Decision Trees For Differential Diagnosis Pdf Creator
Flow cytometry bioinformatics is the application of to data, which involves storing, retrieving, organizing and analyzing flow cytometry data using extensive computational resources and tools.Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from and.Flow cytometry and related methods allow the quantification of multiple independent on large numbers of single. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results.Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. Representation of flow cytometry data from an instrument with three scatter channels and 13 fluorescent channels. Only the values for the first 30 (of hundreds of thousands) of cells are shown.Flow cytometry data is in the form of a large matrix of intensities over M wavelengths by N events. Most events will be a particular cell, although some may be doublets (pairs of cells which pass the laser closely together). For each event, the measured fluorescence intensity over a particular wavelength range is recorded.The measured fluorescence intensity indicates the amount of that fluorophore in the cell, which indicates the amount that has bound to detector molecules such as antibodies. Therefore, fluorescence intensity can be considered a proxy for the amount of detector molecules present on the cell.
A simplified, if not strictly accurate, way of considering flow cytometry data is as a matrix of M measurements of amounts of molecules of interest by N cells.Steps in computational flow cytometry data analysis. An example pipeline for analysis of FCM data and some of the Bioconductor packages relevant to each step.The process of moving from primary FCM data to disease diagnosis and biomarker discovery involves four major steps:.
Data pre-processing (including compensation, transformation and normalization). Cell population identification (a.k.a. Two-dimensional scatter plots covering all three combinations of three chosen dimensions.
The colours show the comparison of consensus of eight independent manual gates (polygons) and automated gates (colored dots). The consensus of the manual gates and the algorithms were produced using the CLUE package. Figure reproduced from.The complexity of raw flow cytometry data (dozens of measurements for thousands to millions of cells) makes answering questions directly using statistical tests or supervised learning difficult.
Thus, a critical step in the analysis of flow cytometric data is to reduce this complexity to something more tractable while establishing common features across samples. This usually involves identifying multidimensional regions that contain functionally and phenotypically homogeneous groups of cells. This is a form of.
Process Of Differential Diagnosis
There are a range of methods by which this can be achieved, detailed below.Gating The data generated by flow-cytometers can be plotted in one or two to produce a or scatter plot. The regions on these plots can be sequentially separated, based on fluorescence, by creating a series of subset extractions, termed '. These gates can be produced using software, e.g.
Flowjo, FCS Express, WinMDI, CytoPaint (aka Paint-A-Gate), VenturiOne, CellQuest Pro, Cytospec, Kaluza. Or flowCore.In datasets with a low number of dimensions and limited cross-sample technical and biological variability (e.g., clinical laboratories), manual analysis of specific cell populations can produce effective and reproducible results. However, exploratory analysis of a large number of cell populations in a high-dimensional dataset is not feasible.
Bmg entertainment case pdf. In addition, manual analysis in less controlled settings (e.g., cross-laboratory studies) can increase the overall error rate of the study. In one study, several computational gating algorithms performed better than manual analysis in the presence of some variation.
However, despite the considerable advances in computational analysis, manual gating remains the main solution for the identification of specific rare cell populations that are not well-separated from other cell types.Gating guided by dimension reduction The number of scatter plots that need to be investigated increases with the square of the number of markers measured (or faster since some markers need to be investigated several times for each group of cells to resolve high-dimensional differences between cell types that appear to be similar in most markers). To address this issue, has been used to summarize the high-dimensional datasets using a combination of markers that maximizes the variance of all data points.
However, PCA is a linear method and is not able to preserve complex and non-linear relationships. More recently, two dimensional layouts have been used to guide the manual gating process. Density-based down-sampling and clustering was used to better represent rare populations and control the time and memory complexity of the minimum spanning tree construction process. More sophisticated algorithms are yet to be investigated. Cell populations in a high-dimensional mass-cytometry dataset manually gated after dimension reduction using 2D layout for a minimum spanning tree. Figure reproduced from the data provided in. Automated gating Developing computational tools for identification of cell populations has been an area of active research only since 2008.
