DTD: An R Package for Digital Tissue Deconvolution.
Ontology highlight
ABSTRACT:
SUBMITTER: Schon M
PROVIDER: S-EPMC7074920 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors-in-variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.
| S-EPMC3100171 | biostudies-literature
Project description:SummaryWe develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts.Availability and implementationhttp://bioconductor.org/packages/debCAM.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7320609 | biostudies-literature
Project description:NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity.
| S-EPMC8305572 | biostudies-literature
Project description:MotivationDeciphering molecular signals from omics data helps understanding cellular processes and disease progression. Effective algorithms for extracting these signals are essential, with a strong emphasis on robustness and reproducibility.ResultsR/Bioconductor package consICA implements consensus independent component analysis (ICA)-a data-driven deconvolution method to decompose heterogeneous omics data and extract features suitable for patient stratification and multimodal data integration. The method separates biologically relevant molecular signals from technical effects and provides information about the cellular composition and biological processes. Build-in annotation, survival analysis, and report generation provide useful tools for the interpretation of extracted signals. The implementation of parallel computing in the package ensures efficient analysis using modern multicore systems. The package offers a reproducible and efficient data-driven solution for the analysis of complex molecular profiles, with significant implications for cancer research.Availability and implementationThe package is implemented in R and available under MIT license at Bioconductor (https://bioconductor.org/packages/consICA) or at GitHub (https://github.com/biomod-lih/consICA).
| S-EPMC11257712 | biostudies-literature
Project description:Background: Developmental trauma disorder (DTD) has been proposed to describe the biopsychosocial sequelae of exposure to interpersonal victimization in childhood that extend beyond the symptoms of post-traumatic stress disorder (PTSD). Objective: To characterize the psychopathology comorbid with DTD and to determine whether this comorbidity is distinct from, and extends beyond, comorbidities of PTSD. Method: DTD was assessed by structured interview, and probable Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) psychiatric disorders were identified with screening modules on the Kiddie Schedule for Affective Disorders and Schizophrenia, Present/Lifetime version (K-SADS-PL), in a multi-site sample of 236 children (7-18 years old; 50% female) referred by paediatric or mental health providers. Results: DTD (N = 80, 34%) and PTSD (N = 69, 29%) were highly comorbid and shared several DSM-IV internalizing disorder and DSM, 5th Edition (DSM-5) dysregulation disorder comorbidities. However, DTD, but not PTSD, was associated with comorbid panic disorder and disruptive behaviour disorders. On a multivariate basis including all probable DSM-IV disorders and DSM-5 dysregulation disorders, DTD was associated with separation anxiety disorder and attention deficit hyperactivity disorder after controlling for PTSD, while PTSD was associated with major depression and generalized anxiety disorder after controlling for DTD. Conclusions: DTD's comorbidities overlap with but extend beyond those of PTSD to include panic, separation anxiety, and disruptive behaviour disorders. DTD warrants further investigation as a potential diagnosis or a complex variant of PTSD in children, similar to the adult symptoms of disturbances of self-organization in the proposed International Classification of Diseases, 11th revision (ICD-11) complex post-traumatic stress disorder subtype.
| S-EPMC6352932 | biostudies-literature
Project description:MotivationGlobal expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.ResultsWe propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository.AvailabilityR code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.
| S-EPMC2853690 | biostudies-literature
Project description:SummaryWe develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data.Availability and implementationUNDO is available at http://bioconductor.org/packages.
| S-EPMC4271149 | biostudies-literature
Project description:BackgroundThe large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolution methods are used to determine the composition of cancer subpopulations in the biopsy sample, a fundamental step to determine clonal expansions and their evolutionary trajectories.ResultsIn a recent work we have developed a new model-based approach to carry out subclonal deconvolution from the site frequency spectrum of somatic mutations. This new method integrates, for the first time, an explicit model for neutral evolutionary forces that participate in clonal expansions; in that work we have also shown that our method improves largely over competing data-driven methods. In this Software paper we present mobster, an open source R package built around our new deconvolution approach, which provides several functions to plot data and fit models, assess their confidence and compute further evolutionary analyses that relate to subclonal deconvolution.ConclusionsWe present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing neutral and positive selection in cancer. We showcase the analysis of two datasets, one simulated and one from a breast cancer patient, and overview all package functionalities.
| S-EPMC7672894 | biostudies-literature
Project description:A digital filter derived from linear discriminant analysis (LDA) is developed for recovering impulse responses in photon counting from a high speed photodetector (rise time of ~1 ns) and applied to remove ringing distortions from impedance mismatch in multiphoton fluorescence microscopy. Training of the digital filter was achieved by defining temporally coincident and noncoincident transients and identifying the projection within filter-space that best separated the two classes. Once trained, data analysis by digital filtering can be performed quickly. Assessment of the reliability of the approach was performed through comparisons of simulated voltage transients, in which the ground truth results were known a priori. The LDA filter was also found to recover deconvolved impulses for single photon counting from highly distorted ringing waveforms from an impedance mismatched photomultiplier tube. The LDA filter was successful in removing these ringing distortions from two-photon excited fluorescence micrographs and through data simulations was found to extend the dynamic range of photon counting by approximately 3 orders of magnitude through minimization of detector paralysis.
| S-EPMC3983021 | biostudies-other
Project description:Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
| S-EPMC6863530 | biostudies-literature