Arvutiteaduse instituut
  1. Kursused
  2. 2020/21 kevad
  3. Bioinformaatika seminar (MTAT.03.242)
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Bioinformaatika seminar 2020/21 kevad

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Overview of single-cell RNA-seq technologies

  • Power analysis of single-cell RNA-sequencing experiments
  • Exponential scaling of single-cell RNA-seq in the past decade
  • A curated database reveals trends in single-cell transcriptomics
  • Bonus: Comparative Analysis of Single-Cell RNA Sequencing Methods

Pre-processing, quantification and demultiplexing

  • Modular and efficient pre-processing of single-cell RNA-seq
  • soupX, CellBender
  • souporcell: Robust clustering of single cell RNAseq by genotype and ambient RNA inference without reference genotypes
  • Bonus papers: soupX, CellBender

Properties of the data

  • Missing data and technical variability in single-cell RNA-sequencing experiments
  • Droplet scRNA-seq is not zero-inflated
  • Zeros in scRNA-seq data: good or bad? How to embrace or tackle zeros in scRNA-seq data analysis?

Best practice

  • Current best practices in single-cell RNA-seq analysis: a tutorial
  • Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data

Batch correction

  • A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples
  • Benchmarking atlas-level data integration in single-cell genomics
  • A benchmark of batch-effect correction methods for single-cell RNA sequencing data

Cell type annotations (machine learning)

  • MARS: discovering novel cell types across heterogeneous single-cell experiments
  • Query to reference single-cell integration with transfer learning

eQTL analysis

  • Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
  • Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

RNA velocity

  • RNA velocity of single cells
  • Preprocessing choices affect RNA velocity results for droplet scRNA-seq data
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