Rory Nolan

Data scientist. R developer. Image analyst. Statistician.

Recent Posts

The rate of testing for COVID-19 is variable everywhere. As such, using the number of confirmed cases over time is not a reliable method for tracking the spread of the disease. We should be using deaths per capita First of all, we should be using per capita statistics. For example, 100,000 cases in China vs 100,000 cases in Ireland would be very different things, because of what they imply for concentration of cases.

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The problem I have a package called strex which is for string manipulation. In this package, I want to take advantage of the regex capabilities of C++11. The reason for this is that in strex, I find myself needing to do a calculation like x <- list(c("1,000", "2,000,000"), c("1", "50", "3,455")) lapply(x, function(x) as.numeric(stringr::str_replace_all(x, ",", ""))) #> [[1]] #> [1] 1e+03 2e+06 #> #> [[2]] #> [1] 1 50 3455 A lapply like this can be done faster in C++11, so I’d like to have that speedup in my package.

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In native R, the user sets the seed for random number generation (RNG) with set.seed(). Random number generators exist in C and C++ too; these need their own seeds, which are not obviously settable by set.seed(). Good news! It can be done. pacman::p_load(inline, purrr) rbernoulli Base R (or technically the stats package) provides no rbernoulli(). It’s a pretty gaping hole in the pantheon of rbeta(), rbinom(), rcauchy(), rchisq(), rexp(), rf(), rgamma(), etc.

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Selected Publications

General purpose TIFF file I/O for R users. This is currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from ‘ImageJ’ and write TIFF files than can be correctly read by ‘ImageJ’ https://imagej.nih.gov/ij/. Also supports text image I/O.
In JOSS, 2018

The possibility to detect and quantify protein-protein interactions with good spatial and temporal resolutions in live cells is crucial in biology. Number and brightness is a powerful approach to detect both protein aggregation/desegregation dynamics and stoichiometry in live cells. Importantly, this technique can be applied in commercial set ups: both camera based and laser scanning microscopes. It provides pixel-by-pixel information on protein oligomeric states. If performed with two colours, the technique can retrieve the stoichiometry of the reaction under study. In this review, we discuss the strengths and weaknesses of the technique, stressing which are the correct acquisition parameters for a given microscope, the main challenges in analysis, and the limitations of the technique.
In Methods, 2017

An R package for performing number and brightness image analysis, with the implementation of a novel automatic detrending algorithm.
In Bioinformatics, 2017

Recent Publications

. Detecting Protein Aggregation and Interactions in Live Cells: a Guide to Number and Brightness. In Methods, 2017.

Article PubMed

. nandb — Number and Brightness in R with a Novel Automatic Detrending Algorithm. In Bioinformatics, 2017.

Article PubMed CRAN GitHub

. exampletestr - An Easy Start to Unit Testing R Packages. In Wellcome Open Res., 2017.

PDF Article CRAN GitHub

. autothresholdr: An R Port of the 'ImageJ' Plugin 'Auto Threshold'. On CRAN, 2016.

CRAN GitHub

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