Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis

Practical Compass of Single-Cell RNA-Seq Analysis

PURPOSE OF REVIEW

This review paper provides step-by-step instructions on the fundamental process, from handling fastq datasets to illustrating plots and drawing trajectories.

RECENT FINDINGS

The number of studies using single-cell RNA-seq (scRNA-seq) is increasing. scRNA-seq revealed the heterogeneity or diversity of the cellular populations. scRNA-seq also provides insight into the interactions between different cell types. User-friendly scRNA-seq packages for ligand-receptor interactions and trajectory analyses are available. In skeletal biology, osteoclast differentiation, fracture healing, ectopic ossification, human bone development, and the bone marrow niche have been examined using scRNA-seq. scRNA-seq data analysis tools are still being developed, even at the fundamental step of dataset integration. However, updating the latest information is difficult for many researchers. Investigators and reviewers must share their knowledge of in silico scRNA-seq for better biological interpretation. This review article aims to provide a useful guide for complex analytical processes in single-cell RNA-seq data analysis.

Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method

Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network ( [Formula: see text]OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. [Formula: see text]OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.

Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification

Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results. However, most human physical activity identification models can only be trained based on labeled data, and it is difficult to obtain enough labeled data, which leads to weak generalization ability of the model. A Pruning Growing SOM model is proposed in this paper to address the limitations of small-scale labeled dataset, which is unsupervised in the training stage, and then only a small amount of labeled data is used for labeling neurons to reduce dependency on labeled data. In training stage, the inactive neurons in network can be deleted by pruning mechanism, which makes the model more consistent with the data distribution and improves the identification accuracy even on unbalanced dataset, especially for the action categories with poor identification effect. In addition, the pruning mechanism can also speed up the inference of the model by controlling its scale.

Clustering Ensemble Model Based on Self-Organizing Map Network

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter

In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results.