Single cell and spatial omics: A short introduction to the core concepts of scRNA-seq and more
A short introduction to the core concepts on single-cell omics data and spatial omics data. I will start by introducing how these types of data relate to and differ from normal omics data and concisely explain the typical experimental workflows used to produce such data. I will then talk about some of the computational aspects of analyzing single-cell data, including pooling of cells, clustering to identify cell types, and the concept of pseudo-time. Finally, I will briefly talk about how analysis of spatial omics data relate to analysis of images.
0:00 Introduction: reminder of omics is and introduction to single-cell and spatial data
0:42 Single-cell workflow: dissociated cell culture, isolation of single cells, omics on single cells, UMIs, and multiplexing
2:24 Spatial workflow: in situ capture, spatial indexing, spatial RNA-seq, single-cell resolution, and laser capture microdissection
3:48 Computational analysis: single cell vs. bulk data, cells vs. samples, pooling, cell-type clustering, pseudo-time reconstruction, and analysis of spatial patterns
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Post Tags :
- bioinformatics
- Cell
- cell isolation
- cell sorting
- cell-type clustering
- Concepts
- CORE
- dissociated cell culture
- DNA barcodes
- FACS
- image analysis
- in situ capture
- Introduction
- Macs
- microdroplets
- microfluidics
- multiplexed omics
- next-generation sequencing
- omics
- pseudo-time
- pseudotime
- scRNA-seq
- scRNAseq
- short
- single
- single-cell omics
- single-cell proteomics
- single-cell RNA-seq
- single-cell transcriptomics
- Slide-seq
- Spatial
- spatial omics
- spatial proteomics
- spatial transcriptomics
- trajectory inference
- UMIs
- unique molecular identifiers