Single cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. Unlike bulk RNA-sequencing profiling where sequencing libraries are generated from thousands of cells, scRNA-sequencing technologies isolate single cells and generate cell-specific sequencing libraries to mark RNA content with a cell-specific molecular barcode. Analysis of this data generates gene expression estimates at the single cell level. SCT enables the measurement of the transcriptomic information for a range of thousands up to millions of single cells in a single experiment.
However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Many of the methods have been extensively tested and standardized.
In this video we talk about single cell sequencing, look at RNA sequencing pipeline, and discover how to utilize scRNA-seq methods such as batch effect elimination (through anchoring), manifold learning, as well as annotation of cell clusters by their biological cell type.
Here is what we will cover in this video:
00:00 Intro
00:42 Single cell sequencing technology
02:44 Challenges of single cell data
04:01 Single cell analysis pipeline
05:50 Batch effect
07:22 Manifold learning
08:20 Annotation of cell clusters by their biological type
11:35 Result of analysis
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scRNA-seq: Single Cell RNA Sequencing Data Analysis
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