Dr Heather Turner

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Data Analysis

Data analysis projects may involve data processing, application of standard or novel techniques, and production of a report. Reports are typically created programmatically, where outputs from the code are directly incorporated into the document, to enable reproducibility of the full analysis.

Example data analysis projects include:

Predictive modelling using proteomics data
Building a classification model using penalized logistic regression to evaluate the additional predictive value of proteomics data over clinical predictors.
Analysis of timecourse microarray experiment
Exploratory analysis of sample similarities using principal components analysis. Assessment of significant genes using generalized least squares model to account for correlation over time (using the limma package). Use gene set enrichment analysis to identify significant gene sets.
Mediation analysis of an intervention study
Applying the R package mediation to investigate the mediating effect of a individual's belief regarding treatment on the effect of treatment in a clinical trial for restless leg syndrome.

Methodology Studies

The objective of a methodology study may be to research the state of the art in a particular area, to propose a novel approach or to evaluate competing methods both in theory and practice.

Example methodology projects include:

Methods to identify synergism/antagonism
Research and development of procedures for evaluating the significance of departures from an additive model for compound interactions.
Exon array software evaluation
Evaluating JETTA package for the analysis of Human Exon 1.0 and Human Transcriptome 2.0 arrays. Providing analysis templates, using customised R functions.
Gene set enrichment analysis review
Reviewing current methodology for gene set enrichment analysis and identifying methods that may provide some benefit over the method currently in use by the client. Applying selected methods to internal data sets to assess performance in practice and investigate differences between the methods.

R Programming

An R programming project may involve writing a script to perform specified tasks using existing R functions, implementing a novel method in R, or developing a custom R package.

Example programming projects include:

R function to fit Bayesian Model
Coding block-update Gibbs sampler in R for Bayesian hierarchical model that client was unable to fit using WinBUGS. Provision of example analysis using client's data on retail sales.
R package for Dose Response Modelling
Development of custom R package from prototype code for mixed effects dose response modelling. Follow-on “Generation II” package combining stages of analysis.
Demonstration scripts for R novice
Writing R scripts to demonstrate clustering, logistic regression and Cox regression using client's data on laboratory analysis of breast cancer tumours.
Automated Evaluation of Predictive Models
R code to compare the predictions from computational chemistry models to observed values and generate a Word report and associated data files.

Training

I have run general R courses in collaboration with Prism, based in Cambridge, including Getting Started with R and R Programming. If you are interested in attending such a course, or having a custom course run on-site, you are welcome to contact myself or Prism.

Sample slides from "Getting Started with R"
Sample slides from "Programming with R"