Computational Analysis Pipeline Research Articles

EEG Spectral Changes Linked to Psychiatric Medications: Computational Pipeline for Data Mining and Analysis.

The presented computational pipeline is designed to analyze drug-induced changes in EEG data from the Temple University EEG Corpus. The data is cleaned from artifacts, pre-processed, the averaged absolute and relative frequency powers are calculated and compared to a control group. Thus, different research hypotheses can be tested with the intention to reuse accessible data collections.

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach.

Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3+ T regulatory cells and a CD163+ myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163+ myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R+CD1C+ myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems.

Abstract B020: Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients

Abstract Purpose of study: Non-small cell lung cancer (NSCLC) is the most common and fatal of cancers. In this work, we examine multiplexed images of NSCLC tumors to investigate both the spatial and spatiotemporal eco-evolutionary interactions between the tumor and its microenvironment, to better understand NSCLC tumor progression and therapy response. Methods: We developed a scalable, computational image analysis pipeline using cell-segmentation and quadrat-based approaches to analyze the spatial and temporal features of high-dimensional multiplexed NSCLC images. Multiplexed images enable the spatial readouts of numerous biomarkers per tissue sample and allow the interpretation of cellular states and the characterization of tumor-immune interactions across tissue ensembles. We also implement statistical approaches for ecological niche modelling combined with machine learning and deep learning models to predict disease progression and identify clinical imaging biomarkers. Data: Images were obtained from two 9-patient cohorts having advanced/metastatic NSCLC who were treated with the oral HDAC inhibitor vorinostat combined with the PD-1 inhibitor pembrolizumab. The first cohort had 4 progressors (PD) and 5 with stable disease (SD). The second cohort had 3 patients each in the PD, SD and partial response (PR) categories. Images were collected from all patients both pre- and on-treatment (during the third week). Results: Using our computational framework based on cell segments and quadrats, we confirm that different spatial neighborhoods exist that distinguish PD from SD, and that these spatial ecologies aid disease progression: PD patients have distinct ecologies with higher colocalization of PanCK+PD-1+FoxP3 indicating an immunosuppressive environment, whereas SD patients have a higher colocalization of PanCK+PD-L1 along with T cells, suggesting immunoactive tumor regions. These can be considered as potential biomarker candidates for predicting tumor progression. In an additional experiment where we include PR samples in our analyses, these distinct spatial neighborhoods are reinforced amongst PD, SD and PR patient groups corroborating the existence of spatiotemporal patterns. Further, we were able to predict treatment response with >91% accuracy given each patient’s spatial distribution of cell types from pre-treatment images. Using these predictions, we can generate risk maps at the patient level to identify areas of the tumor that are indicators of a higher probability of progression during treatment. Conclusions: We leveraged both single-cell and quadrat-resolution analysis of multiplexed imaging data and identified fundamentally distinct spatial ecologies between PD and SD patients. The ecology in PD patients appears to be primed for immune resistance even before treatment. This ecological diversity between SD and PD patients acts as a biomarker that enables accurate disease progression prediction. Our disease progression predictions can be used in conjunction with standard PD-L1 status to further strengthen personalized treatment strategies. Citation Format: Sandhya Prabhakaran, Chandler Gatenbee, Mark Robertson-Tessi, Amer A. Beg, Jhanelle Gray, Scott Antonia, Robert A. Gatenby, Alexander R.A. Anderson. Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B020.

Unveiling forensically relevant biogeographic, phenotype and Y-chromosome SNP variation in Pakistani ethnic groups using a customized hybridisation enrichment forensic intelligence panel.

Massively parallel sequencing following hybridisation enrichment provides new opportunities to obtain genetic data for various types of forensic testing and has proven successful on modern as well as degraded and ancient DNA. A customisable forensic intelligence panel that targeted 124 SNP markers (67 ancestry informative markers, 23 phenotype markers from the HIrisplex panel, and 35 Y-chromosome SNPs) was used to examine biogeographic ancestry, phenotype and sex and Y-lineage in samples from different ethnic populations of Pakistan including Pothwari, Gilgit, Baloach, Pathan, Kashmiri and Siraiki. Targeted sequencing and computational data analysis pipeline allowed filtering of variants across the targeted loci. Study samples showed an admixture between East Asian and European ancestry. Eye colour was predicted accurately based on the highest p-value giving overall prediction accuracy of 92.8%. Predictions were consistent with reported hair colour for all samples, using the combined highest p-value approach and step-wise model incorporating probability thresholds for light or dark shade. Y-SNPs were successfully recovered only from male samples which indicates the ability of this method to identify biological sex and allow inference of Y-haplogroup. Our results demonstrate practicality of using hybridisation enrichment and MPS to aid in human intelligence gathering and will open many insights into forensic research in South Asia.

Assessing risk of vector transmission of Chagas disease through blood source analysis using LC-MS/MS for hemoglobin sequence identification.

