scholarly journals MONET: Multi-omic patient module detection by omic selection

2020 ◽  
Author(s):  
Nimrod Rappoport ◽  
Roy Safra ◽  
Ron Shamir
Keyword(s):  
Clustering Algorithms ◽  
Simulated Data ◽  
Cell Types ◽  
Clustering Methods ◽  
Data Types ◽  
Common Structure ◽  
Genome Wide ◽  
Distinct Cluster ◽  
Genome Wide Data ◽  
Unique Approach

AbstractRecent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present Monet (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. Monet discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most extant multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures using Bayesian statistics. We tested Monet extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that Monet compares favorably with other multi-omic clustering methods. We demonstrate Monet’s biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that Monet is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that Monet is a valuable tool that can provide complementary results to those provided by extant algorithms for multi-omic analysis.

scholarly journals Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types

2021 ◽  
Vol 7 (3) ◽  
pp. eabd9036
Author(s):  
Sara Saez-Atienzar ◽  
Sara Bandres-Ciga ◽  
Rebekah G. Langston ◽  
Jonggeol J. Kim ◽  
Shing Wan Choi ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Membrane Trafficking ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Polygenic Risk Score ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Data Driven Approach ◽  
Single Nucleus ◽  
Lateral Sclerosis

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

Single-cell RNA-seq data clustering: A survey with performance comparison study

2020 ◽  
Vol 18 (04) ◽  
pp. 2040005
Author(s):  
Ruiyi Li ◽  
Jihong Guan ◽  
Shuigeng Zhou
Keyword(s):  
Single Cell ◽  
Data Clustering ◽  
Performance Metrics ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Performance Comparison ◽  
Cellular Heterogeneity ◽  
Clustering Methods ◽  
Multiple Perspectives ◽  
Underlying Mechanisms

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.

scholarly journals Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Erdogan Taskesen ◽  
Sjoerd M. H. Huisman ◽  
Ahmed Mahfouz ◽  
Jesse H. Krijthe ◽  
Jeroen de Ridder ◽  
...  
Keyword(s):  
Therapy Response ◽  
Visual Exploration ◽  
Molecular Characteristics ◽  
Cancer Type ◽  
Breast Cancers ◽  
Data Types ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Cancer Types

Abstract The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2209 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

Summary.In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available athttp://fluff.readthedocs.org.Availability.fluff is implemented in Python and runs on Linux. The source code is freely available for download athttps://github.com/simonvh/fluff.

scholarly journals RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms

2020 ◽  
Vol 6 ◽  
pp. e251 ◽  
Author(s):  
Zhaodong Hao ◽  
Dekang Lv ◽  
Ying Ge ◽  
Jisen Shi ◽  
Dolf Weijers ◽  
...  
Keyword(s):  
Gc Content ◽  
R Package ◽  
Whole Genome ◽  
Data Mapping ◽  
Data Types ◽  
Model Species ◽  
Chromosomal Distribution ◽  
Whole Genome Analysis ◽  
Genome Wide ◽  
Genome Wide Data

Background Owing to the rapid advances in DNA sequencing technologies, whole genome from more and more species are becoming available at increasing pace. For whole-genome analysis, idiograms provide a very popular, intuitive and effective way to map and visualize the genome-wide information, such as GC content, gene and repeat density, DNA methylation distribution, genomic synteny, etc. However, most available software programs and web servers are available only for a few model species, such as human, mouse and fly, or have limited application scenarios. As more and more non-model species are sequenced with chromosome-level assembly being available, tools that can generate idiograms for a broad range of species and be capable of visualizing more data types are needed to help better understanding fundamental genome characteristics. Results The R package RIdeogram allows users to build high-quality idiograms of any species of interest. It can map continuous and discrete genome-wide data on the idiograms and visualize them in a heat map and track labels, respectively. Conclusion The visualization of genome-wide data mapping and comparison allow users to quickly establish a clear impression of the chromosomal distribution pattern, thus making RIdeogram a useful tool for any researchers working with omics.

scholarly journals mbkmeans: Fast clustering for single cell data using mini-batch k-means

2021 ◽  
Vol 17 (1) ◽  
pp. e1008625
Author(s):  
Stephanie C. Hicks ◽  
Ruoxi Liu ◽  
Yuwei Ni ◽  
Elizabeth Purdom ◽  
Davide Risso
Keyword(s):  
Single Cell ◽  
Clustering Algorithms ◽  
Large Datasets ◽  
Clustering Methods ◽  
Cell Clustering ◽  
Genome Wide ◽  
Data Representations ◽  
Computing Performance ◽  
Cell Data ◽  
Genome Wide Gene Expression

