Analysis of high-throughput cell imaging databy
Andreas Hadjiprocopis (contact details: 'andreashad2' then the funny snail symbol then 'gmail.com' without the quotes)
In this place I present four problems I tried to tackled during my work at the Institute of Cancer Research, London between 2009-2013. There are others but are not shown here.
The main idea is to analyse large datasets comprising of various morphological features of cells (e.g. cell area) belonging to different cell lines (e.g. HeLa etc.) and treated in different conditions (e.g. TNF-alpha etc.) for different time durations. Examples of cell lines, conditions et al are here.
The features have been extracted from high-throughput images of cells processed by third-party software.
The presented work here serves as an exploration to dealing with high-throughput cell imaging data and all the software and pipelines are in prototype form (mainly written in perl, bash, C++ and R).
Here they are:
Models predicting cell line or NFkB activation given cell morphological features, here.
Calculating differences in the value of a cell's morphological feature (from high-throughput imaging, e.g. cell area) for two different cell-lines/cell-treatments. Here.
For a given cell-line/treatment-condition/treatment-duration, are there pairs of features which are likely to occur together? (For example, baldness and sex=male in humans). Here.
A method to see how different (separable) are pairs of cell-lines/treatment/duration with respect to cell shape and texture. Basically, whether cells belonging to one of two cell-lines/treatment/duration combinations, can be distinguished based on their cell shape and texture. Here.
(contact details: 'andreashad2' then the funny snail symbol -aka 'at' - then 'gmail.com' without the quotes)