DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

Abstract Motivation Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. Results DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. Availability and implementation DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). Supplementary information Supplementary data are available at Bioinformatics online.

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simGWAS: a fast method for simulation of large scale case–control GWAS summary statistics

Bioinformatics ◽

10.1093/bioinformatics/bty898 ◽

2018 ◽

Vol 35 (11) ◽

pp. 1901-1906 ◽

Cited By ~ 4

Author(s):

Mary D Fortune ◽

Chris Wallace

Keyword(s):

Large Scale ◽

Simulated Data ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Supplementary Information ◽

Intermediate Step ◽

Fast Method ◽

Summary Statistics ◽

Causal Variants

Abstract Motivation Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some ‘truth’ is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. Results We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. Availability and implementation Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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simGWAS: a fast method for simulation of large scale case-control GWAS summarystatistics

10.1101/313023 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mary D. Fortune ◽

Chris Wallace

Keyword(s):

Large Scale ◽

Simulated Data ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Supplementary Information ◽

Intermediate Step ◽

Fast Method ◽

Summary Statistics ◽

Causal Variants

AbstractMotivationMethods for analysis of GWAS summary statistics have encouraged data sharing and democratised the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some “truth” is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study.ResultsWe have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis.Availability and ImplementationOur method is available under a GPL license as an R package from http://github.com/chr1swallace/[email protected] InformationSupplementary Information is appended.

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MOSim: Multi-Omics Simulation in R

10.1101/421834 ◽

2018 ◽

Cited By ~ 5

Author(s):

Carlos Martínez-Mira ◽

Ana Conesa ◽

Sonia Tarazona

Keyword(s):

Time Series Data ◽

Simulated Data ◽

R Package ◽

Experimental Designs ◽

Supplementary Information ◽

Series Data ◽

Data Sets ◽

Expression Data ◽

Supplementary Material ◽

Omic Data

AbstractMotivationAs new integrative methodologies are being developed to analyse multi-omic experiments, validation strategies are required for benchmarking. In silico approaches such as simulated data are popular as they are fast and cheap. However, few tools are available for creating synthetic multi-omic data sets.ResultsMOSim is a new R package for easily simulating multi-omic experiments consisting of gene expression data, other regulatory omics and the regulatory relationships between them. MOSim supports different experimental designs including time series data.AvailabilityThe package is freely available under the GPL-3 license from the Bitbucket repository (https://bitbucket.org/ConesaLab/mosim/)[email protected] informationSupplementary material is available at bioRxiv online.

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RMTL: an R library for multi-task learning

Bioinformatics ◽

10.1093/bioinformatics/bty831 ◽

2018 ◽

Vol 35 (10) ◽

pp. 1797-1798 ◽

Cited By ~ 2

Author(s):

Han Cao ◽

Jiayu Zhou ◽

Emanuel Schwarz

Keyword(s):

Biological Networks ◽

Simulated Data ◽

R Package ◽

Low Rank ◽

Supplementary Information ◽

Supplementary Data ◽

Software Environment ◽

Machine Learning Technique ◽

Task Learning ◽

Learning Technique

Abstract Motivation Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research. Results We developed an efficient, easy-to-use R library for MTL (www.r-project.org) comprising 10 algorithms applicable for regression, classification, joint predictor selection, task clustering, low-rank learning and incorporation of biological networks. We demonstrate the utility of the algorithms using simulated data. Availability and implementation The RMTL package is an open source R package and is freely available at https://github.com/transbioZI/RMTL. RMTL will also be available on cran.r-project.org. Supplementary information Supplementary data are available at Bioinformatics online.

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Detecting and correcting misclassified sequences in the large-scale public databases

Bioinformatics ◽

10.1093/bioinformatics/btaa586 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4699-4705

Author(s):

Hamid Bagheri ◽

Andrew J Severin ◽

Hridesh Rajan

Keyword(s):

Large Scale ◽

Sequence Similarity ◽

Heuristic Method ◽

Simulated Data ◽

Supplementary Information ◽

Small Subset ◽

Taxonomic Assignment ◽

User Input ◽

Public Repositories ◽

Taxonomic Assignments

Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability and implementation Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online.

