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Background: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. Results: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. Conclusion: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. Availability: A python package is available at: https://github.com/SayehSobhani/AFITBin. [ABSTRACT FROM AUTHOR]

Background: In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. Results: The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. Conclusions: The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus). [ABSTRACT FROM AUTHOR]

Background: Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques such as single-cell transcriptomics continue to drive the need for clustering or classification techniques to focus on portions of datasets of interest. While many libraries and packages exist that implement various algorithms, there remains the need for clustering packages that are easy to use, integrated with visualization of the results, and integrated with other commonly used tools for biological data analysis. clusterMaker2 has added several new algorithms, including two entirely new categories of analyses: node ranking and dimensionality reduction. Furthermore, many of the new algorithms have been implemented using the Cytoscape jobs API, which provides a mechanism for executing remote jobs from within Cytoscape. Together, these advances facilitate meaningful analyses of modern biological datasets despite their ever-increasing size and complexity. Results: The use of clusterMaker2 is exemplified by reanalyzing the yeast heat shock expression experiment that was included in our original paper; however, here we explored this dataset in significantly more detail. Combining this dataset with the yeast protein–protein interaction network from STRING, we were able to perform a variety of analyses and visualizations from within clusterMaker2, including Leiden clustering to break the entire network into smaller clusters, hierarchical clustering to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations between our hierarchical visualization and the UMAP plot, fuzzy clustering, and cluster ranking. Using these techniques, we were able to explore the highest-ranking cluster and determine that it represents a strong contender for proteins working together in response to heat shock. We found a series of clusters that, when re-explored as fuzzy clusters, provide a better presentation of mitochondrial processes. Conclusions: clusterMaker2 represents a significant advance over the previously published version, and most importantly, provides an easy-to-use tool to perform clustering and to visualize clusters within the Cytoscape network context. The new algorithms should be welcome to the large population of Cytoscape users, particularly the new dimensionality reduction and fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

Background: Cluster analysis is utilized frequently in scientific theory and applications to separate data into groups. A key assumption in many clustering algorithms is that the data was generated from a population consisting of multiple distinct clusters. Clusterability testing allows users to question the inherent assumption of latent cluster structure, a theoretical requirement for meaningful results in cluster analysis. Results: This paper proposes methods for clusterability testing designed for high-dimensional data by utilizing sparse principal component analysis. Type I error and power of the clusterability tests are evaluated using simulated data with different types of cluster structure in high dimensions. Empirical performance of the new methods is evaluated and compared with prior methods on gene expression, microarray, and shotgun proteomics data. Our methods had reasonably low Type I error and maintained power for many datasets with a variety of structures and dimensions. Cluster structure was not detectable in other datasets with spatially close clusters. Conclusion: This is the first analysis of clusterability testing on both simulated and real-world high-dimensional data. [ABSTRACT FROM AUTHOR]

Background: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods: In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results: Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion: We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods. [ABSTRACT FROM AUTHOR]

Background: This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph. Results: We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper. Conclusions: Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline. [ABSTRACT FROM AUTHOR]

Background: Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. Results: To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. Conclusion: Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes. [ABSTRACT FROM AUTHOR]

Background: Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed. Results: In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes. Conclusions: We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes. [ABSTRACT FROM AUTHOR]

Abstract Background Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. Results In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. Conclusion The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. Availability A python package is available at: https://github.com/SayehSobhani/AFITBin .

Background: DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality. Results: This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings. Conclusions: The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization. [ABSTRACT FROM AUTHOR]

Background: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. Results: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results—or partitions—on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html. Conclusions: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets. [ABSTRACT FROM AUTHOR]

Background: Recent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods. Results: This work investigates data transformation approaches used in single-cell clustering analysis tools and their effects on batch integration analysis. In particular, we compare 16 transformations and their impact on the low-dimensional representations, aiming to reduce the batch effect and integrate multiple single-cell sequencing data. Our results show that data transformations strongly influence the results of single-cell clustering on low-dimensional data space, such as those generated by UMAP or PCA. Moreover, these changes in low-dimensional space significantly affect trajectory analysis using multiple datasets, as well. However, the performance of the data transformations greatly varies across datasets, and the optimal method was different for each dataset. Additionally, we explored how data transformation impacts the analysis of deep feature encodings using deep neural network-based models, including autoencoder-based models and proto-typical networks. Data transformation also strongly affects the outcome of deep neural network models. Conclusions: Our findings suggest that the batch effect and noise in integrative analysis are highly influenced by data transformation. Low-dimensional features can integrate different batches well when proper data transformation is applied. Furthermore, we found that the batch mixing score on low-dimensional space can guide the selection of the optimal data transformation. In conclusion, data preprocessing is one of the most crucial analysis steps and needs to be cautiously considered in the integrative analysis of multiple scRNA-seq datasets. [ABSTRACT FROM AUTHOR]