Many individual approaches have recently been developed, including model-based algorithms (e.g., flowClust and FLAME ), density based algorithms (e.g. FLOCK and SWIFT, graph-based approaches (e.g. SamSPECTRAL ) and most recently, hybrids of several approaches (flowMeans and flowPeaks ). These algorithms are different in terms of memory and time complexity, their software requirements, their ability to automatically determine the required number of cell populations, and their sensitivity and specificity. The FlowCAP (Flow Cytometry: Critical Assessment of Population Identification Methods) project, with active participation from most academic groups with research efforts in the area, is providing a way to objectively cross-compare state-of-the-art automated analysis approaches.Other surveys have also compared automated gating tools on several datasets. Probability binning methods. An example of frequency difference gating, created using the flowFP Bioconductor package.
The dots represent individual events in an FCS file. Overview of the flowType/RchyOptimyx pipeline for identification of correlates of protection against HIV: First, tens of thousands of cell populations are identified by combining one-dimensional partitions (panel one). The cell populations are then analyzed using a statistical test (and bonferroni's method for multiple testing correction) to identify those correlated with the survival information. The third panel shows a complete gating hierarchy describing all possible strategies for gating that cell population. This graph can be mined to identify the 'best' gating strategy (i.e., the one in which the most important markers appear earlier).
These hierarchies for all selected phenotypes are demonstrated in panel 4. In panel 5, these hierarchies are merged into a single graph that summarized the entire dataset and demonstrates the trade-off between the number of markers involved in each phenotype and the significance of the correlation with the clinical outcome (e.g., as measured by the in panel 6). Figure reproduced in part from and.After identification of the cell population of interest, a cross sample analysis can be performed to identify phenotypical or functional variations that are correlated with an external variable (e.g., a clinical outcome). These studies can be partitioned into two main groups:Diagnosis In these studies, the goal usually is to diagnose a disease (or a sub-class of a disease) using variations in one or more cell populations. For example, one can use multidimensional clustering to identify a set of clusters, match them across all samples, and then use to construct a classifier for prediction of the classes of interest (e.g., this approach can be used to improve the accuracy of the classification of specific lymphoma subtypes ).
Alternatively, all the cells from the entire cohort can be pooled into a single multidimensional space for clustering before classification. This approach is particularly suitable for datasets with a high amount of biological variation (in which cross-sample matching is challenging) but requires technical variations to be carefully controlled.
Discovery In a discovery setting, the goal is to identify and describe cell populations correlated with an external variable (as opposed to the diagnosis setting in which the goal is to combine the predictive power of multiple cell types to maximize the accuracy of the results). Similar to the diagnosis use-case, cluster matching in high-dimensional space can be used for exploratory analysis but the descriptive power of this approach is very limited, as it is hard to characterize and visualize a cell population in a high-dimensional space without first reducing the dimensionality. Finally, combinatorial gating approaches have been particularly successful in exploratory analysis of FCM data.
Simplified Presentation of Incredibly Complex Evaluations (SPICE) is a software package that can use the gating functionality of FlowJo to statistically evaluate a wide range of different cell populations and visualize those that are correlated with the external outcome. FlowType and RchyOptimyx (as discussed above) expand this technique by adding the ability of exploring the impact of independent markers on the overall correlation with the external outcome. This enables the removal of unnecessary markers and provides a simple visualization of all identified cell types. In a recent analysis of a large (n=466) cohort of HIV+ patients, this pipeline identified three correlates of protection against HIV, only one of which had been previously identified through extensive manual analysis of the same dataset. Data formats and interchange Flow Cytometry Standard. Main article:Flow Cytometry Standard (FCS) was developed in 1984 to allow recording and sharing of flow cytometry data.
