Reading Notes-Rongting
Preface
About the author
Research topics
About the book
1
Introduction
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
5
StatMethods
5.1
HMM based methods
5.2
EM based methods
5.3
VB based methods
5.4
GLM
6
PhD Research topic based
6.1
CNV calling
6.1.1
breaking point detection
6.1.2
Related research
6.1.3
Smoothing strategies in RDR
6.1.4
PUBMON
6.2
Clonal Tree
6.3
Deconvolution
6.3.1
Deconvolution of bulk tissue and spatial transcriptomic data
6.4
Omics integration
6.5
Spatial transcriptomics
6.6
Spatial transcriptomics Methods
6.7
Spatial Datasets Collection
6.8
3D biology
6.9
Sequencing data relative Knowledge
6.10
Image data relative Knowledge
7
Proteomics
7.1
PTM cross-talk
7.2
PTM Cluster
7.3
PTM and cancer
7.4
Protein structure
7.4.1
Protein Folding Prediction Background
8
Epigenomics
9
Pubmon
9.1
2020-2021 PhD year 1
9.2
2021-2022 PhD year 2
9.2.1
Resources
9.2.2
PPI topic
9.3
2023-2024 PhD year 4
10
Method of the Year
10.1
Method of the Year 2022:
10.2
Method of the Year 2021: Protein structure prediction
10.3
Method of the Year 2020: spatially resolved transcriptomics
10.4
Method of the Year 2019: Single-cell multimodal omics
References
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ReadingNotes: Have you read today’s papers?
Chapter 5
StatMethods
5.1
HMM based methods
5.2
EM based methods
5.3
VB based methods
5.4
GLM
statsmodel GLM
statsmodels.discrete.discrete_model.NegativeBinomial