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

scholarly journals Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types

2020 ◽  
Author(s):  
Sara Saez-Atienzar ◽  
Sara Bandres-Ciga ◽  
Rebekah G. Langston ◽  
Jonggeol J. Kim ◽  
Shing Wan Choi ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Membrane Trafficking ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Polygenic Risk Score ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Data Driven Approach ◽  
Single Nucleus ◽  
Lateral Sclerosis

ABSTRACTDespite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic cortical interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated five differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

scholarly journals Pervasive selection biases inferences of the species tree

2020 ◽  
Author(s):  
Rui Borges ◽  
Bastien Boussau ◽  
Gergely Szollosi ◽  
Carolin Kosiol
Keyword(s):  
Simulated Data ◽  
Population Level ◽  
Fruit Fly ◽  
Genetic Distances ◽  
Species Tree ◽  
Molecular Dating ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Gc Bias ◽  
The Impact

Despite the importance of natural selection in species' evolutionary history, phylogenetic methods that take into account population-level processes ignore selection. Assuming neutrality is often based on the idea that selection occurs at a minority of loci in the genome and is unlikely to significantly compromise phylogenetic inferences. However, selection might behave more pervasively, as it the case of nearly neutral evolving mutations. Genome-wide processes like GC-bias and some of the variation segregating at the coding regions are known to evolve in the nearly neutral range. As we are now using genome-wide data to estimate species tree, it is just natural to ask whether weak, but pervasive, selection is likely to blur species tree inferences. Here, we employed a polymorphism-aware phylogenetic model, specially tailored for measuring signatures of nucleotide usage biases, to test the impact of nearly neutrally in the substitution process. Analyses with simulated data indicate that while the inferred relationships among species are not significantly compromised, the genetic distances are systematically underestimated, with the deeper nodes suffering more than the younger ones. Such biases have implications for molecular dating. We found signatures of GC-bias considerably affecting the estimated divergence times (up to 21%) of worldwide fruit fly populations. Our findings call for the need to account for nearly neutral forces (or any other form of pervasive selection) when quantifying divergence or dating species evolution.

scholarly journals Robust clustering and interpretation of scRNA-seq data using reference component analysis

2021 ◽  
Author(s):  
Florian Schmidt ◽  
Bobby Ranjan ◽  
Quy Xiao Xuan Lin ◽  
Vaidehi Krishnan ◽  
Ignasius Joanito ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Robust Clustering ◽  
Supervised Clustering ◽  
Downstream Analysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation.ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets.AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

A Parsimonious Explanation of the Resilient, Undercontrolled, and Overcontrolled Personality Types

2017 ◽  
Vol 31 (6) ◽  
pp. 658-668 ◽  
Author(s):  
Tom Rosenström ◽  
Markus Jokela ◽  
Christian Kandler
Keyword(s):  
Big Five ◽  
Meta Analysis ◽  
Clustering Algorithms ◽  
Simulated Data ◽  
Personality Types ◽  
Correlation Structure ◽  
Clustering Methods ◽  
Personality Psychology ◽  
Cluster Solutions ◽  
Personality Description

Researchers applying clustering methods have found that the five most commonly studied personality traits (the ‘Big Five’) appear to form three prototypes, known as resilient, undercontrolled, and overcontrolled (RUO) personality types. The analysis has been replicated cross–nationally, and the results have been reasonably robust. However, these findings do not necessarily imply discontinuities or non–linearities in the Big Five data. We study whether the RUO types can arise from typical Big Five intercorrelations alone. We used data from a previous meta–analysis of inter–trait correlations ( N = 144 117 participants) and simulated normally distributed observations with this correlation structure. Applying three different clustering algorithms ( k–means, hierarchical agglomerative, and model based) with three–cluster solutions to the simulated data, we examined whether the known correlations alone can give rise to the RUO typology. The simulated results were compared with previous empirical findings. A simple multivariate normal distribution with the Big Five correlation structure was sufficient to generate the RUO typology in three–cluster solutions for all the three clustering methods. Contrary to the RUO typology ‘carving personality description at its joints’, linear correlations typical for correlations among Big Five traits can create RUO types even in the absence of any points of discontinuity. Copyright © 2017 European Association of Personality Psychology

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