scholarly journals Power-law Null Model for Bystander Mutations in Cancer

2014 ◽  
Author(s):  
Loes Olde Loohuis ◽  
Andreas Witzel ◽  
Bud Mishra
Keyword(s):  
Power Law ◽  
Copy Number ◽  
Preferential Attachment ◽  
Null Model ◽  
Generative Model ◽  
Power Law Distribution ◽  
Driver Genes ◽  
Underlying Process ◽  
Cancer Driver ◽  
Number Variation

In this paper we study Copy Number Variation (CNV) data. The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic, and that segment lengths follow a power-law distribution instead. We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

scholarly journals Systematic identification of mutations and copy number alterations associated with cancer patient prognosis

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Joan C Smith ◽  
Jason M Sheltzer
Keyword(s):  
Copy Number ◽  
Meta Analysis ◽  
Gene Copy Number ◽  
Gene Copy ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Patient Prognosis ◽  
Almost All ◽  
Systematic Identification

Successful treatment decisions in cancer depend on the accurate assessment of patient risk. To improve our understanding of the molecular alterations that underlie deadly malignancies, we analyzed the genomic profiles of 17,879 tumors from patients with known outcomes. We find that mutations in almost all cancer driver genes contain remarkably little information on patient prognosis. However, CNAs in these same driver genes harbor significant prognostic power. Focal CNAs are associated with worse outcomes than broad alterations, and CNAs in many driver genes remain prognostic when controlling for stage, grade, TP53 status, and total aneuploidy. By performing a meta-analysis across independent patient cohorts, we identify robust prognostic biomarkers in specific cancer types, and we demonstrate that a subset of these alterations also confer specific therapeutic vulnerabilities. In total, our analysis establishes a comprehensive resource for cancer biomarker identification and underscores the importance of gene copy number profiling in assessing clinical risk.

DISTRIBUTION OF VOTES AND A MODEL OF POLITICAL OPINION FORMATION FOR MAJORITY ELECTIONS

2012 ◽  
Vol 16 ◽  
pp. 1-12 ◽  
Author(s):  
DODE PRENGA ◽  
MARGARITA IFTI
Keyword(s):  
Power Law ◽  
Gaussian Distribution ◽  
Similar Pattern ◽  
Word Of Mouth ◽  
Preferential Attachment ◽  
The Other ◽  
Opinion Formation ◽  
Power Law Distribution ◽  
Parliamentary Elections ◽  
Data Points

We study the behavior of the number of votes cast for different electoral subjects in majority elections, and in particular, the Albanian elections of the last 10 years, as well as the British, Russian, and Canadian elections. We report the frequency of obtaining a certain percentage (fraction) of votes versus this fraction for the parliamentary elections. In the distribution of votes cast in majority elections we identify two regimes. In the low percentiles we see a power law distribution, with exponent about -1.7. In the power law regime we find over 80% of the data points, while they relate to 20% of the votes cast. Votes of the small electoral subjects are found in this regime. The other regime includes percentiles above 20%, and has Gaussian distribution. It corresponds to large electoral subjects. A similar pattern is observed in other first past the post (FPP) elections, such as British and Canadian, but here the Gaussian is reduced to an exponential. Finally we show that this distribution can not be reproduced by a modified "word of mouth" model of opinion formation. This behavior can be reproduced by a model that comprises different number of zealots, as well as different campaign strengths for different electoral subjects, in presence of preferential attachment of voters to candidates.

scholarly journals ATAC-seq identifies thousands of extrachromosomal circular DNA in cancer and cell lines

2020 ◽  
Vol 6 (20) ◽  
pp. eaba2489 ◽  
Author(s):  
Pankaj Kumar ◽  
Shashi Kiran ◽  
Shekhar Saha ◽  
Zhangli Su ◽  
Teressa Paulsen ◽  
...  
Keyword(s):  
Cell Lines ◽  
Inverse Pcr ◽  
Driver Genes ◽  
Circular Dna ◽  
Cancer Driver ◽  
Genome Wide ◽  
Number Variation ◽  
Circular Dnas ◽  
Tumor Types ◽  
Pan Cancer

Extrachromosomal circular DNAs (eccDNAs) are somatically mosaic and contribute to intercellular heterogeneity in normal and tumor cells. Because short eccDNAs are poorly chromatinized, we hypothesized that they are sequenced by tagmentation in ATAC-seq experiments without any enrichment of circular DNA. Indeed, ATAC-seq identified thousands of eccDNAs in cell lines that were validated by inverse PCR and by metaphase FISH. ATAC-seq in gliomas and glioblastomas identify hundreds of eccDNAs, including one containing the well-known EGFR gene amplicon from chr7. More than 18,000 eccDNAs, many carrying known cancer driver genes, are identified in a pan-cancer analysis of ATAC-seq libraries from 23 tumor types. Somatically mosaic eccDNAs are identified by ATAC-seq even before amplification is recognized by genome-wide copy number variation measurements. Thus, ATAC-seq is a sensitive method to detect eccDNA present in a tumor at the pre-amplification stage and can be used to predict resistance to therapy.

