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· Sahir Bhatnagar

Sahir Bhatnagar. Powered by jekyll. In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data. In this post I simulate some gene expression data and visualise it using the. Function from the pheatmap. You will also need the. Function from the MASS. Library to simulate from a multivariate normal distribution, and the. Function from the RColorBrewer. Speed dist # Min....

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· Sahir Bhatnagar | sahirbhatnagar.com Reviews

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Sahir Bhatnagar. Powered by jekyll. In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data. In this post I simulate some gene expression data and visualise it using the. Function from the pheatmap. You will also need the. Function from the MASS. Library to simulate from a multivariate normal distribution, and the. Function from the RColorBrewer. Speed dist # Min....

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1 archive

2 publications

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5 software

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7 presentations

8 tutorials

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· Sahir Bhatnagar | sahirbhatnagar.com Reviews

https://sahirbhatnagar.com

Sahir Bhatnagar. Powered by jekyll. In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data. In this post I simulate some gene expression data and visualise it using the. Function from the pheatmap. You will also need the. Function from the MASS. Library to simulate from a multivariate normal distribution, and the. Function from the RColorBrewer. Speed dist # Min....

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sahirbhatnagar.com

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Publications ·

http://sahirbhatnagar.com/pubs

Sahir Bhatnagar. Powered by jekyll. Klein Oros K, Oualkacha K, Lafond M, Bhatnagar SR. Tonin PN, Greenwood CMT (2016). Gene coexpression analyses differentiate networks associated with diverse cancers harbouring TP53 missense or null mutations. Submitted (2016/04). Wang Y, Murphy O, Turgeon M, Wang ZY, Bhatnagar S. Schulz J, and Moodie EEM. The perils of Quasi-likelihood Information Criteria. Stat Feb 1;4(1):246-54. Greenwood CMT, Labbe A. BMC Genetics. (Accepted 2015/01). Sun J, Bhatnagar S.

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Linux: Thunderbird and McGill mail ·

http://sahirbhatnagar.com/thunderbird

Sahir Bhatnagar. Powered by jekyll. Linux: Thunderbird and McGill mail. The Microsoft Outlook web app is terrible for Linux. Simple tasks such as saving addresses or deleting messages by filter is painfully difficult, especially when you have had experience with good email clients on Windows, or even Mac OS. I always knew Thunderbird existed for Linux, but didn’t know my university’s specific settings. If you have any questions or comments, please post them below. Heatmaps in R 10 Jun 2015.

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Software ·

http://sahirbhatnagar.com/software

Sahir Bhatnagar. Powered by jekyll. Fragment lengths or molecular weights from pairs of lanes are compared, and a number of matching bands are calculated using the Align-and-Count Method.

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Contrasts in R ·

http://sahirbhatnagar.com/contrasts

Sahir Bhatnagar. Powered by jekyll. In this post I discuss how to create custom contrasts for factor variables in. First lets create some simulated data. Create the data, and factor Disease status:. We want the following contrasts:. Control versus all 4 diseases combined. RA versus the combination of (SLE, Scleroderma, Myositis), leaving out the Controls. Let be the indicators for Myositis, RA, Scleroderma and SLE, respectively. The standard linear model. Since we want only two contrasts, we want. Again ...

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Tutorials ·

http://sahirbhatnagar.com/tutorial

Sahir Bhatnagar. Powered by jekyll. Reproducible Research: An introduction to. Atelier sur le logiciel R. GitHub for Data Scientists without the terminal. Interactive Heat Maps for R. Power curves in R. Manhattanly: R package for interactive manhattan plots.

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· Sahir Bhatnagar

Sahir Bhatnagar. Powered by jekyll. In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data. In this post I simulate some gene expression data and visualise it using the. Function from the pheatmap. You will also need the. Function from the MASS. Library to simulate from a multivariate normal distribution, and the. Function from the RColorBrewer. Speed dist # Min....

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