Background: The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency. Results: In this paper, we propose a novel method called CaFew to select genes based on cluster-aware feature weighting. By optimizing the clustering objective function, CaFew obtains a feature weight matrix, which is further used for feature selection. The genes have large weights in at least one cluster or the genes whose weights vary greatly in different clusters are selected. Experiments on 8 real scRNA-seq datasets show that CaFew can obviously improve the clustering performance of existing scRNA-seq data clustering methods. Particularly, the combination of CaFew with SC3 achieves the state-of-art performance. Furthermore, CaFew also benefits the visualization of scRNA-seq data. Conclusion: CaFew is an effective scRNA-seq data clustering method due to its gene selection mechanism based on cluster-aware feature weighting, and it is a useful tool for scRNA-seq data analysis. [ABSTRACT FROM AUTHOR]

Background: Bisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale in both plants and animals. This is especially relevant for non-model organisms with poorly annotated genomes and/or organisms where genome sequences are not yet assembled on chromosome level. Despite being a technology of choice to profile DNA methylation for many years now there are surprisingly few lightweight and robust standalone tools available for efficient graphical analysis of data in non-model systems. This significantly limits evolutionary studies and agrigenomics research. BSXplorer is a tool specifically developed to fill this gap and assist researchers in explorative data analysis and in visualising and interpreting bisulfite sequencing data more easily. Results: BSXplorer provides in-depth graphical analysis of sequencing data encompassing (a) profiling of methylation levels in metagenes or in user-defined regions using line plots and heatmaps, generation of summary statistics charts, (b) enabling comparative analyses of methylation patterns across experimental samples, methylation contexts and species, and (c) identification of modules sharing similar methylation signatures at functional genomic elements. The tool processes methylation data quickly and offers API and CLI capabilities, along with the ability to create high-quality figures suitable for publication. Conclusions: BSXplorer facilitates efficient methylation data mining, contrasting and visualization, making it an easy-to-use package that is highly useful for epigenetic research. [ABSTRACT FROM AUTHOR]

Background: Detection of tandem duplication within coding exons, referred to as internal tandem duplication (ITD), remains challenging due to inefficiencies in alignment of ITD-containing reads to the reference genome. There is a critical need to develop efficient methods to recover these important mutational events. Results: In this paper we introduce ITD Assembler, a novel approach that rapidly evaluates all unmapped and partially mapped reads from whole exome NGS data using a De Bruijn graphs approach to select reads that harbor cycles of appropriate length, followed by assembly using overlap-layout-consensus. We tested ITD Assembler on The Cancer Genome Atlas AML dataset as a truth set. ITD Assembler identified the highest percentage of reported FLT3-ITDs when compared to other ITD detection algorithms, and discovered additional ITDs in FLT3, KIT, CEBPA, WT1 and other genes. Evidence of polymorphic ITDs in 54 genes were also found. Novel ITDs were validated by analyzing the corresponding RNA sequencing data. Conclusions: ITD Assembler is a very sensitive tool which can detect partial, large and complex tandem duplications. This study highlights the need to more effectively look for ITD's in other cancers and Mendelian diseases. [ABSTRACT FROM AUTHOR]

Background: In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application. Results: This paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis. Conclusions: Apart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease. [ABSTRACT FROM AUTHOR]

Background: DNA fingerprinting is a technique for comparing DNA patterns that has applications in a wide variety of contexts. Several commercial and freely-available tools can be used to analyze DNA fingerprint gel images; however, commercial tools are expensive and usually difficult to use; and, free tools support the basic functionality for DNA fingerprint analysis, but lack some instrumental features to obtain accurate results. Results: In this paper, we present GelJ, a feather-weight, user-friendly, platform-independent, open-source and free tool for analyzing DNA fingerprint gel images. Some of the outstanding features of GelJ are mechanisms for accurate lane- and band-detection, several options for computing migration models, a number of band- and curve-based similarity methods, different techniques for generating dendrograms, comparison of banding patterns from different experiments, and database support. Conclusions: GelJ is an easy to use tool for analyzing DNA fingerprint gel images. It combines the best characteristics of both free and commercial tools: GelJ is light and simple to use (as free programs), but it also includes the necessary features to obtain precise results (as commercial programs). In addition, GelJ incorporates new functionality that is not supported by any other tool. [ABSTRACT FROM AUTHOR]

