Publications by Shirin's playgRound
exprAnalysis package
I created the R package exprAnalysis designed to streamline my RNA-seq data analysis pipeline. Below you find the vignette for installation and usage of the package. This package combines functions from various packages used to analyze and visualize expression data from NGS or expression chips. It supports normalized input as e.g. from Cufflink...
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DESeq2 Course Work
The following workflow has been designed as teaching instructions for an introductory course to RNA-seq data analysis with DESeq2. The course is designed for PhD students and will be given at the University of Münster from 10th to 21st of October 2016. For questions or other comments, please contact me. Go to exprAnalysis or this post for insta...
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USA/ Canada Roadtrip 2016
Mapping GPS data from our USA/ Canada Roadtrip This September we went on a roadtrip to the US and Canada. Of course, we had our trusty GPS to guide us along the way – the data from which I downloaded and used to play around with. (If you want to see a few photos from the trip and don’t care about the rest, skip to the bottom…) Loading the...
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Exploring the human genome (Part 1) – Gene Annotations
When working with any type of genome data, we often look for annotation information about genes, e.g. what’s the gene’s full name, what’s its abbreviated symbol, what ID it has in other databases, what functions have been described, how many and which transcripts exist, etc. However, when looking for this information we (luckily) find a num...
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Exploring the human genome (Part 2) – Transcripts
How many transcripts and proteins do genes have? In Exploring the human genome (Part 1) – Gene Annotations I examined Ensembl, Entrez and HGNC gene annotations with AnnotationDbi via three R packages: org.Hs.eg.db, EnsDb.Hsapiens.v79 and TxDb.Hsapiens.UCSC.hg38.knownGene. Now, I want to know how many transcripts there are for genes in these dat...
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Is ‘Yeah’ Josh and Chuck’s favorite word?
Text mining and sentiment analysis of a Stuff You Should Know Podcast Stuff You Should Know (or SYSK) is one of the many great podcasts from How Stuff Works. The two SYSK hosts Josh and Chuck have taught me so many fascinating things over the years, and today I want to use one of their podcasts to learn a little bit about text analysis in R. Ini...
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Creating a Gilmore Girls character network with R
With the impending (and by many – including me – much awaited) Gilmore Girls Revival, I wanted to take a somewhat different look at our beloved characters from Stars Hollow. I had recently read a few cool examples of how to create co-occurrence networks and wanted to combine this with an analysis similar to what David Robinson did for Love Ac...
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Analysing the Gilmore Girls’ coffee addiction with R
Last week’s post showed how to create a Gilmore Girls character network. In this week’s short post, I want to explore the Gilmore Girls’ famous coffee addiction by analysing the same episode transcripts that were also used last week. I am also showcasing how to use the recently updated ggplot2 2.2.0. The transcripts were prepared as describ...
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Can we predict flu deaths with Machine Learning and R?
Among the many R packages, there is the outbreaks package. It contains datasets on epidemics, on of which is from the 2013 outbreak of influenza A H7N9 in China, as analysed by Kucharski et al. (2014): A. Kucharski, H. Mills, A. Pinsent, C. Fraser, M. Van Kerkhove, C. A. Donnelly, and S. Riley. 2014. Distinguishing between reservoir exposure and...
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Extreme Gradient Boosting and Preprocessing in Machine Learning – Addendum to predicting flu outcome with R
In last week’s post I explored whether machine learning models can be applied to predict flu deaths from the 2013 outbreak of influenza A H7N9 in China. There, I compared random forests, elastic-net regularized generalized linear models, k-nearest neighbors, penalized discriminant analysis, stabilized linear discriminant analysis, nearest shrun...
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