scholarly journals BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiaqi Wu ◽  
Shuofeng Hu ◽  
Yaowen Chen ◽  
Zongcheng Li ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Copy Number Variation ◽  
Copy Number ◽  
Expression Profiles ◽  
Mammary Tissue ◽  
Lymph Node Status ◽  
Specific Gene ◽  
Driver Genes ◽  
Number Variation

Abstract Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of potential regulatory or driver genes, we present the Breast Cancer Integrative Platform (BCIP, http://www.omicsnet.org/bcancer/). BCIP maintains multi-omics data selected with strict quality control and processed with uniform normalization methods, including gene expression profiles from 9,005 tumor and 376 normal tissue samples, copy number variation information from 3,035 tumor samples, microRNA-target interactions, co-expressed genes, KEGG pathways, and mammary tissue-specific gene functional networks. This platform provides a user-friendly interface integrating comprehensive and flexible analysis tools on differential gene expression, copy number variation, and survival analysis. The prominent characteristic of BCIP is that users can perform analysis by customizing subgroups with single or combined clinical features, including subtypes, histological grades, pathologic stages, metastasis status, lymph node status, ER/PR/HER2 status, TP53 mutation status, menopause status, age, tumor size, therapy responses, and prognosis. BCIP will help to identify regulatory or driver genes and candidate biomarkers for further research in breast cancer.

Spatial Distribution of Tourism Activities: A Polya Urn Process Model of Rank-Size Distribution

2019 ◽  
Vol 59 (2) ◽  
pp. 231-246 ◽  
Author(s):  
Pong Lung Lau ◽  
Tay T. R. Koo ◽  
Cheng-Lung Wu
Keyword(s):  
Size Distribution ◽  
Power Law ◽  
Process Model ◽  
Word Of Mouth ◽  
Preferential Attachment ◽  
Power Law Distribution ◽  
Habit Persistence ◽  
Pólya Urn ◽  
Polya Urn ◽  
Attachment Process

The power law is considered one of the most enduring regularities in human geography. This article aims to develop an understanding of the circumstances that may result in the power law distribution in the geography of tourism activities. The finite Polya urn process is adopted as a device to model the preferential attachment process in the flow of tourists. The model generates a rank-size distribution of tourism regions along with intuitively appealing parameters. Empirically examined using two independent sets of Australian inbound and outbound tourism data, results show that the rank-size distribution emerging from the finite Polya urn process is a superior fit to the conventional power law curve. This rank-size distribution (termed the Polya urn process model of visitor distribution) is compatible with tourist behaviors such as habit persistence and word-of-mouth effects, and can be adopted by tourism modelers to predict and efficiently summarize the spatiality of tourism.

scholarly journals Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting

2017 ◽  
Author(s):  
Francesco Iorio ◽  
Fiona M Behan ◽  
Emanuel Gonçalves ◽  
Shriram G Bhosle ◽  
Elisabeth Chen ◽  
...  
Keyword(s):  
Copy Number ◽  
Large Scale ◽  
False Positive Rate ◽  
Essential Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Guide Rna ◽  
Genome Wide ◽  
Positive Rate ◽  
Downstream Analysis

AbstractBackground: Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes.Results Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries.Conclusions CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.

scholarly journals Formation of Chinese venture capital syndication network

2020 ◽  
Vol 72 (1) ◽  
pp. 49-64 ◽  
Author(s):  
Makoto Nirei ◽  
Toshiaki Shoji ◽  
Fei Yu
Keyword(s):  
Venture Capital ◽  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Statistical Properties ◽  
Power Law Distribution

AbstractUsing a dataset that recorded a large number of investment transactions in China from 1991 to 2018, we examine the statistical properties of the Chinese venture capital (VC) syndication network. Our main findings are as follows. First, the number of investment transactions sharply increased after 2014. Second, more than half of the VC firms are located in Beijing, Shanghai, and Shenzhen. Third, the degree distribution becomes roughly straight on a log–log plot. Fourth, the hypothesis that the degree distribution follows a power-law distribution is not rejected for 2015 and 2016. Fifth, better connected VC firms increase their connectivity faster, which suggests the existence of preferential attachment.

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