One essential step in the preprocessing of single-cell RNA-Seq data (scRNA-seq) is batch effect correction. However, much confusion remained around this step. In this article, let’s address the most frequently asked questions about handling batch effect in single-cell RNA-Seq. . What is Batch Effect in Single-cell RNA-Seq? Batch effect happens […]
Clustering is The Microscope For scRNA-Seq data In previous posts, we have walked you through important steps in analyzing your single-cell RNA sequencing (scRNA-Seq) data, including data visualization and normalization. This time, let’s explore the next logical step in the data analysis pipeline: clustering scRNA-Seq data. What is scRNA-Seq clustering? […]
Principal component analysis (PCA) is one of the most popular dimension reduction methods. It works by converting the information in a complex dataset into principal components (PC), a few of which can describe most of the variation in the original dataset. The data can then be plotted with just the […]
“How to extract the biology behind this huge matrix of thousands of rows and columns?” Large numbers of data points hold valuable biological stories yet are tricky to graph and analyze. Imagine you had the data for 1000 features and observations, your mind would be full of questions, wondering how […]
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 […]
