Incidence matrix description of intersecting 𝑝-brane solutions

Generalized Transfer-Matrix Description of the Optical Properties of Spatially Dispersive Periodic Metal Multilayers

physica status solidi (b) ◽

10.1002/pssb.2221510143 ◽

1989 ◽

Vol 151 (1) ◽

pp. 383-397 ◽

Cited By ~ 3

Author(s):

U. Trutschel ◽

M. Golz ◽

M. Abraham

Keyword(s):

Optical Properties ◽

Transfer Matrix ◽

Matrix Description ◽

Spatially Dispersive

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Power Losses Contribution Tracking Using Power Flow Tracing Incidence Matrix Method Based on Renewable Energy Distributed Generator

2020 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE) ◽

10.1109/icitamee50454.2020.9398451 ◽

2020 ◽

Author(s):

Avian Lukman Setya Budi ◽

Feby Agung Pamuji ◽

Mohamad Ridwan ◽

Adi Soeprijanto ◽

Ni Ketut Aryani

Keyword(s):

Renewable Energy ◽

Matrix Method ◽

Power Flow ◽

Incidence Matrix ◽

Power Losses ◽

Distributed Generator ◽

Power Flow Tracing

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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