Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization.

The development of single-cell RNA sequencing (scRNA-seq) technology has made it possible to measure gene expression levels at the resolution of a single cell, which further reveals the complex growth processes of cells such as mutation and differentiation. Recognizing cell heterogeneity is one of the most critical tasks in scRNA-seq research. To solve it, we propose a non-negative matrix factorization framework based on multi-subspace cell similarity learning for unsupervised scRNA-seq data analysis (MscNMF).

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR).

Sparse robust graph-regularized non-negative matrix factorization based on correntropy.

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC).

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework.

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data.

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification.

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can learn part-based representations effectively. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF).

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis.

Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality. Gene expression data usually have some noise and outliers, while the original NMF loss function is very sensitive to non-Gaussian noise.

[Effects of Long-term Implementation of the Flow-Sediment Regulation Scheme on Grain and Clay Compositions of Inshore Sediments in the Yellow River Estuary].

Based on the laser particle size and X-ray diffraction (XRD) analysis, 28 sediment samples collected from the inshore region of the Yellow River estuary in October 2013 were determined to discuss the influence of long-term implementation of the flow-sediment regulation scheme (FSRS, initiated in 2002) on the distributions of grain size and clay components (smectite, illite, kaolinite and chlorite) in sediments. Results showed that, after the FSRS was implemented for more than 10 years, although the proportion of sand in inshore sediments of the Yellow River estuary was higher (average value, 23.5%) than those in sediments of the Bohai Sea and the Yellow River, silt was predominated (average value, 59.

[Spatial-temporal distributions of dissolved inorganic carbon and its affecting factors in the Yellow River estuary].

Estuary is an important area contributing to the global carbon cycle. In order to analyze the spatial-temporal distribution characteristics of the dissolved inorganic carbon (DIC) in the surface water of Yellow River estuary. Samples were collected in spring, summer, fall, winter of 2013, and discussed the correlation between the content of DIC and environmental factors.

[Geochemical characteristics of radon and mercury in soil gas in Lhasa, Tibet, China].

The geochemical characteristics of radon and mercury in soil gas in Lhasa and vicinity are investigated based on the measurements of Rn and Hg concentrations, and environmental quality for Rn and Hg in soil gas was evaluated by means of the index of geoaccumulation. The data of Rn and Hg of 1 579 sampling site indicate that the values of environmental-geochemical background of Rn and Hg are 7 634.9 Bq/m3, 41.