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Tumor Phylogeny Topology Inference via Deep Learning
[article]
2020
bioRxiv
pre-print
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In fact, even when the goal is to infer basic topological features of the tumor phylogeny rather than reconstructing it entirely, available techniques may be prohibitively slow. ...
Because of the limitations of SCS technologies, such as frequent allele dropout and variable sequence coverage, a noise reduction/elimination process may become necessary to infer a tumor phylogeny. ...
E.S.A. and S.C.S. were supported in part by NSF grant AF-1619081, R.K. was supported in part by NSF grant IIS-1906694, and M.H.E. was supported in part by Indiana U. ...
doi:10.1101/2020.02.07.938852
fatcat:ngd6ndp4wrcxthleaf2dkceayi
Tumor Phylogeny Topology Inference via Deep Learning
2020
iScience
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Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents ...
In this paper, we introduce fast deep learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible ...
on phylogeny inference from bulk or single-cell sequencing data El-Kebir, 2018) . ...
doi:10.1016/j.isci.2020.101655
pmid:33117968
pmcid:PMC7582044
fatcat:r3vj6gwdvzdw7jtynf4zqb3ct4
Parameter, noise, and tree topology effects in tumor phylogeny inference
2019
BMC Medical Genomics
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While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these ...
Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease. ...
A number of recent approaches have focused on using single-cell sequencing data to reconstruct tumor phylogenies [14] [15] [16] . ...
doi:10.1186/s12920-019-0626-0
pmid:31865909
pmcid:PMC6927103
fatcat:p5nomzes5jdwhkvr3b5tiawq34
Reference-free inference of tumor phylogenies from single-cell sequencing data
2015
BMC Genomics
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The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially ...
Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression. ...
Acknowledgements This work is supported in part by the US National Institutes of Health award #1R01CA140214. We acknowledge Dr. Stanley Shackney for his useful inputs and feedback. ...
doi:10.1186/1471-2164-16-s11-s7
pmid:26576947
pmcid:PMC4652515
fatcat:nhv6zujaibeedcypcby2a3koua
Reference-free inference of tumor phylogenies from single-cell sequencing data
2014
2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)
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The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially ...
Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression. ...
Acknowledgements This work is supported in part by the US National Institutes of Health award #1R01CA140214. We acknowledge Dr. Stanley Shackney for his useful inputs and feedback. ...
doi:10.1109/iccabs.2014.6863944
dblp:conf/iccabs/SubramanianS14
fatcat:c3fmrp4osnavthcoibaokyjkk4
Applying unmixing to gene expression data for tumor phylogeny inference
2010
BMC Bioinformatics
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Articles in BMC journals are listed in PubMed and archived at PubMed Central. ...
Like all articles in BMC journals, this peer-reviewed article was published immediately upon acceptance. ...
The organizations providing funding for this work had no role in the study design; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit ...
doi:10.1186/1471-2105-11-42
pmid:20089185
pmcid:PMC2823708
fatcat:qm62u22qcjfw7eotnezs2rcika
BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies
2015
Genome Biology
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In two case studies, we demonstrate how BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm. ...
Here, we present BitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. ...
The authors would like to acknowledge Wei Liu for preparing the Sankey plot script, Edith Ross for converting SNV profiles to binary data, Andrea Sottoriva for helpful discussions and providing the processed ...
doi:10.1186/s13059-015-0592-6
pmid:25786108
pmcid:PMC4359483
fatcat:kspn5r667fhf3geq66r7g2jigy
MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells
2022
Frontiers in Genetics
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We present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles. ...
In cancer, somatic mutations occur continuously, causing cell populations to evolve. ...
In conclusion, MOCA provides tools to assess the reliability of genetic ancestry annotation from phylogenies inferred using noisy genetic data (i.e., SNVs detected in scRNA-seq data). ...
doi:10.3389/fgene.2022.831040
pmid:35432484
pmcid:PMC9009314
fatcat:bvocyyvnlzajjhtalh274aouay
Accurate and Efficient Cell Lineage Tree Inference from Noisy Single Cell Data: the Maximum Likelihood Perfect Phylogeny Approach
[article]
2019
bioRxiv
pre-print
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In this paper, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. ...
Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. ...
It is highly desirable to infer cell lineage tree with single cell data (e.g., single cell DNA sequences) from multiple sampled (healthy or tumor) cells. ...
doi:10.1101/742395
fatcat:qdfhknej7fgwrcy3iqx7wu6lnm
Tree inference for single-cell data
[article]
2016
biorxiv/medrxiv
pre-print
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Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches. ...
Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumour from noisy and incomplete mutation profiles of single cells. ...
for tree inference from bulk-sequencing data. ...
doi:10.1101/047795
fatcat:i7k55rq67fakjiz4ypqwtliile
Predicting clone genotypes from tumor bulk sequencing of multiple samples
[article]
2018
bioRxiv
pre-print
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Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. ...
Motivation: Analyses of data generated from bulk sequencing of tumors have revealed extensive ge-nomic heterogeneity within patients. ...
While phylogenies of tumor samples are commonly inferred using these data, these phylogenies are not identical to clone phylogenies because multiple clones are often present within each sample (Alves ...
doi:10.1101/341180
fatcat:tipsb5nm55gydkkvako5vxku34
Tree inference for single-cell data
2016
Genome Biology
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Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. ...
Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches. ...
for tree inference from bulk-sequencing data. ...
doi:10.1186/s13059-016-0936-x
pmid:27149953
pmcid:PMC4858868
fatcat:blwhd3rbdfh7hh5zyjmy3ds4wu
Next Generation Sequencing (Dagstuhl Seminar 16351)
2017
Dagstuhl Reports
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Next Generation Sequencing (NGS) data have begun to appear in many applications that are clinically relevant, such as resequencing of cancer patients, disease-gene discovery and diagnostics for rare diseases ...
The analysis of sequencing data is demanding because of the enormous data volume and the need for fast turnaround time, accuracy, reproducibility, and data security. ...
Phylogeny inference under the infinite sites assumption to clean up noisy observations: SCITE and OncoNEM. ...
doi:10.4230/dagrep.6.8.91
dblp:journals/dagstuhl-reports/MyersPRW16
fatcat:ffovfpifvfe5nbmlsvwqkhiv3u
SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models
[article]
2016
bioRxiv
pre-print
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We propose a statistical inference method for tumor phylogenies from noisy SCS data under a finite-sites model. We demonstrate the performance of our method on synthetic and biological data sets. ...
Single-cell sequencing (SCS) enables the inference of tumor phylogenies and methods were recently introduced for this task under the infinite-sites assumption. ...
Results and discussion Overview of SiFit We start with a brief explanation of how SiFit infers a tumor phylogeny from noisy genotype data obtained from single-cell sequencing. ...
doi:10.1101/091595
fatcat:qvnp7lro3bhyhef5g6f7gwagie
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
2017
Genome Biology
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We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. ...
Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. ...
Raw sequencing data for the human tumor data sets are available from the Short Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra), under SRA numbers SRP067815 and SRP074289. ...
doi:10.1186/s13059-017-1311-2
pmid:28927434
pmcid:PMC5606061
fatcat:d4rhrx4t75g5bl7idot7gbv26q
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