SMS scnews item created by John Ormerod at Mon 31 Mar 2014 1528
Type: Seminar
Distribution: World
Expiry: 12 Apr 2014
Calendar1: 11 Apr 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod4.pc (assumed)

Statistics Seminar: Yuliya Karpievitch (UTAS) -- Quantitative Analysis of Mass Spectrometry Data

Quantitative Analysis of Mass Spectrometry Data

Quantification of liquid chromatography mass spectrometry (LC-MS) 
data is complicated by missing values. Some are easier to deal 
with than others. For example, values missing completely at random 
can be ignored or imputed based on the observed peptide abundances.  
Left-censored values that fall bellow the detection limit are 
harder to deal with as observed values are not representative of 
the missing ones. Here I will present a statistical model that 
takes into account the ’missingness’ mechanism and imputes the 
values accordingly. I will also discuss a normalisation method that 
removes biases of arbitrary complexity.