Large Heterogeneous Datasets Research Articles

Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies

BackgroundThere is an increasing recognition of the need for the data capture phase of clinical studies to be improved and for more effective sharing of clinical data. The Health Care and Life Sciences community has embraced semantic technologies to facilitate the integration of health data from electronic health records, clinical studies and pharmaceutical research. This paper explores the integration of clinical study data exchange standards and semantic statistic vocabularies to deliver clinical data as linked data in a format that is easier to enrich with links to complementary data sources and consume by a broad user base.MethodsWe propose a Linked Clinical Data Cube (LCDC), which combines the strength of the RDF Data Cube and DDI-RDF vocabulary to enrich clinical data based on the CDISC standards. The CDISC standards provide the mechanisms for the data to be standardised, made more accessible and accountable whereas the RDF Data Cube and DDI-RDF vocabularies provide novel approaches to managing large volumes of heterogeneous linked data resources.ResultsWe validate our approach using a large-scale longitudinal clinical study into neurodegenerative diseases. This dataset, comprising more than 1600 variables clustered in 25 different sub-domains, has been fully converted into RDF forming one main data cube and one specialised cube for each sub-domain. One sub-domain, the Medications specialised cube, has been linked to relevant external vocabularies, such as the Australian Medicines Terminology and the ATC DDD taxonomy and DrugBank terminology. This provides new dimensions on which to query the data that promote the exploration of drug-drug and drug-disease interactions.ConclusionsThis implementation highlights the effectiveness of the association of the semantic statistics vocabularies for the publication of large heterogeneous data sets as linked data and the integration of the semantic statistics vocabularies with the CDISC standards. In particular, it demonstrates the potential of the two vocabularies in overcoming the monolithic nature of the underlying model and improving the navigation and querying of the data from multiple angles to support richer data analysis of clinical study data. The forecasted benefits are more efficient use of clinicians’ time and the potential to facilitate cross-study analysis.

Associations across spatial patterns of disease incidences, socio-demographics, and land use in Finland 1991-2010.

The epidemiological aim was to draw a general picture of spatial patterns of diseases, socio-demographics, and land use in Finland to detect possible under-recognized associations between the patterns. The methodological purpose was to compare and combine two statistical techniques to approach the data from different viewpoints. Two different statistical methods, the self-organizing map and principal coordinates of neighbor matrices with variation partitioning, were used to search for spatial patterns of 15 non-infectious diseases and 17 direct or indirect risk factors. The dataset was gathered from five Finnish registries and pooled over the years 1991-2010. The statistical unit in the analyses was a municipality (n=303). Variables referring to urban living were related to low incidences of all other diseases but cancer, whereas variables referring to rural living were related to low incidences of cancer and high incidences of other diseases, especially coronary heart disease (CHD), hypertension, diabetes, asthma/chronic obstructive pulmonary disease, and serious mental illnesses at the municipal level. The relationships between diseases other than cancer and risk factors related to socio-demographics and land use variables were stronger than those between cancer and risk factors. The structuration of spatial patterns was dominated by CHD together with land use features and unemployment rate. The relationship between unemployment and spatial health inequalities was emphasized. On the basis of the present study, it is suggested that large heterogeneous datasets are clustered and analyzed simultaneously with more than one statistical method to recognize the most significant and generalizable results.

The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis.

ObjectiveChildhood arthritis encompasses a heterogeneous family of diseases. Significant variation in clinical presentation remains despite consensus-driven diagnostic classifications. Developments in data analysis provide powerful tools for interrogating large heterogeneous data sets. We report a novel approach to integrating biologic and clinical data toward a new classification for childhood arthritis, using computational biology for data-driven pattern recognition.MethodsProbabilistic principal components analysis was used to transform a large set of data into 4 interpretable indicators or composite variables on which patients were grouped by cluster analysis. Sensitivity analysis was conducted to determine key variables in determining indicators and cluster assignment. Results were validated against an independent validation cohort.ResultsMeaningful biologic and clinical characteristics, including levels of proinflammatory cytokines and measures of disease activity, defined axes/indicators that identified homogeneous patient subgroups by cluster analysis. The new patient classifications resolved major differences between patient subpopulations better than International League of Associations for Rheumatology subtypes. Fourteen variables were identified by sensitivity analysis to crucially determine indicators and clusters. This new schema was conserved in an independent validation cohort.ConclusionData-driven unsupervised machine learning is a powerful approach for interrogating clinical and biologic data toward disease classification, providing insight into the biology underlying clinical heterogeneity in childhood arthritis. Our analytical framework enabled the recovery of unique patterns from small cohorts and addresses a major challenge, patient numbers, in studying rare diseases.

