Classification of human cancers based on DNA copy number amplification modeling

Myllykangas, Samuel; Tikka, Jarkko; Böhling, Tom; Knuutila, Sakari; Hollmén, Jaakko

doi:10.1186/1755-8794-1-15

Cited by 37 publications

(31 citation statements)

References 27 publications

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“…The ADO data and percent of cells removed from further analyses as a result of these quality control measures are summarized in SI Appendix, modeling-based approach where we first execute an expectation maximization algorithm on a multivariate Bernoulli model (SI Appendix, Fig. S2) (16)(17)(18). The number of clones was then estimated using Akaike information criterion.…”

Section: Resultsmentioning

confidence: 99%

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics

Gawad

Koh

Quake

2014

Proc. Natl. Acad. Sci. U.S.A.

285

323

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Many cancers have substantial genomic heterogeneity within a given tumor, and to fully understand that diversity requires the ability to perform single cell analysis. We performed targeted sequencing of a panel of single nucleotide variants (SNVs), deletions, and IgH sequences in 1,479 single tumor cells from six acute lymphoblastic leukemia (ALL) patients. By accurately segregating groups of cooccurring mutations into distinct clonal populations, we identified codominant clones in the majority of patients. Evaluation of intraclonal mutation patterns identified clone-specific punctuated cytosine mutagenesis events, showed that most structural variants are acquired before SNVs, determined that KRAS mutations occur late in disease development but are not sufficient for clonal dominance, and identified clones within the same patient that are arrested at varied stages in B-cell development. Taken together, these data order the sequence of genetic events that underlie childhood ALL and provide a framework for understanding the development of the disease at single-cell resolution.single-cell genomics | acute lymphoblastic leukemia | intratumor heterogeneity | clonal evolution | cytosine mutagenesis A more comprehensive understanding of how malignancies develop could facilitate the rational development of novel anticancer treatment and prevention strategies. Large projects that aim to comprehensively characterize somatic mutations in cancer samples have cataloged many of the recurrent genomic lesions in a wide variety of tumors (1). However, these studies do not measure the correlated cooccurrence of genomic lesions between different cells, which is required for understanding the clonal structure of a tumor as well as for rigorously determining temporal ordering of mutation acquisition. Other studies have provided some temporal resolution of mutation segregation patterns from diagnosis to disease recurrence, allowing for post hoc inference of intratumor clonal heterogeneity at diagnosis (2-5). However, approaches that rely on mutant allele frequencies to determine clonal structure require multiple samples from the same patient and are unable to resolve clones with mutations present at similar frequencies, which is a prerequisite to unambiguously determine the clonal structure and delineate the evolution of the disease (3-5). In principle, single cell genomics provides the most rigorous method to determine the clonal heterogeneity of tumors; as discussed below, there have been recent advances in this approach, but technical limitations have until now prevented it from fully addressing the questions of interest.Studies of pediatric acute lymphoblastic leukemias (ALL) have provided a limited ordering of the genetic events that underlie childhood leukemogenesis by studying prediagnostic samples. For example, ETV6 -RUNX1 translocations, which occur in about a third of patients under 10 y of age, have been shown to occur in utero by tracking the translocation back to neonatal blood spots (6, 7). In addition, a recent report ...

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Section: Resultsmentioning

confidence: 99%

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics

Gawad

Koh

Quake

2014

Proc. Natl. Acad. Sci. U.S.A.

285

323

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“…In their development, they applied BMM to various aspects of life i.e. on the study recognition of image, clustering of text and word [4,5,6,7,8,9], on cancer and schizophrenia [10,11,12,13] and in the machine learning research [14,15] …”

Section: Bernoulli Mixture Modelmentioning

confidence: 99%

“…y π x β (12) ( ) The MCMC convergence of estimated parameter can be monitored through Brooks-Gelman-Rubin method [21].…”

Section: Bbmrm Algorithmmentioning

confidence: 99%

“…In relation with estimation processes of model (12), Figure 4 presents the convergence of 1  , whereas Figure 5 shows the convergence of 2  . These graphics describe the evolution of the pooled posterior variance which has green color line, average within-sample variance which is plotted as a blue color line, and their ratio which is marked in red line.…”

Section: Bbmrm Algorithmmentioning

confidence: 99%

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Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Iriawan

Fithriasari

Ulama

et al. 2018

Jurnal Ilmu Komputer dan Informasi

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Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are AbstrakPenerima beasiswa Bidikmisi ditentukan berdasarkan kriteria yang berkaitan dengan kondisi sosial ekonomi dari orang tua atau wali dari penerima beasiswa. Proses penentuan penerimaan tidak mudah dilakukan mengingat terdapat data calon pendaftar yang cukup besar dan variabel kriteria yang bervariasi. Berdasarkan permasalahan tersebut, diusulkan pendekatan baru untuk penentuan penerima Bidikmisi dengan menggunakan model regresi mixture Bernouuli Bayesian. Prosedur pemodelan dilakukan dengan menyusun klaster pendaftar yang diterima dan tidak diterima yang selanjutnya dilakukan estimasi model untuk setiap klaster melalui model mixture regresi Bernoulli. Proses estimasi parameter model dilakukan dengan menyusun suatu algoritma dengan berdasarkan metode Bayesian Markov Chain Monte Carlo (MCMC). Ketepatan proses klasifikasi melalui model regresi mixture Bernoulli Bayesian diukur dengan menentukan prosentase klasifikasi penerimaan dari model yang dibandingkan dengan prosentase klasifikasi penerimaan dari model regresi dummy dan model regresi polytomous. Hasil perbandingan menunjukkan bahwa pendekatan model regresi nixture Bernoulli Bayesian memberikan prosentase ketepatan klasifikasi penerimaan lebih tinggi dibanding model regresi dummy dan model regresi polytomous.Kata Kunci: Model mixture regresi Bernoulli, Bayesian MCMC, Gibbs Sampling, Bidikmisi.

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“…In previous work, we have successfully clustered this data using mixture models (Myllykangas et al 2008;Tikka et al 2007). Furthermore, in Hollmén and Tikka (2007), we have learned linguistic names for the patterns that coincide with the natural structure in the data, enabling domain experts to use these names to refer to the clusters or to the patterns extracted from the clusters.…”

Section: Introductionmentioning

confidence: 99%

Explaining mixture models through semantic pattern mining and banded matrix visualization

et al. 2016

Self Cite

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This paper presents an approach to semi-automated data analysis, supported by tools for pattern construction, exploration and explanation. The proposed three-part methodology for multiresolution 0-1 data analysis consists of data clustering with mixture models, extraction of rules from clusters, as well as data and rule visualization using banded matrices. The results of the three-part process: clusters, rules from clusters, and banded structure of the data matrix are finally merged in a unified visual banded matrix display. The incorporation of multiresolution data is enabled by the supporting ontology, describing the relationships between the different resolutions, which is used as background knowledge in the semantic pattern mining process of descriptive rule induction. The presented experimental use case highlights the usefulness of the proposed methodology for analyzing complex DNA copy number amplification data, studied in previous research, for which we provide new insights in terms of induced semantic patterns and cluster/pattern visualization. The methodology is

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Classification of human cancers based on DNA copy number amplification modeling

Cited by 37 publications

References 27 publications

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics

Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Explaining mixture models through semantic pattern mining and banded matrix visualization

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