Literatures

My literature reading list

Mainly about single cell clustering

Single cell clustering

Bi clustering

• [HOPACH] New algorithm for hybrid hierarchical clustering with visualization and the bootstrap

Clustering

• [CIDR] Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

• [Corr] Single Cell Clustering Based on Cell-Pair Differentiability Correlation and Variance Analysis

• [DendroSplit] An interpretable framework for clustering single-cell RNA-Seq datasets

• [Mpath] Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development

• [RaceID] Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Data

• [SCANPY] Large-scale single-cell gene expression data analysis

• [ScClassify] Hierarchical classification of cells

• [ScDMFK] Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm

• [ScRCMF] Identification of cell subpopulations and transition states from single cell transcriptomes

• [SIMLR] A tool for large-scale genomic analyses by multi-kernel learning

• [SIMLR] Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning

• [SINCERA] A Pipeline for Single-Cell RNA-Seq Profiling Analysis

• [SinNLRR] A robust subspace clustering method for cell type detection by non-negative and low-rank representation

• [SNN-Cliq] Identification of cell types from single-cell transcriptomes using a novel clustering method

• [SurvExpress] An Online Biomarker Validation Tool andDatabase for Cancer Gene Expression Data UsingSurvival Analysis

• A Hybrid Clustering Algorithm for Identifying Cell

• A kernel non-negative matrix factorization framework for single cell clustering

• A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data

• Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

• Spectral clustering based on learning similarity matrix

Deep learning

• [AutoImpute] Autoencoder based imputation of single-cell RNA-seq data

• [DCA] Single-cell RNA-seq denoising using a deep count autoencoder

• [DeepImpute] An accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

• [DESC] Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

• [VASC] Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

• Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

• Clustering single-cell RNA-seq data with a model-based deep learning approach

• Enhancing the prediction of disease-gene associations with multimodal deep learning

• Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

• Using neural networks for reducing the dimensions of single-cell RNA-Seq data

Dimension reduction

• [PcaReduce] Hierarchical clustering of single cell transcriptional profiles

• [SC3] Consensus clustering of single-cell rna-seq data

• [t-SNE] Visualizing Data using t-SNE

• [Review] Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Others

• A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

• Cell-specific network constructed by single-cell RNA sequencing data

• Identifying disease genes by integrating multiple data sources

• Integrating network topology, gene expression data and GO annotation information for protein complex prediction

• Limma powers differential expression analyses for RNA-sequencing and microarray studies

• Prediction of Human Disease-Related Gene Clusters by Clustering Analysis

• Seq-Well：portable, low-cost RNA sequencing of single cells at high throughput

• The emergent landscape of the mouse gut endoderm at single-cell resolution

• The single-cell transcriptional landscape of mammalian organogenesis

• Using diversity in cluster ensembles

Review

• Challenges in unsupervised clustering of single-cell RNA-seq data

• Clustering and classification methods for single-cell RNA-sequencing data

• Comparing clusterings – an overview

• Current best practices in single-cell RNA-seq analysis-a tutorial

• Eleven grand challenges in single-cell data science

• Impact of similarity metrics on single-cell RNA-seq data clustering

• Machine learning and statistical methods for clustering single-cell RNA-sequencing data

• Normalizing single-cell rna sequencing data - challenges and opportunities

• Review of Single-cell RNA-seq Data Clustering for Cell TypeIdentification and Characterization

• Single-cell RNA-seq clustering：datasets,models,and algorithms

Computer Vision

• [AlexNet] ImageNet Classification with Deep Convolutional Neural Networks

• [FaceNet] A Unified Embedding for Face Recognition and Clustering

• [GoogLeNet] Going Deeper with Convolutions

• [LeNet] Gradient-Based Learning Applied to Document Recognition

• [ResNet] Deep Residual Learning for Image Recognition

• [SSD] Single Shot MultiBox Detector

• [VGG] Very Deep Convolutional Networks for Large-Scale Image Recognition

• Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine

Deep Learning and NN

• A Comprehensive Survey on Graph Neural Networks

• Deconvolutional Networks

• Deep Clustering for UnsupervisedLearning of Visual Features

• Deep Learning

• Learning a Similarity Metric Discriminatively, with Application to Face Verification

• T2F-LSTM Method for Long-term Traffic

Machine Learning

• [Structural Regularized Support Vector Machine] A Framework for Structural LargeMargin Classifier

• [v-TSVM] Am-twin support vector machine (m-TSVM) classifier and its geometric algorithms

• A Tutorial on Spectral Clustering

• SimpleMKL

• Structural support vector machine

• Structural twin support vector machine for classification