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APBC 2020

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http://epigenomics.snu.ac.kr/APBC2020/program.html

Asian Pacific Bioinformatics Conference (APBC) 2020

Systems metabolic engineering for sustainability and good health by Sang Yup Lee (KAIST)

Using the systems metabolic engineering, we can produce some chemical substance such as fuel or plastic from E.coli (bacteria). Furthermore, the produced substance could be recycled into fertilizer or organic matter.

AI and Network Bioinformatics for Multi-omics Data Analysis by Sun Kim and Inuk Jung

AI-based drug discovery and development by Doheon Lee

Lead discovery and optimization —> AI
synthesized drug v.s. natural product (win)

Single-cell RNA sequencing

Robust clustering method against the droplet effect

1.
Data normalization
2.
Feature selection (highly-variable genes)
3.
Dimensionality reduction (PCA: linearly; UMAP: non-linearly)
4.
KNN network —> PCA + UMAP

I-Impute (imputation for dropout event)

Two main tools: (1) SAVER (estimating the expression levels using Bayesian) and (2) scImpute
X —> SAVER —> C-Impute (repeat) —> Output

Cell-type classification with Ensemble

challenges in cell-type annotation in scRNA-seq: (1) cumbersome, time-consuming, (2) high-dimensionality, high sparsity, technical effect

Time-course bimodality of scRNA-seq data

Multi-omics integration using autoencoder

Autoencoder —> Lasso —> Kmeans —> Model comparison with coxph
More Bottleneck layer nodes can incerase the performance

BIOINFO 2020

Gene network reconstruction using single cell transcriptomic data reveals condition specific regulators by Kyoung Jae Won

scRNAseq for Heterogeneity

Single cell clustering

"CellBIC" Top-down clustering, but dimension reduction이 필요하구나.
Random Projection (Johnson-Linderberg 1984)
"SHARP" (Random Projection based scRNA seq clustering) Clustering the splitted data set with the genes smaller than original gene size.

scRNAseq for Gene Regulation

pseudo-time : X's expression is regulated by Y's expression
Transfer entropy :
"TENET"
github.com

scRNAseq for Cell Interaction

The Genome Korea Project: Mapping the Korean Genome Diversity by Semin Lee

울산지놈프로젝트 관련 연구결과

dddd

mutation —> Tumor —> Mutated protein —> mutated peptide

Public single cell data

Human CD8+ T cells from public single cell data

Multi-modal profiling methods at single-cell resolution

Genetics + Transcriptomics + Spatial context + Epigenetics + Proteomics + Perturbations + TCR + BCR
DNA-seq + RNA-seq = SIDR-seq, G&T-seq, DR-seq
DNA-seq + RNA-seq + Methyl-seq = Trio-seq
RNA-seq + ATAC-seq = sciCAR
RNA-seq + TCR/BCR = (10X) 5' GEX with Immune Cell profiling
Epitope-profiling + RNA-seq = CITE-seq
Genotyping + RNA-seq = GoT
Genetic screening with CRISPR + RNA-seq = Perturb-seq
...

Basic single-cell analysis workflow

Pre-processing pipeline: 10X CellRanger

Useful resources to identify cell type markers when we annotate the clustered cell types

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