gene expression – IRIC's Bioinformatics Platform

Big data, big challenge

You've probably heard the expression "Big Data" before. Particularly, if you read Simon Mathien's blog post on IRIC's website. (If you haven't read it yet, you should do it now!). There exist several definitions (or interpretations) of this expression, which is best summarized by the following two : Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges; (also) the branch of computing involving such data Oxford English Dictionary Domaine technologique dédié [...]

By Geneviève|2017-05-02T21:05:43+00:00April 24, 2017|Categories: Data Analysis|Tags: big data, data integration, gene expression|3 Comments

Logistic regression and GTEx

Working with all sorts of data, it happens sometimes that we want to predict the value of a variable which is not numerical. For those cases, a logistic regression is appropriate. It is similar to a linear regression except that it deals with the fact that the dependent variable is categorical. Here is the formula for the linear regression, where we want to estimate the parameters beta (coefficients) that fit best our data : \begin{equation} Y_i = \beta_0 + \beta_1 X_i [...]

By Geneviève|2017-04-29T17:44:14+00:00January 27, 2017|Categories: Biology, Data Analysis, Python|Tags: curve fitting, gene expression, machine learning|0 Comments

Applying PCA to Leucegene data

GEO offers an extremely rich source of transcriptional profile data, but downloading and preparing a dataset is often an obstacle to aspiring bioinformaticians. I'll walk you through one way to do it using the Leucegene dataset as an example. Once this data is loaded and ready to use in R, I'll then present a very simplified and practical perspective on the use of PCA for exploratory analysis. Loading data A dataset of 285 transcriptional profiles of acute myeloid leukemia (AML) [...]

By Sébastien|2017-04-29T23:05:21+00:00November 17, 2015|Categories: Data Analysis, R|Tags: gene expression, PCA|0 Comments