Symposium on Big Data in Mental Health
- Wednesday, 09 July 2014 11:47
We are excited to announce our upcoming symposium on Big Data in Mental Health.
The event will be held at ORTUS, Denmark Hill, on July 23rd and highlights will include Maneesh Juneja, Chris Hollis from MindTech, Will Spooner from Eagle Genomics and speakers from the University of Oxford, KCL and SLaM.
See the event page for more details: https://phidatalab.org/events/big-data-in-mental-health/
Registration on Eventbrite: https://www.eventbrite.co.uk/e/big-data-in-mental-health-biomedical-research-tickets-11667875931
- Thursday, 03 July 2014 14:13
Algorithm design and strategy by Dr David Baker (Janssen). Implementation and application by Dr Steven Kiddle (KCL). For queries please contact steven.kiddle (at) kcl (dot) ac (dot) uk .
Pre-processing for Relative Quantification (PRQ) of TMT tagged LC-MS data is an R script written to pre-process mass spectrometry data. It performs the following steps:
Median normalisation to correct for labelling and MS-run variation (Step 1) is performed within each sample and gel fraction. This involves calculating the median of the ratios of all peptide intensities from one sample versus the corresponding intensities measured in the reference sample. All intensities relating to that sample and gel fraction are then divided by the median ratio. Ratio scores for each peptide are then calculated (Step 2) by calculating the ratios of the normalised data for each peptide by dividing it by the reference intensity. Ratios corresponding to the same source protein, peptide sequence and gel fraction are then summed. Protein level data is derived from these summed peptide scores (Step 3) by taking either the mean or median of all peptide scores from the same source protein and gel fraction. This protein level data is then collected across all sixplexs (Step 4).
A paper describing and applying PRQ has been submitted.
Open source script and readme available here