Youtor
20201020 PCA Proteomics QC
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=V3bwpMX_RKY
Slides for this talk can be viewed here: https://drive.google.com/file/d/1D8NX.... • To kick off the South Africa Society for Bioinformatics' Virtual Seminar Series, I decided to describe the value of quality metrics and Principal Components Analysis (PCA) for evaluating variability in LC-MS/MS proteomics experiments. I started by describing the variability that can be observed when the same sample is repeatedly analyzed by LC-MS/MS and moved to variability in which proteins were called as differences when two different cohorts were analyzed by LC-MS/MS. We examined processes such as enrichment, depletion, and fractionation that can increase the variability of measurements. • The discussion on the role of PCA tried to explain the meaning of projection pursuit, and we saw its value for unsupervised learning, when the difference between cohorts is a dominant source of variance overall. • Finally our discussion turned to the value of PCA-combined quality metrics for recognizing outliers, detecting batch effects, and recognizing different sources of variability through ANOVA. Most of these applications analyzed QuaMeter IDFree metrics in PCA, sometimes through the easy-to-use Assurance software. The HUPO-PSI has taken an interest in quality control, with a working group approaching publication for the mzQC standard for transmitting quality control information among tools in these pipelines.
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