Posts

Have you ever wanted to take a peek at your knit markdown file without actually hitting knit… or wish you could make some final tweaks to your file in it’s visual form?

During my internship this past summer, I spent time surveying the many existing trajectory inference (TI) methods. TI methods leverage data from thousands of single cells to reconstruct lineage paths and infer the order of cells along developmental trajectories.

As promised, this is part 2 of the analyzing single-cell data series. In this post I intend to discuss Seurat: an R toolkit designed for the exploration of single-cell expression data.

This past summer, I was immersed in the world of single-cell genomics - a completely new field to me. With this came learning not only how data were experimentally collected, but also how to computationally process these data.

In this post I will talk briefly about the advantages of and tools for building project-oriented workflows. All this with the hope that I can convince you to never write another absolute file path again.

‘R’ you ready for some Python?? Over the last few weeks I’ve been immersed in an internship where I’ve learned a TON about single cell genomics, trajectory inference methods, and Python.

I’ve been looking for ways to apply the R tools I use to fun data outside my research, so when I happened to stumble across the Pokémon dataset I thought… what better way to dig into some Pokémon stats than with tidyverse!

Model selection is the process of selecting one statistical model from a set of many candidate models. It seems relatively straightforward in principal but in practice (especially with noisy experimental data) it can be challenging.

Lately, Machine Learning has become such a buzzword in the world of data science. As a biologist, it’s always seemed so daunting… come to find out, it’s really not all that scary!

If you’ve ever handled data you’ve probably faced the ever daunting first step of cleaning and tidying said data. As a matter of fact, it’s been mentioned that 80% of data analysis is spent solely on the process of cleaning and preparing the data [1].