Data analysis made easy. For biologists, especially.
Single-cell sequencing technologies have brought up an unprecedented level of resolution to omics studies with extremely detailed views into individual cells’ expression patterns. With the emergence of drop-based sequencing methods, the resolution now even comes with scale and efficiency. In a single experiment, scientists can now profile hundreds of thousands […]
Tiny as they are, retinal cells are extremely abundant and complex. The total population of about 100 million rod photoreceptors is among the most numerous neuron populations in the human body, second to cerebellar granule cells (Masland et al., 2012). Forming a sheet of tissue that is only ~200 μm […]
1. An atlas of acute myeloid leukemia at single-cell resolution Adapted from Galan et. al., 2019 Acute myeloid leukemia (AML) is a cancer characterized by the accumulation of white blood cells in the bone marrow and blood. The disease has been notoriously challenging to study because of its extreme intratumoral […]
Ever questioned why the immune system does not attack our own double-stranded RNA? Speaking of this, we should not ignore the role of ADAR, the gene that encodes Double-stranded RNA-specific adenosine deaminase enzyme, responsible for converting adenosines to inosines (A -> I editing) in double-stranded RNA (dsRNA) substrates. ADAR was […]
Single-cell RNA-seq technologies have opened up a completely new era for transcriptomic studies. For the first time ever, scientists can look at individual transcriptomic profiles of millions of cells, and better understand how each cell functions in a tissue. Yet science is confronting bigger challenges analyzing these massive amounts of […]
Today we finished the first version of Hera-T, a new single-cell RNA-seq quantification algorithm. We developed Hera-T by improving challenging alignment errors that Cell Ranger has. As a result, Hera-T is more accurate than Cell Ranger. Hera-T is more than 10 times faster than Cell Ranger, while consuming just a small amount of […]
Heatmaps. We see them all over the scientific journals — the shading matrices that convey meaningful stories. Wanna interpret gene expressions? Draw a heatmap. Gotta examine the correlation among variables? Get it done with a heatmap. It seems that the shading has breathed such life into those cheerless matrices of […]
Principal component analysis (PCA) has been gaining popularity as a tool to bring out strong patterns from complex biological datasets. We have answered the question “What is a PCA?” in this jargon-free blog post — check it out for a simple explanation of how PCA works. In a nutshell, PCA capture […]
As we are entering the era of Big Data, everyone and their moms seem to be talking about PCA. All the papers you read mention PCA (with lots of jargon, of course). Half of the seminars you’ve been to this month touch on PCA. Your boss/collaborators suggest trying PCA on your data. “What […]
We showed a quick and easy way to compare box plots in previous post. Let’s dig deeper into what information you can use to compare two box plots. Overlapping boxes and medians It gets tricky when the boxes overlap and their median lines are inside the overlap range. As always, math comes to […]
Box plots, a.k.a. box-and-whiskers plots, are an excellent way to visualize differences among groups. They manage to carry a lot of statistical details — medians, ranges, outliers — without looking intimidating. But box plots are not always intuitive to read. How do you compare two box plots? The key information […]
1. Violin graph is like box plot, but better Box-and-whisker plots are great. They show medians, ranges and variabilities effectively. They allow comparing groups of different sizes. They are super simple to create and read, so naturally, they are all over the place. But box plots can be misleading. They are not […]
