Please subscribe to this YouTube channel or sign up to my 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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