Computational biology and genomics
I haven’t been blogging as much as I’d like to due to other commitments but I wanted to write a post before 2018 ends. This post is on plotting Google Trends results with R. If you’ve never heard of or used Google Trends, it’s fun! You can see how certain keywords have trended over the…
Cell Ranger is a set of analysis pipelines that process Chromium single cell 3′ RNA-seq data. The pipelines process raw sequencing output, performs read alignment, generate gene-cell matrices, and can perform downstream analyses such as clustering and gene expression analysis. Cell Ranger includes four pipelines: cellranger mkfastq cellranger count cellranger aggr cellranger reanalyze You can…
The Chromium Single Cell 3′ Solution is a commercial platform developed by 10x Genomics for preparing single cell cDNA libraries for performing single cell RNA-seq. In addition, 10x Genomics have developed an entire software suite called Cell Ranger that can process the raw BCL files produced by an Illumina sequencer and output a final gene-barcode…
Interactive plots, as the name suggests, are plots that users can interact with. In my last post, I mentioned that for interactive heatmaps I use the d3heatmap package. To get started with this post, I’ll create the same heatmap as my last post but this time using the d3heatmap package.
For a while, heatmap.2() from the gplots package was my function of choice for creating heatmaps in R. Then I discovered the superheat package, which attracted me because of the side plots. However, shortly afterwards I discovered pheatmap and I have been mainly using it for all my heatmaps (except when I need to interact…
A quick guide to organizing computational biology projects has been published over 8 years ago but the main messages are still very relevant (perhaps even more so nowadays given the exponential increase in biological data). In a nutshell, computational biology projects need to be organised so that we can share and reproduce our work in…
Updated 2018 March 23rd for R-3.4.4 I use a lot of shortcuts provided by GNU Readline. I recently compiled R without Readline support and it was almost unusable! This was because I ran into the error: configure: error: –with-readline=yes (default) and headers/libs are not available To circumvent this I compiled R by running: ./configure –with-readline=no…
I was going to write a post on using the Seurat alignment method as a batch correction tool but as it turned out the two datasets that I chose didn’t seem to have strong batch effects! I heard about the alignment method sometime last year but was motivated to try it out after listening to…
From the original paper describing the Gene Set Enrichment Analysis: The goal of GSEA is to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L, in which case the gene set is correlated with the phenotypic class distinction.
The Variant Effect Predictor (VEP) tool can be used for annotating variants with respect to custom annotation sources. This is useful if gene models of interest are not represented in the Ensembl or RefSeq databases. To get started, first install VEP since it takes some time.