Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions. The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension, while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension. Motivated by this finding, a new strategy, called QRS, is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not. Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. We perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

The rapid development of single-cell transcriptome sequencing technology has provided us with a cell-level perspective to study biological problems. Identification of cell types is one of the fundamental issues in computational analysis of single-cell data. Due to the large amount of noise from single-cell technologies and high dimension of expression profiles, traditional clustering methods are not so applicable to solve it. To address the problem, we have designed an adaptive sparse subspace clustering method, called AdaptiveSSC, to identify cell types. AdaptiveSSC is based on the assumption that the expression of cells with the same type lies in the same subspace; one cell can be expressed as a linear combination of the other cells. Moreover, it uses a data-driven adaptive sparse constraint to construct the similarity matrix. The comparison results of 10 scRNA-seq datasets show that AdaptiveSSC outperforms original subspace clustering and other state-of-art methods in most cases. Moreover, the learned similarity matrix can also be integrated with a modified t-SNE to obtain an improved visualization result.

In order to study the horizontal bearing characteristics of large-diameter rock-socketed rigid pile and flexible pile, two lateral loading tests in which the pile lengths are 5.2 m and 11.07 m were carried out. Unidirectional multi-cyclic loading was applied to the piles during the tests, with the maximum load reaching 3500 kN. The measured results are compared with the calculated results of Zhang’s method, m-method and the rigid pile method in the design codes. It is indicated that if the characteristic values of the horizontal bearing capacity of the large-diameter rock-socketed rigid pile and flexible pile are determined by the same horizontal displacement of the pile head, some risk will be brought to the design of the rigid pile. Compared with the rigid pile method, the m-method is more suitable for calculating the rotation angle of the pile head. In terms of the maximum bending moment of the large-diameter rock-socketed flexible pile under the critical load, the calculated result of Zhang’s method is less than the measured result, while the calculated result of the m-method is the largest. However, for the rigid pile, both Zhang’s method and m-method underestimate the maximum bending moment of the pile body. In summary, when a large-diameter rock-socketed pile is designed, reasonable calculation method and failure discrimination standard should be chosen according to the actual conditions.

The foundation pit area of Kunming International Comprehensive Transportation Hub is 56,000 m², and the excavation depth ranges from 18m to 25m. Because the surrounding environment is very complex, the foundation pit is supported by underground continuous wall and three layers of internal support system with seven rings. The force of this internal support system is coupled in-tegrally, and the number of rings is the most in the world at present. In this work, a finite element model considering the interaction between soil and retaining structure is established. Hardening Soil model with small strain stiffness is used to simulate and analyze the whole excavation process of the foundation pit. Considering the ultra-large plane size of the foundation pit, we cannot ignore the temperature effect, so that the deformation of the underground continuous wall and the force of the internal support system under seasonal temperature variation are investigated. By comparing numerical simulation results with field measurements, deformation of the ultra-large seven-ring internal support system, deformation of the surrounding soil and axial force of the supports are analyzed. The results show that the finite element simulation agrees well with the measured data. This work provides a reliable method for analyzing ultra-large deep foundation pit.