Background: Previously, a seven-cluster pattern claiming to be a universal one in bacterial genomes has been reported. Keeping in mind the most popular theory of chloroplast origin, we checked whether a similar pattern is observed in chloroplast genomes. Results: Surprisingly, eight cluster structure has been found, for chloroplasts. The pattern observed for chloroplasts differs rather significantly, from bacterial one, and from that latter observed for cyanobacteria. The structure is provided by clustering of the fragments of equal length isolated within a genome so that each fragment is converted in triplet frequency dictionary with non-overlapping triplets with no gaps in frame tiling. The points in 63-dimensional space were clustered due to elastic map technique. The eight cluster found in chloroplasts comprises the fragments of a genome bearing tRNA genes and exhibiting excessively high GC-content, in comparison to the entire genome. Conclusion: Chloroplasts exhibit very specific symmetry type in distribution of coding and non-coding fragments of a genome in the space of triplet frequencies: this is mirror symmetry. Cyanobacteria may have both mirror symmetry, and the rotational symmetry typical for other bacteria. [ABSTRACT FROM AUTHOR]

Abstract Background In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. Results The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. Conclusions The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository ( https://github.com/yshen25/HLA-Clus ).

Abstract Background Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques such as single-cell transcriptomics continue to drive the need for clustering or classification techniques to focus on portions of datasets of interest. While many libraries and packages exist that implement various algorithms, there remains the need for clustering packages that are easy to use, integrated with visualization of the results, and integrated with other commonly used tools for biological data analysis. clusterMaker2 has added several new algorithms, including two entirely new categories of analyses: node ranking and dimensionality reduction. Furthermore, many of the new algorithms have been implemented using the Cytoscape jobs API, which provides a mechanism for executing remote jobs from within Cytoscape. Together, these advances facilitate meaningful analyses of modern biological datasets despite their ever-increasing size and complexity. Results The use of clusterMaker2 is exemplified by reanalyzing the yeast heat shock expression experiment that was included in our original paper; however, here we explored this dataset in significantly more detail. Combining this dataset with the yeast protein–protein interaction network from STRING, we were able to perform a variety of analyses and visualizations from within clusterMaker2, including Leiden clustering to break the entire network into smaller clusters, hierarchical clustering to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations between our hierarchical visualization and the UMAP plot, fuzzy clustering, and cluster ranking. Using these techniques, we were able to explore the highest-ranking cluster and determine that it represents a strong contender for proteins working together in response to heat shock. We found a series of clusters that, when re-explored as fuzzy clusters, provide a better presentation of mitochondrial processes. Conclusions clusterMaker2 represents a significant advance over the previously published version, and most importantly, provides an easy-to-use tool to perform clustering and to visualize clusters within the Cytoscape network context. The new algorithms should be welcome to the large population of Cytoscape users, particularly the new dimensionality reduction and fuzzy clustering techniques.

Background: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms. Results: In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain. Conclusions: In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis. [ABSTRACT FROM AUTHOR]

Abstract Background Cluster analysis is utilized frequently in scientific theory and applications to separate data into groups. A key assumption in many clustering algorithms is that the data was generated from a population consisting of multiple distinct clusters. Clusterability testing allows users to question the inherent assumption of latent cluster structure, a theoretical requirement for meaningful results in cluster analysis. Results This paper proposes methods for clusterability testing designed for high-dimensional data by utilizing sparse principal component analysis. Type I error and power of the clusterability tests are evaluated using simulated data with different types of cluster structure in high dimensions. Empirical performance of the new methods is evaluated and compared with prior methods on gene expression, microarray, and shotgun proteomics data. Our methods had reasonably low Type I error and maintained power for many datasets with a variety of structures and dimensions. Cluster structure was not detectable in other datasets with spatially close clusters. Conclusion This is the first analysis of clusterability testing on both simulated and real-world high-dimensional data.

