Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery ...settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.
We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry.
The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool ...amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.
We tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., "events" in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.
This work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Adult hematopoietic stem cells (HSCs) with serially transplantable activity comprise two subtypes. One shows a balanced output of mature lymphoid and myeloid cells; the other appears selectively ...lymphoid deficient. We now show that both of these HSC subtypes are present in the fetal liver (at a 1:10 ratio) with the rarer, lymphoid-deficient HSCs immediately gaining an increased representation in the fetal bone marrow, suggesting that the marrow niche plays a key role in regulating their ensuing preferential amplification. Clonal analysis of HSC expansion posttransplant showed that both subtypes display an extensive but variable self-renewal activity with occasional interconversion. Clonal analysis of their differentiation programs demonstrated functional and molecular as well as quantitative HSC subtype-specific differences in the lymphoid progenitors they generate but an indistinguishable production of multipotent and myeloid-restricted progenitors. These findings establish a level of heterogeneity in HSC differentiation and expansion control that may have relevance to stem cell populations in other hierarchically organized tissues.
► HSC subtypes differ in their ability to complete lymphoid differentiation programs ► All HSC subtypes appear early in development but later expand at different rates ► Lymphoid-deficient HSCs expand preferentially after the fetal marrow is colonized ► Individual HSCs can generate different HSC subtypes
As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis ...and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.
In view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.
flowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
...even though the vast majority of the computational tools are open source and thus freely available, most do not have user-friendly interfaces, limiting their use to investigators with programming ...expertise. ...the OpenCyto framework provides open-source tools for analyzing FCM data within an extensible and flexible interface to simplify the construction of re-usable FCM workflows while facilitating comparative analysis against manually gated results in order to enhance user confidence (Finak et al., 2014).
Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high ...number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R‐based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.
The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant ...information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values. flowSim clustering compared to consensus manual clustering on a dataset composed of 160 images of bivariate FCM data had a mean Adjusted Rand Index of 0.90, demonstrating its efficiency in identifying similar patterns. flowSim selectively discarded near duplicate files in datasets constructed with known redundancy, and removed 92.6% of FCM images in a dataset of over 500,000 drawn from public repositories.
flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating ...approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript.
R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW).
rbrinkman@bccrc.ca
Supplementary data are available at Bioinformatics online.
Previous studies demonstrated that imatinib mesylate (IM) induces autophagy in chronic myeloid leukemia (CML) and that this process is critical to cell survival upon therapy. However, it is not known ...if the autophagic process differs at basal levels between CML patients and healthy individuals and if pretreatment CML cells harbor unique autophagy characteristics that could predict patients’ clinical outcomes. We now demonstrate that several key autophagy genes are differentially expressed in CD34+ hematopoietic stem/progenitor cells, with the highest transcript levels detected for ATG4B, and that the transcript and protein expression levels of ATG4 family members, ATG5 and BECLIN-1 are significantly increased in CD34+ cells from chronic-phase CML patients (P < .05). Importantly, ATG4B is differentially expressed in pretreatment CML stem/progenitor cells from subsequent IM responders vs IM nonresponders (P < .05). Knockdown of ATG4B suppresses autophagy, impairs the survival of CML stem/progenitor cells and sensitizes them to IM treatment. Moreover, deregulated expression of ATG4B in CD34+ CML cells inversely correlates with transcript levels of miR-34a, and ATG4B is shown to be a direct target of miR-34a. This study identifies ATG4B as a potential biomarker for predicting therapeutic response in treatment-naïve CML stem/progenitor cells and uncovers ATG4B as a possible drug target in these cells.
•The core autophagy protein ATG4B is highly expressed in CML stem/progenitor cells and may be useful in predicting treatment response.•ATG4B knockdown reduces autophagy, impairs the survival of CML stem/progenitor cells, and sensitizes them to IM treatment.
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