Journal ArticleDOI
Noise-robust soft clustering of gene expression time-course data
TLDR
To overcome the limitations of hard clustering, this work applied soft clustering which offers several advantages for researchers, including more noise robust and a priori pre-filtering of genes can be avoided.Abstract:
Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.read more
Citations
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Journal ArticleDOI
Comparative Transcriptome Landscape of Mouse and Human Hearts
TL;DR: The mouse and human dataset is focused on and a novel set of maturation marker genes are identified that are more consistent between mice and humans and highlight the importance of studying human samples rather than relying on a mouse time-series dataset.
Posted ContentDOI
Sequential defects in cardiac lineage commitment and maturation cause hypoplastic left heart syndrome
Markus Krane,M Dressen,Gianluca Santamaria,Ilaria My,Christine M. Schneider,Tatjana Dorn,Svenja Laue,Elisa Mastantuono,Riccardo Berutti,H. Rawat,Ralf Gilsbach,Ralf Gilsbach,Pedro Schneider,Harald Lahm,S. Schwarz,Stefanie A. Doppler,Sharon L. Paige,Nazan Puluca,Sophia Doll,I. Neb,Thomas Brade,Zhong Zhang,C. Abou-Ajram,Bernd H. Northoff,Lesca M. Holdt,Stefanie Sudhop,Makoto Sahara,Alexander Goedel,Andreas Dendorfer,Fleur V.Y. Tjong,Maria Rijlaarsdam,Julie Cleuziou,Nora Lang,Christian Kupatt,Connie R. Bezzina,Ruediger Lange,Neil E. Bowles,Matthias Mann,Bruce D. Gelb,Lia Crotti,Lutz Hein,Thomas Meitinger,Sen Wu,Daniel Sinnecker,Peter J. Gruber,K L Laugwitz,Alessandra Moretti +46 more
TL;DR: In this paper, the authors identify perturbations in gene programs controlling ventricular muscle lineage development in Hypoplastic Left Heart Syndrome (HLHS) and demonstrate that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.
Journal ArticleDOI
Enabling more sophisticated gene expression analysis for understanding diseases and optimizing treatments
TL;DR: An advanced integrated framework is envisioned, and a system based on it is developed, to provide biologically inspired solutions in the analysis of gene expression data for the purposes of disease subtype diagnosis, new subtype discovery, and understanding of diseases and treatment responses.
Journal ArticleDOI
Plasma cell-free RNA characteristics in COVID-19 patients
Yanqun Wang,Jie Li,Lu Zhang,Hai-Xi Sun,Zhaoyong Zhang,Jinjin Xu,Yonghao Xiu,Yu-Cheng Lin,Airu Zhu,Yuxue Luo,Haibo Zhou,Yan Wu,Shanwen Lin,Yuzhe Sun,Fei Xiao,Ruiying Chen,Liyan Wen,Wei Min Chen,Fang Li,Rijing Ou,Yanjun Zhang,Ting-Yi Kuo,Yuming Li,Lingguo Li,Jing Sun,Ke Sun,Zhen Zhuang,Hao Rong Lu,Zhao Chen,Guoqiang Mai,Jian-fang Zhuo,Puyi Qian,Jiayu Chen,Huanming Yang,Jian Wang,XunHua Xu,Nanshan Zhong,Jingxian Zhao,Junhua Li,Jincun Zhao,Xin Jin +40 more
TL;DR: Insight is offered into the potential mechanisms of cfRNAs to explain COVID-19 pathogenesis and several pneumonia-related microorganisms were detected in the plasma of COIDs, raising the possibility of simultaneously monitoring immune response regulation and microbial communities using cfRNA analysis.
Journal ArticleDOI
VSClust: feature-based variance-sensitive clustering of omics data.
Veit Schwämmle,Ole N. Jensen +1 more
TL;DR: Based on an algorithm derived from fuzzy clustering, VSClust unifies statistical testing with pattern recognition to cluster the data into feature groups that more accurately reflect the underlying molecular and functional behavior.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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