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Inferring cellular heterogeneity of associations from single cell genomics

Bioinformatics ◽

10.1093/bioinformatics/btaa151 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3466-3473

Author(s):

Maya Levy ◽

Amit Frishberg ◽

Irit Gat-Viks

Keyword(s):

Simulated Data ◽

R Package ◽

Biological Data ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Dynamic Changes ◽

Entire Cell ◽

Complete Set ◽

Cellular Phenotypes ◽

Cell Variation

Abstract Motivation Cell-to-cell variation has uncovered associations between cellular phenotypes. However, it remains challenging to address the cellular diversity of such associations. Results Here, we do not rely on the conventional assumption that the same association holds throughout the entire cell population. Instead, we assume that associations may exist in a certain subset of the cells. We developed CEllular Niche Association (CENA) to reliably predict pairwise associations together with the cell subsets in which the associations are detected. CENA does not rely on predefined subsets but only requires that the cells of each predicted subset would share a certain characteristic state. CENA may therefore reveal dynamic modulation of dependencies along cellular trajectories of temporally evolving states. Using simulated data, we show the advantage of CENA over existing methods and its scalability to a large number of cells. Application of CENA to real biological data demonstrates dynamic changes in associations that would be otherwise masked. Availability and implementation CENA is available as an R package at Github: https://github.com/mayalevy/CENA and is accompanied by a complete set of documentations and instructions. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

Bioinformatics ◽

10.1093/bioinformatics/btab683 ◽

2021 ◽

Author(s):

Zachary B Abrams ◽

Dwayne G Tally ◽

Lynne V Abruzzo ◽

Kevin R Coombes

Keyword(s):

Large Scale ◽

International System ◽

Genetic Data ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Computational Tools ◽

Text Format ◽

Karyotype Analyses ◽

Computational Analyses

Abstract Summary Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and Implementation Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. Supplementary information There is no supplementary data.

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genuMet: distinguish genuine untargeted metabolic features without quality control samples

10.1101/837260 ◽

2019 ◽

Cited By ~ 1

Author(s):

L Cao ◽

C Clish ◽

FB Hu ◽

MA Martínez-González ◽

C Razquin ◽

...

Keyword(s):

Quality Control ◽

Large Scale ◽

Control Method ◽

R Package ◽

True Positive Rate ◽

Supplementary Information ◽

True Negative ◽

True Negative Rate ◽

Positive Rate ◽

Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

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Mercator: An R Package for Visualization of Distance Matrices

10.1101/733261 ◽

2019 ◽

Author(s):

Zachary B. Abrams ◽

Caitlin E. Coombes ◽

Suli Li ◽

Kevin R. Coombes

Keyword(s):

Large Scale ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Data Types ◽

Vast Number ◽

Large Scale Data ◽

Scientific Disciplines ◽

Compare And Contrast ◽

Scale Data

AbstractSummaryUnsupervised data analysis in many scientific disciplines is based on calculating distances between observations and finding ways to visualize those distances. These kinds of unsupervised analyses help researchers uncover patterns in large-scale data sets. However, researchers can select from a vast number of different distance metrics, each designed to highlight different aspects of different data types. There are also numerous visualization methods with their own strengths and weaknesses. To help researchers perform unsupervised analyses, we developed the Mercator R package. Mercator enables users to see important patterns in their data by generating multiple visualizations using different standard algorithms, making it particularly easy to compare and contrast the results arising from different metrics. By allowing users to select the distance metric that best fits their needs, Mercator helps researchers perform unsupervised analyses that use pattern identification through computation and visual inspection.Availability and ImplementationMercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html)[email protected] informationSupplementary data are available at Bioinformatics online.

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Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features

Bioinformatics ◽

10.1093/bioinformatics/btz010 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3143-3145

Author(s):

Kevin Matlock ◽

Raziur Rahman ◽

Souparno Ghosh ◽

Ranadip Pal

Keyword(s):

Information Integration ◽

Cancer Cell Line ◽

R Package ◽

Heterogeneous Data ◽

Supplementary Information ◽

Genomic Feature ◽

Modeling Framework ◽

Building Models ◽

Drug Sensitivity Prediction ◽

Sequential Addition

Abstract Summary Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples. Availability and implementation The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further explanation of the package is available. Supplementary information Supplementary data are available at Bioinformatics online.

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