A framework for generalized subspace pattern mining in high-dimensional datasets.

BackgroundA generalized notion of biclustering involves the identification of patterns across subspaces within a data matrix. This approach is particularly well-suited to analysis of heterogeneous molecular biology datasets, such as those collected from populations of cancer patients. Different definitions of biclusters will offer different opportunities to discover information from datasets, making it pertinent to tailor the desired patterns to the intended application. This paper introduces ‘GABi’, a customizable framework for subspace pattern mining suited to large heterogeneous datasets. Most existing biclustering algorithms discover biclusters of only a few distinct structures. However, by enabling definition of arbitrary bicluster models, the GABi framework enables the application of biclustering to tasks for which no existing algorithm could be used.ResultsFirst, a series of artificial datasets were constructed to represent three clearly distinct scenarios for applying biclustering. With a bicluster model created for each distinct scenario, GABi is shown to recover the correct solutions more effectively than a panel of alternative approaches, where the bicluster model may not reflect the structure of the desired solution. Secondly, the GABi framework is used to integrate clinical outcome data with an ovarian cancer DNA methylation dataset, leading to the discovery that widespread dysregulation of DNA methylation associates with poor patient prognosis, a result that has not previously been reported. This illustrates a further benefit of the flexible bicluster definition of GABi, which is that it enables incorporation of multiple sources of data, with each data source treated in a specific manner, leading to a means of intelligent integrated subspace pattern mining across multiple datasets.ConclusionsThe GABi framework enables discovery of biologically relevant patterns of any specified structure from large collections of genomic data. An R implementation of the GABi framework is available through CRAN (http://cran.r-project.org/web/packages/GABi/index.html).Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0355-5) contains supplementary material, which is available to authorized users.

Gene coexpression measures in large heterogeneous samples using count statistics

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

Two-Dimensional Contribution Map for Fault Identification [Focus on Education

All control engineers should be able to detect and iden-tify faults (that is, abnormal conditions in a system) from the analysis of large heterogeneous time-series data sets. This Focus on Education column provides an introduction to multivariable data-based methods for fault detection and fault identification, with the latter being the determination of system variables that contribute the most to a detected fault. For fault identification in statistical process monitoring, the contribution plot is the most com-monly used tool for quickly identifying the most affected variables. Contribution calculations are revisited in the context of principal component analysis (PCA) and T2 statistics, and a two-dimensional (2-D) contribution map is illustrated for the examination of time-series data under faulty conditions. The 2-D contribution map is compared to the traditional one-dimensional (1-D) contribution plot using simulated data from a realistic chemical process. The 2-D contribution map demonstrates the potential to enable a greater understanding of the fault and how its effects are propagated through the system.

User-centered design and evaluation of overview components for semantic data exploration

Purpose – The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach – The Visual Information-Seeking Mantra “Overview first, zoom and filter, then details-on-demand” proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings – The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value – Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Adaptive Color Constancy Using Faces.

In this work we design an adaptive color constancy algorithm that, exploiting the skin regions found in faces, is able to estimate and correct the scene illumination. The algorithm automatically switches from global to spatially varying color correction on the basis of the illuminant estimations on the different faces detected in the image. An extensive comparison with both global and local color constancy algorithms is carried out to validate the effectiveness of the proposed algorithm in terms of both statistical and perceptual significance on a large heterogeneous data set of RAW images containing faces.

Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices)--one based solely on topological considerations without discriminating atom or bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Integrative study ofArabidopsis thalianametabolomic and transcriptomic data with the interactive MarVis-Graph software

State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

Electromagnetism-like algorithm for support vector machine parameter tuning

This paper introduces an electromagnetism-like (EM) approach for solving the problem of parameter tuning in the support vector machine (SVM). The proposed method is used to tune binary SVM classifiers in single and multiple kernel mode. The internal kernel structure is based on linear and radial basis functions (RBF). An appropriate encoding scheme of EM enables easy transformation of real-valued EM points directly to real-valued parameter combinations. Estimations of the generalization error based on the cross-validation and validation set error are used as objective functions. The efficient local search procedure uses variable size interval movement in order to improve the convergence of the method. The quality of the proposed method is tested on four collections of testing benchmarks through five separate experiments. The first three collections consist of small-size to medium-size classification data sets with up to 60 features and 1,300 training vectors, while the fourth collection is formed of large heterogeneous data sets with up to 1,554 features and 2,186 training vectors. The obtained results indicate that EM outperforms the comparison algorithms in 10 out of 13 instances from the first collection, 5 out of 5 instances from the second, and 13 out of 15 instances from the third collection. The last two experiments, conducted on the fourth collection, show that the proposed method outperforms 14 successful methods in 3 out of 5 data sets where RBF multiple kernel learning is used, and behaves competitively in cases when linear kernels are used.

Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation

SummaryThe analysis of large heterogeneous data sets of avian point‐count surveys compiled across studies is hindered by a lack of analytical approaches that can deal with detectability and variation in survey protocols.We reformulated removal models of avian singing rates and distance sampling models of the effective detection radius (EDR) to control for the effects of survey protocol and temporal and environmental covariates on detection probabilities.We estimated singing rates andEDRfor 75 boreal forest songbird species and found that survey protocol, especially point‐count radius, explained most of the variation in detectability. However, environmental and temporal covariates (date, time, vegetation) affected singing rates andEDRfor 73% and 59% of species, respectively.Unadjusted survey counts increased by an average of 201% from a 5‐min, 50‐m radius survey to a 10‐min, 100‐m radius survey (n= 75 species). This variability was decreased to 8·5% using detection probabilities estimated from a combination of removal and distance sampling models.Our modelling approach reduced computation when fitting complex models to large data sets and can be used with a wide range of statistical techniques for inference and prediction of avian densities.

CONCEPTS AND TECHNOLOGIES FOR A COMPREHENSIVE INFORMATION SYSTEM FOR HISTORICAL RESEARCH AND HERITAGE DOCUMENTATION

Abstract. Information systems play an important role in historical research as well as in heritage documentation. As part of a joint research project of the German Archaeological Institute, the Brandenburg University of Technology Cottbus and the Dresden University of Applied Sciences a web-based documentation system is currently being developed, which can easily be adapted to the needs of different projects with individual scientific concepts, methods and questions. Based on open source and standardized technologies it will focus on open and well-documented interfaces to ease the dissemination and re-use of its content via web-services and to communicate with desktop applications for further evaluation and analysis. Core of the system is a generic data model that represents a wide range of topics and methods of archaeological work. By the provision of a concerted amount of initial themes and attributes a cross project analysis of research data will be possible. The development of enhanced search and retrieval functionalities will simplify the processing and handling of large heterogeneous data sets. To achieve a high degree of interoperability with existing external data, systems and applications, standardized interfaces will be integrated. The analysis of spatial data shall be possible through the integration of web-based GIS functions. As an extension to this, customized functions for storage, processing and provision of 3D geo data are being developed. As part of the contribution system requirements and concepts will be presented and discussed. A particular focus will be on introducing the generic data model and the derived database schema. The research work on enhanced search and retrieval capabilities will be illustrated by prototypical developments, as well as concepts and first implementations for an integrated 2D/3D Web-GIS.

Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis

Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

Combining SFCscore with Random Forests leads to improved affinity prediction for protein-ligand complexes

SFCscore is a collection of emprirical scoring functions derived from a set of over 60 descriptors for protein-ligand complexes of known structure [1]. By the time of their derivation, SFCscore functions were the best-performing scoring functions tested on large heterogeneous data sets, but the overall correlation was still not within the desired range. Similarly, despite the ever increasing amount of structure and affinity data, the general advancements in the development of empirical scoring functions have been rather moderate over the past years. However, more recently, Ballester and Mitchell [2] published a function that outperformed current state-of-the-art scoring functions when tested against the PDBbind benchmark set [3]. This function uses relatively simple atom contact counts as descriptors and is derived by the Random Forest algorithm. Here, we present a study in which we used Random Forests to derive a new function based on the SFCscore descriptors as input data. Although this is not a fully non-parametric approach, the descriptors are supposed to capture more accurately the physically relevant interactions. We tested the new function against the PDBbind benchmark set and the CSAR-NRC HiQ 2010 set [4] and, in addition, performed the Leave-Cluster-Out validation as proposed by Kramer and Gedeck for the PDBbind set [5]. The results suggest that the new function significantly improves the predictive power of SFCscore, as it increases the correlation between predicted and experimentally determined affinities for the PDBbind benchmark set from r2 = 0.41 (best previous SFCscore function) to r2 = 0.61 (SFCscoreRF) and for the CSAR data set from r2 = 0.38 to r2 = 0.53.

An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data

BackgroundThe procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates.ResultsVESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data.ConclusionsVESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php.

Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering

Key-words: boreal lake, chlorophyll, phytoplankton, Self-Organizing map, water temperature, zooplankton The Self-Organizing Map (SOM) proved to be the method of choice for analysing a large heterogeneous ecological dataset. In addition to distributing the data into clusters, the SOM enabled hunting for correlations between the data components. This revealed logical and plausible relationships between and within the environment and groups of organisms. The main conclusions derived from the results were: (i) the structure of early summer plankton community significantly differed from that of late summer community in Lake Pyhaselka and (ii) plankton community in late summer was characterized by two functional groups. The first group was formed mainly by phytoplankton, rotifers, and small cladocerans, such as Bosmina spp., and driven by water temperature. The second group was formed by small copepods and the abundant generalist herbivorous cladocerans Daphnia cristata and Limnosida frontosa, which, in turn, associated with chlorophylla concentration.Biomassesof Bosmina spp. and D. cristata showed decreasing monotonic trends during a 20-year study period supposedly due to oligotrophication. Versatile possibilities to cluster data and hunt for correlations between data components offered by the SOM decisively helped to reveal associations across the original variables and draw conclusions. The results would have been undetectable solely on the basis of unorganised values.

Modified Pure Radix Sort for Large Heterogeneous Data Set

We have proposed a Modified Pure Radix Sort for Large Heterogeneous Data Set. In this research paper we discuss the problems of radix sort, brief study of previous works of radix sort & present new modified pure radix sort algorithm for large heterogeneous data set. We try to optimize all related problems of radix sort through this algorithm. This algorithm works on the Technology of Distributed Computing which is implemented on the principal of divide & conquer method. I. Introduction Sorting is a computational building block of fundamental importance and is the most widely studied algorithmic problem. The importance of sorting has led to the design of efficient sorting algorithms for a variety of architectures. Many applications rely on the availability of efficient sorting routines as a basis for their own efficiency, while some algorithms can be conveniently phrased in terms of sorting. Radix sort is an algorithm that sorts numbers by processing individual digits. n numbers consisting of k digits each are sorted in O (n · k) time. Radix sort can either process digits of each number starting from the least significant digit (LSD) or the most significant digit (MSD). The LSD algorithm first sorts the list by the least significant digit while preserving their relative order using a stable sort. Then it sorts them by the next digit, and so on from the least significant to the most significant, ending up with a sorted list. While the LSD radix sort requires the use of a stable sort, the MSD radix sort algorithm does not (unless stable sorting is desired). MSD radix sort is not stable. It is common for the counting sort algorithm to be used internally by the radix sort; Hybrid sorting approach, such as using insertion sort for small bins improves performance of radix sort significantly.