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About Caroline

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So far Caroline has created 12 blog entries.

ggplot2 101 : Easy Visualization for Easier Analysis

Biological data are often easier to interpret and analyse when we can visualize them via a plot format. A good way of doing so is by exploiting the different options of ggplot2, a R plotting system. In the following post, I will present some of my go-to tricks to visualize data: nothing to fancy or to hard, perfect for both the R masters and the R beginners! The sample codes are in R and the ggplot2 library must be installed [...]

By | 2017-05-19T15:08:52+00:00 May 18, 2017|Categories: Data Analysis, Data Visualization, R, Uncategorized|0 Comments

Chemical Screen: Evaluating drug sensitivity

The study of the cellular response to a chemical compound is crucial to the development of new therapeutic drugs. Such an analysis is usually done by a screen experiment where the disease-specific cells (such as leukemia primary cells) are exposed to chemical compound for different concentrations. The response, in the form of sensitivity, of these cells is conventionally quantified by the IC50 or the l’EC50. Here are some notions to keep in mind when we analyze these values.  IC50/EC50 : estimate of [...]

By | 2017-04-29T11:08:17+00:00 February 13, 2017|Categories: Data Analysis|Tags: |0 Comments

Bootstraps and Confidence Intervals

When analyzing data, you might want or need to fit a specific curve to a particular dataset. This type of analysis can result in instructive outputs regarding the relationship between two (or more...) quantifiable parameters. The main object of this post is not how to implement such fitting, but rather how to display the goodness of such a fit i.e. how to calculate a confidence interval around a fitted curve. That being said, I will show how to do curve fitting in [...]

By | 2017-04-29T18:33:55+00:00 September 29, 2016|Categories: Data Analysis, R, Statistics|Tags: |1 Comment

SciPy and Logistic Regressions

Given a set of data points, we often want to see if there exists a satisfying relationship between them. Linear regressions can easily be visualized with Seaborn, a Python library that is meant for exploration and visualization rather than statistical analysis. As for logistic regressions, SciPy is a good tool when one does not have his or her own analysis script. Let's look at the optimize package                        from scipy.optimize import [...]

By | 2017-04-29T16:58:35+00:00 June 9, 2016|Categories: Data Analysis, Python|Tags: , |0 Comments

Formatting data for Circos with R

When generating a Circos plot, the formatting of the data to be represented is a crucial step. Here are some pointers on how to avoid the dreadful *** CIRCOS ERROR ***. All data files must be in text format. For instance, using R, I would generate a myData.txt file that I would then call within a specific plot block (<plot>...</plot>). Data files are used for 2-dimensional graphical representations (histogram, scatter plot, heatmap, tiles), labels (which are technically also a type [...]

By | 2017-04-29T15:36:21+00:00 October 29, 2015|Categories: Data Visualization, R|Tags: , , |0 Comments