单细胞转录组数据分析课件||5. scRNA-seq data
本文主要介绍了单细胞数据分析中的多样本分析，这对于单细胞数据挖掘是一个挑战，以后也会变成一种常态。
- 整合分析与合并分析的区别
- 整合过程是去除批次效应的过程吗？
- 也许我们要重新理解批次效应
- 介绍了MNN 、 CCA+、LIGER、Harmony、Conos、Scanorama、scMerge几款批次校正工具（整合工具）
- 主要介绍了seurat的方法。
kBET
A test metric for assessing single-cell RNA-seq batch correction
Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; https://github.com/theislab/kBET) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas.
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
This lecture by Ahmed Mahfouz (Leiden Computational Biology Center, LUMC, Netherlands) is part of the course "Single cell RNA-seq data analysis with R" (27.-29.5.2019). Please see https://www.csc.fi/web/training/-/scr... for the full course description and all the materials.
