TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes

Jung, Il Bong; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Yang; Kim, Sun

doi:10.1093/bioinformatics/btw780

Cited by 29 publications

(25 citation statements)

References 28 publications

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“…After normalization using estimated size factors with the DESeq2 package 51 , the expression values were transformed using the log 2 function. Differentiation-related expression dynamics were calculated using TimesVector v1.03 56 . The K-value, which is the number of clusters targeted for detection, was evaluated using the following equation: K 85 71 28 57x, ( 1) = − .…”

Section: Sample Preparation Genomicmentioning

confidence: 99%

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Han

Jung

et al. 2020

Sci Rep

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Although human induced pluripotent stem cell (hiPSC) lines are karyotypically normal, they retain the potential for mutation in the genome. Accordingly, intensive and relevant quality controls for clinicalgrade hiPSCs remain imperative. As a conceptual approach, we performed RNA-seq-based broad-range genetic quality tests on GMp-compliant human leucocyte antigen (HLA)-homozygous hipScs and their derivatives under postdistribution conditions to investigate whether sequencing data could provide a basis for future quality control. We found differences in the degree of single-nucleotide polymorphism (SNP) occurring in cells cultured at three collaborating institutes. However, the cells cultured at each centre showed similar trends, in which more SNPs occurred in late-passage hiPSCs than in earlypassage hiPSCs after differentiation. In eSNP karyotyping analysis, none of the predicted copy number variations (CNVs) were identified, which confirmed the results of SNP chip-based CNV analysis. HLA genotyping analysis revealed that each cell line was homozygous for HLA-A, HLA-B, and DRB1 and heterozygous for HLA-DPB type. Gene expression profiling showed a similar differentiation ability of early-and late-passage hiPSCs into cardiomyocyte-like, hepatic-like, and neuronal cell types. However, time-course analysis identified five clusters showing different patterns of gene expression, which were mainly related to the immune response. In conclusion, RNA-seq analysis appears to offer an informative genetic quality testing approach for such cell types and allows the early screening of candidate hipSc seed stocks for clinical use by facilitating safety and potential risk evaluation.In the decades since the discovery of human induced pluripotent stem cells (hiPSCs) by Takahashi and Yamanaka 1 , considerable advances have been made in our understanding of these cells 2-4 . hiPSCs currently present potential clinical applications in cell therapy and regenerative medicine 5 , and with the broadening of these clinical applications, the standardization of the quality control (QC) of hiPSCs is becoming increasingly open Scientific RepoRtS | (2020) 10:3939 | https://doi.org/10.1038/s41598-020-60466-9www.nature.com/scientificreports www.nature.com/scientificreports/ important. In particular, the evaluation of the genetic stability of the starting materials and the final product is a key consideration during QC processes for selecting suitable hiPSC lines for clinical application, as it may affect final product quality, efficacy, and safety 6,7 .The genomes of hiPSCs are characterized by potentially wide variability, including aneuploidy, subchromosomal copy number variation (CNV), single-nucleotide variations (SNVs), and epigenetic aberrations 8 . Deletions of tumour suppressor genes and changes in immune response-related genes may not be reflected in phenotypic changes in hiPSCs; however, they could play a pivotal role once the cells have differentiated or been transplanted, although such mutations are also known to occur in perfectly he...

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Section: Sample Preparation Genomicmentioning

confidence: 99%

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Han

Jung

et al. 2020

Sci Rep

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“…TimesVector (Jung et al 2017) has been recently proposed to find similarly and differentially expressed patterns in gene-sample-time data. To this end, the sample and time dimensions are first concatenated into a single dimension and spherical k-means applied to measure the similarity between observations under a silhouette score.…”

Section: Pattern-based Approachesmentioning

confidence: 99%

Triclustering Algorithms for Three-Dimensional Data Analysis

Henriques

Madeira

2018

ACM Comput. Surv.

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Three-dimensional data are increasingly prevalent across biomedical and social domains. Notable examples are gene-sample-time, individual-feature-time, or node-node-time data, generally referred to as observationattribute-context data. The unsupervised analysis of three-dimensional data can be pursued to discover putative biological modules, disease progression profiles, and communities of individuals with coherent behavior, among other patterns of interest. It is thus key to enhance the understanding of complex biological, individual, and societal systems. In this context, although clustering can be applied to group observations, its relevance is limited since observations in three-dimensional data domains are typically only meaningfully correlated on subspaces of the overall space. Biclustering tackles this challenge but disregards the third dimension. In this scenario, triclustering-the discovery of coherent subspaces within three-dimensional data-has been largely researched to tackle these problems. Despite the diversity of contributions in this field, there still lacks a structured view on the major requirements of triclustering, desirable forms of homogeneity (including coherency, structure, quality, locality, and orthonormality criteria), and algorithmic approaches. This work formalizes the triclustering task and its scope, introduces a taxonomy to categorize the contributions in the field, provides a comprehensive comparison of state-of-the-art triclustering algorithms according to their behavior and output, and lists relevant real-world applications. Finally, it highlights challenges and opportunities to advance the field of triclustering and its applicability to complex three-dimensional data analysis.

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“…Thus, such tools do not aim to detect genes or transcriptional expression patterns from multi-class time-series scRNA-seq data, which we find is an important topic that needs to be addressed. For example, based on bulk RNA-seq data, we have previously developed a multi-class time-series clustering tool, TimesVector [11], which was successful in finding gene clusters with significantly distinctive expression patterns in the rice plants that were treated with four different hormones [12].…”

Section: Introductionmentioning

confidence: 99%

A Non-Negative Matrix Factorization-Based Framework for the Analysis of Multi-Class Time-Series Single-Cell RNA-Seq Data

2020

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The development of single-cell RNA sequencing (scRNA-seq) has enabled gene expression to be quantified at single-cell resolution. Such advancement is expected to solve important issues that bulk RNA sequencing could not fully answer, such as inferring cell population heterogeneity, genetic variability of cells, detecting rare cell types, accurately predicting cell states and their localization. However, analyzing such large scale data, especially when they are sampled at multiple time points, brings new challenges in data mining informative genes, compared to single snapshot samples. It becomes even more complicated when gene expression patterns are to be mined from time-series scRNA-seq datasets generated from multiple conditions, which will constitute a data with gene, condition and time dimensions. Here, we focused on detecting gene expression patterns that well capture the underlying biological differences between time-series scRNA-seq datasets of three different types of stem cells. The gene expression profile of 2,128 time-series scRNA-seq samples from long-term hematopoietic stem cells (LT-HSC) and two of its progenitor cell types were analyzed using our framework. We have successfully detected condition specific feature genes that were able to achieve 90.03% classification accuracy between the three cell types. Investigating the genes and clusters detected by our framework, we found that cell cycle related genes showed significantly high variance between the three cell types. Such results and transcriptomic characters detected from our analysis were consistent with the original study. Collectively, the framework was able to successfully detect biological meaningful gene sets and expression patterns from multi-condition time-series scRNA-seq samples. INDEX TERMS Gene expression, multi-class, single-cell, time-series.

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TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 29 publications

References 28 publications

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Triclustering Algorithms for Three-Dimensional Data Analysis

A Non-Negative Matrix Factorization-Based Framework for the Analysis of Multi-Class Time-Series Single-Cell RNA-Seq Data

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