High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis

Tang, Hui; Zeng, Tao; Chen, Luonan

doi:10.3389/fgene.2019.00371

Cited by 12 publications

(12 citation statements)

References 67 publications

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“…For example, single-cell RNA sequencing can help to expose biological processes and medical insights [115]. The k-means clustering typically performs better than hierarchical clustering in smaller datasets, but it requires a long computational time [114,115]. Other than that, large amounts of bulk data can address biological dynamics and cancer heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…Other than that, large amounts of bulk data can address biological dynamics and cancer heterogeneity. Tang et al [115] proposed High-order Correlation Integration (HCI), which uses k-means clustering and Pearson's correlation coefficient in the experiments. Their results showed that HCI outperforms the existing methods (k-means clustering and hierarchical clustering) under single-cell and bulk RNA-seq datasets.…”

Section: Discussionmentioning

confidence: 99%

“…However, these techniques have some limitations when the data are very large [114]. Finally, partitioning clustering techniques are inappropriate for non-convex data but suitable for smaller datasets [53,114,115].…”

Section: Discussionmentioning

confidence: 99%

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A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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“…Based on the application of scRNA-Seq, 2 distinct subtypes of cancer-associated fibroblasts associated with prognosis and an mRNA-miRNA regulatory network of CRC were identified in previous studies 13 , 33 . However, in this study, tumor epithelial and T cells were discovered to exert an essential role in the evolution of CRC pathologic stage for the first time by graph-based clustering.…”

Section: Discussionmentioning

confidence: 99%

Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer

Li¹,

Zeng²,

Chen³

et al. 2020

J. Cancer

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Purpose: The patients diagnosed with colorectal cancer (CRC) are likely to undergo differential outcomes in clinical survival owing to different pathologic stages. However, signatures in association with pathologic evolution and CRC prognosis are not clearly defined. This study aimed to identify pathologic evolution-related genes in CRC based on both single-cell and bulk transcriptomics. Patients and methods: The CRC single-cell transcriptomic dataset (GSE81861, n=590) with clinical information and tumor microenvironmental tissues was collected to identify the pathologic evolution-related genes. The colonic adenocarcinoma and rectum adenocarcinoma transcriptomics from The Cancer Genome Atlas were obtained as the training dataset (n=363) and 5 other CRC transcriptomics cohorts from Gene Expression Omnibus (n=1031) were acquired as validation data. Graph-based clustering analysis algorithm was applied to identify pathologic evolution-related cell populations. Pseudotime analysis was performed to construct the trajectory plot of pathologic evolution and to define hub genes in the evolution process. Cell-type identification by estimating relative subsets of RNA transcripts was then executed to build a novel cell infiltration classifier. The prediction efficacy of this classifier was validated in bulk transcriptomic datasets. Results: Epithelial and T cells were elucidated to be related to the pathologic stages in CRC tissues. Pseudotime analysis and survival analysis indicated that HOXC5, HOXC8 and BMP5 were the marker genes in pathologic evolution process. Our cell infiltration classifier exhibited excellent forecast efficacy in predicting pathologic stages and prognosis of CRC patients. Conclusion: We identified pathologic evolution-related genes in single-cell transcriptomic and proposed a novel specific cell infiltration classifier to forecast the prognosis of CRC patients based on pathologic stage-related hub genes HOXC6, HOXC8 and BMP5.

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“…However, compared to traditional bulk RNA-seq data, the prevalence of high technical noise and dropout events is a major problem in scRNA-seq [12-17], which raises substantial challenges for data analysis. Many computational methods were proposed to improve the identification of new cell types [18-21]. Meanwhile, imputation is an effective strategy to transform the dropouts to the substituted values [22-26].…”

Section: Introductionmentioning

confidence: 99%

CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

Dai

Fang

et al. 2020

Preprint

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24The rapid advancement of single cell technologies has shed new light on the complex 25 mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing 26 (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower 27 coverage, which brings new computational difficulties. Based on statistical 28 independence, cell-specific network (CSN) is able to quantify the overall associations 29 between genes for each cell, yet suffering from a problem of overestimation related to 30 indirect effects. To overcome this problem, we propose the "conditional cell-specific 31 network" (CCSN) method, which can measure the direct associations between genes 32 by eliminating the indirect associations. CCSN can be used for cell clustering and 33 dimension reduction on a network basis of single cells. Intuitively, each CCSN can be 34 viewed as the transformation from less "reliable" gene expression to more "reliable" 35 gene-gene associations in a cell. Based on CCSN, we further design network flow 36 entropy (NFE) to estimate the differentiation potency of a single cell. A number of 37 scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one 38 direct association network for one cell; (2) most existing scRNA-seq methods designed 39 for gene expression matrices are also applicable to CCSN-transformed degree matrices; 40 (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by 41 quantifying the potency of each cell. CCSN is publicly available at 42 http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip. 43 44 45 KEYWORDS: Single cell analysis; Network flow entropy; Cell-specific network; 46 Single cell network; Direct association; Conditional independence 47 49 With the development of high-throughput single-cell RNA sequencing (scRNA-seq), 50 novel cell populations in complex tissues [1-5] can be identified and the differentiation 51 trajectory of cell states [6-8] can be obtained, which opens a new way to understand the 52 heterogeneity and transition of cells [9-11]. However, compared to traditional bulk 53RNA-seq data, the prevalence of high technical noise and dropout events is a major 54 problem in scRNA-seq [12][13][14][15][16][17], which raises substantial challenges for data analysis. 55Many computational methods were proposed to improve the identification of new cell 56 types [18][19][20][21]. Meanwhile, imputation is an effective strategy to transform the dropouts 57 to the substituted values [22][23][24][25][26]. However, most of these methods mainly analyze 58 mRNA expression/concentrations, while the information of gene-gene interactions (or 59 their network) is ignored. 60Recently, a network-based method, cell-specific network (CSN), was proposed to 61 perform network analysis for scRNA-seq data [27], which elegantly infers a network 62 for each cell and successfully transforms the noisy and "unreliable" gene expression 63 data to the more "reliable" gene association data. The network degree matrix (NDM) 64 derived from CSN can be furt...

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High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis

Cited by 12 publications

References 67 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer

CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

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