Reading Notes-Rongting
Preface
About the author
About the book
1
Introduction
1.1
Rongting Reading Notes
1.2
About This Notebook
1.3
Topics Covered
1.4
How to Use This Notebook
2
SingleCell
2.1
Technology
2.1.1
single cell
2.1.2
spatial single cell
2.2
Review
2.2.1
Review-2021-06
2.2.2
Review-2021-07
2.2.3
Review-2021-10
2.3
Data processing
2.4
Clustering methods
2.5
Benchmarking
3
Technology Related
3.1
scRNA-seq modelling
3.1.1
NB models for scRNA-seq
3.1.2
Batch-effect correction methods for scRNA-seq
3.2
Hi-Seq
3.3
languages and compilers
3.4
singlecell analysis tools
3.5
singlecell analysis sources
4
Spatial Technology
4.1
Sequencing based ST
4.2
Image based ST
5
StatMethods
5.1
HMM based methods
5.2
EM based methods
5.3
VB based methods
5.4
GLM
6
Biology Topics
6.1
Cancer (General)
6.2
Gyn Malignancies
7
PhD Research topic based
7.1
CNV calling
7.1.1
breaking point detection
7.1.2
Related research
7.1.3
Smoothing strategies in RDR
7.1.4
PUBMON
7.2
Clonal Tree
7.3
Deconvolution
7.3.1
Deconvolution of bulk tissue and spatial transcriptomic data
7.4
Omics integration
7.5
Spatial transcriptomics
7.5.1
An overview of spatial omics methods
7.6
Spatial transcriptomics Methods
7.7
Spatial Datasets Collection
7.8
3D biology
7.9
Sequencing data relative Knowledge
8
Sequencing
9
Image
9.1
Image data relative Knowledge
10
Proteomics
10.1
PTM cross-talk
10.2
PTM Cluster
10.3
PTM and cancer
10.4
Protein structure
10.4.1
Protein Folding Prediction Background
11
Epigenomics
12
Pubmon
12.1
2020-2021 PhD year 1
12.2
2021-2022 PhD year 2
12.2.1
Resources
12.2.2
PPI topic
12.3
2023-2024 PhD year 4
12.4
2024-2025 Pre Postdoc
12.5
2025-2026 Postdoc Yr1
13
Method of the Year
13.1
Year 2024: Spatial Proteomics
13.2
Year 2023: Methods for modeling development
13.3
Year 2022: Long-read sequencing
13.4
Year 2021: Protein structure prediction
13.5
Year 2020: Spatially resolved transcriptomics
13.6
Year 2019: Single-cell multimodal omics
References
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Chapter 11
Epigenomics