Since then, FCS became the standard supported by all flow cytometry software and hardware vendors. The FCS specification has traditionally been developed and maintained by the International Society for Advancement of Cytometry (ISAC). Over the years, updates were incorporated to adapt to technological advancements in both flow cytometry and computing technologies with FCS 2.0 introduced in 1990, FCS 3.0 in 1997, and the most current specification FCS 3.1 in 2010.
FCS used to be the only widely adopted file format in flow cytometry. Recently, additional standard file formats have been developed by ISAC.netCDF ISAC is considering replacing FCS with a flow cytometry specific version of the (netCDF) file format.netCDF is a set of freely available software libraries and machine independent data formats that support the creation, access, and sharing of array-oriented scientific data. In 2008, ISAC drafted the first version of netCDF conventions for storage of raw flow cytometry data. Archival Cytometry Standard (ACS) The Archival Cytometry Standard (ACS) is being developed to bundle data with different components describing cytometry experiments. It captures relations among data, metadata, analysis files and other components, and includes support for audit trails, versioning and digital signatures.
The ACS container is based on the with an -based table of contents specifying relations among files in the container. The Recommendation has been adopted to allow for digital signatures of components within the ACS container.An initial draft of ACS has been designed in 2007 and finalized in 2010. Since then, ACS support has been introduced in several software tools including FlowJo and Cytobank.Gating-ML The lack of gating interoperability has traditionally been a bottleneck preventing reproducibility of flow cytometry data analysis and the usage of multiple analytical tools. To address this shortcoming, ISAC developed Gating-ML, an XML-based mechanism to formally describe gates and related data (scale) transformations.The draft recommendation version of Gating-ML was approved by ISAC in 2008 and it is partially supported by tools like FlowJo, the flowUtils, CytoML libraries in R/BioConductor, and FlowRepository. It supports rectangular gates, polygon gates, convex polytopes, ellipsoids, decision trees and Boolean collections of any of the other types of gates.
In addition, it includes dozens of built in public transformations that have been shown to potentially useful for display or analysis of cytometry data. In 2013, Gating-ML version 2.0 was approved by ISAC's Data Standards Task Force as a Recommendation. This new version offers slightly less flexibility in terms of the power of gating description; however, it is also significantly easier to implement in software tools. Classification Results (CLR) The Classification Results (CLR) File Format has been developed to exchange the results of manual gating and algorithmic classification approaches in a standard way in order to be able to report and process the classification. CLR is based in the commonly supported with columns corresponding to different classes and cell values containing the probability of an event being a member of a particular class. These are captured as values between 0 and 1.
Simplicity of the format and its compatibility with common spreadsheet tools have been the major requirements driving the design of the specification. Although it was originally designed for the field of flow cytometry, it is applicable in any domain that needs to capture either fuzzy or unambiguous classifications of virtually any kinds of objects.Public data and software As in other bioinformatics fields, development of new methods has primarily taken the form of, and several databases have been created for depositing.Bioconductor. Main article:GenePattern is a predominantly genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research. Recently, a GenePattern Flow Cytometry Suite has been developed in order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills. It contains close to 40 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment.
Internally, most of these modules leverage functionality developed in BioConductor.Much of the functionality of the Bioconductor packages for flow cytometry analysis has been packaged up for use with the GenePattern, in the form of the GenePattern Flow Cytometry Suite. FACSanadu FACSanadu is an open source portable application for visualization and analysis of FCS data. Unlike Bioconductor, it is an interactive program aimed at non-programmers for routine analysis.
The American Psychiatric Association (APA) has updated its, including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.Please read the entire. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy, including the utilization of cookies. The DSM-5® Handbook of Differential Diagnosis is the preeminent guide to differential diagnosis for both clinicians and students learning psychiatric diagnosis. Based closely on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, it offers a rich selection of perspectives in an easy-to-use format. The author, an expert on psychiatric diagnosis and assessment, recognizes that psychological distress cannot be reduced to a rubric.
The clinician must have empathy, listening skills, the ability to identify symptoms and contextualize them, and a familiarity with the body of knowledge represented by DSM-5 ®.