An Algorithm for Clustering cDNA Fingerprints

Hartuv, Erez; Schmitt, Armin O.; Lange, Jörg; Meier‐Ewert, Sebastian; Lehrach, Hans; Shamir, Ron

doi:10.1006/geno.2000.6187

Cited by 77 publications

(37 citation statements)

References 22 publications

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“…We used a graph-based density clustering method termed the highly connected subgraph (HCS) method (Hartuv et al 2000), which is optimized for homogeneous clusters within a larger heterogeneous background, with a similarity measure consisting of a weighted Pearson correlation coefficient. HCS is parameter free, except for a robust threshold (set to 0.8) to define the false-discovery rate (FDR) of the final cluster set, enabling the detection of definite clusters from the noisy background of cell lines.…”

Section: Data Normalizationmentioning

confidence: 99%

Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines

et al. 2014

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We expanded the knowledge base for Drosophila cell line transcriptomes by deeply sequencing their small RNAs. In total, we analyzed more than 1 billion raw reads from 53 libraries across 25 cell lines. We verify reproducibility of biological replicate data sets, determine common and distinct aspects of miRNA expression across cell lines, and infer the global impact of miRNAs on cell line transcriptomes. We next characterize their commonalities and differences in endo-siRNA populations. Interestingly, most cell lines exhibit enhanced TE-siRNA production relative to tissues, suggesting this as a common aspect of cell immortalization. We also broadly extend annotations of cis-NAT-siRNA loci, identifying ones with common expression across diverse cells and tissues, as well as cell-restricted loci. Finally, we characterize small RNAs in a set of ovary-derived cell lines, including somatic cells (OSS and OSC) and a mixed germline/somatic cell population (fGS/ OSS) that exhibits ping-pong piRNA signatures. Collectively, the ovary data reveal new genic piRNA loci, including unusual configurations of piRNA-generating regions. Together with the companion analysis of mRNAs described in a previous study, these small RNA data provide comprehensive information on the transcriptional landscape of diverse Drosophila cell lines. These data should encourage broader usage of fly cell lines, beyond the few that are presently in common usage.

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Section: Data Normalizationmentioning

confidence: 99%

Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines

et al. 2014

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“…In order to assess the effectiveness of the DOS-FCM with respect to accurate partitioning, the Minkowski (MS) score [23] is adopted for quantitative evaluation purpose. The MS score is defined as:…”

Section: Experimental Simulations Of the Dos-fcm In Clustering Artifimentioning

confidence: 99%

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

Ong

Zainuddin

2016

Applied Soft Computing

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“…Such study sheds light on obtaining bio-markers for classifying cancers. Clustering analysis [4][5][6][7][8] is prevalent for the analysis of microarray data. Some studies on clustering analysis have focused on biclustering of gene expression data [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A genetic filter for cancer classification on gene expression data

Kim

Yoon

2015

BME

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Abstract. We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-correlation among selected genes. The proposed genetic filter was tested on well-known leukemia datasets, and significant improvement over previous work was obtained.

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An Algorithm for Clustering cDNA Fingerprints

Cited by 77 publications

References 22 publications

Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines

Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

A genetic filter for cancer classification on gene expression data

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