Background: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. Results: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. Conclusion: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE. [ABSTRACT FROM AUTHOR]

Abstract Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

Background: Biclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets. Results: We conducted a broader comparative study involving seventeen algorithms, which were run on three synthetic data collections and two real data collections with a more representative number of datasets. For the experiments with synthetic data, five different experimental scenarios were studied: different levels of noise, different numbers of implanted biclusters, different levels of symmetric bicluster overlap, different levels of asymmetric bicluster overlap and different bicluster sizes, for which the results were assessed with more suitable external measures. For the experiments with real datasets, the results were assessed by gene set enrichment and clustering accuracy. Conclusions: We observed that each algorithm achieved satisfactory results in part of the biclustering tasks in which they were investigated. The choice of the best algorithm for some application thus depends on the task at hand and the types of patterns that one wants to detect. [ABSTRACT FROM AUTHOR]

Background: Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. Results: We present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components' signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis. Conclusion: robustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines. [ABSTRACT FROM AUTHOR]

Though proposing algorithmic approaches for protein domain decomposition has been of high interest, the inherent ambiguity to the problem makes it still an active area of research. Besides, accurate automated methods are in high demand as the number of solved structures for complex proteins is on the rise. While majority of the previous efforts for decomposition of 3D structures are centered on the developing clustering algorithms, employing enhanced measures of proximity between the amino acids has remained rather uncharted. If there exists a kernel function that in its reproducing kernel Hilbert space, structural domains of proteins become well separated, then protein structures can be parsed into domains without the need to use a complex clustering algorithm. Inspired by this idea, we developed a protein domain decomposition method based on diffusion kernels on protein graphs. We examined all combinations of four graph node kernels and two clustering algorithms to investigate their capability to decompose protein structures. The proposed method is tested on five of the most commonly used benchmark datasets for protein domain assignment plus a comprehensive non-redundant dataset. The results show a competitive performance of the method utilizing one of the diffusion kernels compared to four of the best automatic methods. Our method is also able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain. With a competitive accuracy and balanced performance for the simple and complex structures despite relying on a relatively naive criterion to choose optimal decomposition, the proposed method revealed that diffusion kernels on graphs in particular, and kernel functions in general are promising measures to facilitate parsing proteins into domains and performing different structural analysis on proteins. The size and interconnectedness of the protein graphs make them promising targets for diffusion kernels as measures of affinity between amino acids. The versatility of our method allows the implementation of future kernels with higher performance. The source code of the proposed method is accessible at https://github.com/taherimo/kludo. Also, the proposed method is available as a web application from https://cbph.ir/tools/kludo. [ABSTRACT FROM AUTHOR]

Abstract Background This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph. Results We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper. Conclusions Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline .

Abstract Background Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. Results To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. Conclusion Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations. [ABSTRACT FROM AUTHOR]

Abstract Background The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency. Results In this paper, we propose a novel method called CaFew to select genes based on cluster-aware feature weighting. By optimizing the clustering objective function, CaFew obtains a feature weight matrix, which is further used for feature selection. The genes have large weights in at least one cluster or the genes whose weights vary greatly in different clusters are selected. Experiments on 8 real scRNA-seq datasets show that CaFew can obviously improve the clustering performance of existing scRNA-seq data clustering methods. Particularly, the combination of CaFew with SC3 achieves the state-of-art performance. Furthermore, CaFew also benefits the visualization of scRNA-seq data. Conclusion CaFew is an effective scRNA-seq data clustering method due to its gene selection mechanism based on cluster-aware feature weighting, and it is a useful tool for scRNA-seq data analysis.

Background: Clustering of protein sequences is of key importance in predicting the structure and function of newly sequenced proteins and is also of use for their annotation. With the advent of multiple high-throughput sequencing technologies, new protein sequences are becoming available at an extraordinary rate. The rapid growth rate has impeded deployment of existing protein clustering/annotation tools which depend largely on pairwise sequence alignment. Results: In this paper, we propose an alignment-free clustering approach, coreClust, for annotating protein sequences using detected conserved regions. The proposed algorithm uses Min-Wise Independent Hashing for identifying similar conserved regions. Min-Wise Independent Hashing works by generating a (w,c)-sketch for each document and comparing these sketches. Our algorithm fits well within the MapReduce framework, permitting scalability. We show that coreClust generates results comparable to existing known methods. In particular, we show that the clusters generated by our algorithm capture the subfamilies of the Pfam domain families for which the sequences in a cluster have a similar domain architecture. We show that for a data set of 90,000 sequences (about 250,000 domain regions), the clusters generated by our algorithm give a 75% average weighted F1 score, our accuracy metric, when compared to the clusters generated by a semi-exhaustive pairwise alignment algorithm. Conclusions: The new clustering algorithm can be used to generate meaningful clusters of conserved regions. It is a scalable method that when paired with our prior work, NADDA for detecting conserved regions, provides a complete end-to-end pipeline for annotating protein sequences. [ABSTRACT FROM AUTHOR]