Chagas disease is mainly transmitted by triatomine insect vectors that feed on vertebrate blood. The disease has complex domiciliary infestation patterns and parasite transmission dynamics, influenced by biological, ecological, and socioeconomic factors. In this context, feeding patterns have been used to understand vector movement and transmission risk. Recently, a new technique using Liquid chromatography tandem mass spectrometry (LC-MS/MS) targeting hemoglobin peptides has showed excellent results for understanding triatomines’ feeding patterns. The aim of this study was to further develop the automated computational analysis pipeline for peptide sequence taxonomic identification, enhancing the ability to analyze large datasets data. We then used the enhanced pipeline to evaluate the feeding patterns of Triatoma dimidiata, along with domiciliary infestation risk variables, such as unkempt piles of firewood or construction material, cracks in bajareque and adobe walls and intradomiciliary animals. Our new python scripts were able to detect blood meal sources in 100% of the bugs analyzed and identified nine different species of blood meal sources. Human, chicken, and dog were the main blood sources found in 78.7%, 50.4% and 44.8% of the bugs, respectively. In addition, 14% of the bugs feeding on chicken and 15% of those feeding on dogs were captured in houses with no evidence of those animals being present. This suggests a high mobility among ecotopes and houses. Two of the three main blood sources, dog and chicken, were significantly (p < 0.05) affected by domiciliary infestation risk variables, including cracks in walls, construction material and birds sleeping in the intradomicile. This suggests that these variables are important for maintaining reproducing Triatoma dimidiata populations and that it is critical to mitigate these variables in all the houses of a village for effective control of these mobile vectors.

Systematic Assessment of the Accuracy of Subunit Counting in Biomolecular Complexes Using Automated Single-Molecule Brightness Analysis.

Analysis of single-molecule brightness allows subunit counting of high-order oligomeric biomolecular complexes. Although the theory behind the method has been extensively assessed, systematic analysis of the experimental conditions required to accurately quantify the stoichiometry of biological complexes remains challenging. In this work, we develop a high-throughput, automated computational pipeline for single-molecule brightness analysis that requires minimal human input. We use this strategy to systematically quantify the accuracy of counting under a wide range of experimental conditions in simulated ground-truth data and then validate its use on experimentally obtained data. Our approach defines a set of conditions under which subunit counting by brightness analysis is designed to work optimally and helps in establishing the experimental limits in quantifying the number of subunits in a complex of interest. Finally, we combine these features into a powerful, yet simple, software that can be easily used for the analysis of the stoichiometry of such complexes.

Spatial transcriptomics: new dimension of understanding biological complexity.

Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40–49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

Macrophage calcium reporter mice reveal immune cell communication in vitro and in vivo

SummaryCell communication underlies emergent functions in diverse cell types and tissues. Recent evidence suggests that macrophages are organized in communicating networks, but new tools are needed to quantitatively characterize the resulting cellular conversations. Here, we infer cell communication from spatiotemporal correlations of intracellular calcium dynamics that are non-destructively imaged across cell populations expressing genetically encoded calcium indicators. We describe a hematopoietic calcium reporter mouse (Csf1rCreGCaMP5fl) and a computational analysis pipeline for inferring communication between reporter cells based on “excess synchrony.” We observed signals suggestive of cell communication in macrophages treated with immune-stimulatory DNA in vitro and tumor-associated immune cells imaged in a dorsal window chamber model in vivo. Together, the methods described here expand the toolkit for discovery of cell communication events in macrophages and other immune cells.

Evaluating the Reliability of Human Brain White Matter Tractometry.

The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.

Rapid Serial Immunoprofiling of the Tumor Immune Microenvironment by Fine Needle Sampling.

There is increasing effort to discover and integrate predictive and/or prognostic biomarkers into treatment algorithms. While tissue-based methods can reveal tumor-immune cell compositions at a single time point, we propose that single-cell sampling via fine needle aspiration (FNA) can facilitate serial assessment of the tumor immune microenvironment (TME) with a favorable risk-benefit profile. Primary antibodies directed against 20 murine and 25 human markers of interest were chemically modified via a custom linker-bio-orthogonal quencher (FAST) probe. A FAST-FNA cyclic imaging and analysis pipeline were developed to derive quantitative response scores. Single cells were harvested via FNA and characterized phenotypically and functionally both in preclinical and human samples using the newly developed FAST-FNA assay. FAST-FNA samples analyzed manually versus the newly developed deep learning-assisted pipeline gave highly concordant results. Subsequently, an agreement analysis showed that FAST and flow cytometry of surgically resected tumors were positively correlated with an R2 = 0.97 in preclinical samples and an R2 = 0.86 in human samples with the detection of the relevant tumor and immune biomarkers of interest. Finally, the feasibility of applying this minimally invasive approach to analyze the TME during immunotherapy was assessed in patients with cancer revealing local antitumor immune programs. The FAST-FNA is an innovative technology that combines bio-orthogonal chemistry coupled with a computational analysis pipeline for the comprehensive profiling of single cells obtained through FNA. This is the first demonstration that the complex and rapidly evolving TME during treatment can be accurately and serially measured by simple FNA.

An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

AimAllograft rejection is a serious concern in heart transplant medicine. Though endomyocardial biopsy with histological grading is the diagnostic standard for rejection, poor inter-pathologist agreement creates significant clinical uncertainty. The aim of this investigation is to demonstrate that cellular rejection grades generated via computational histological analysis are on-par with those provided by expert pathologistsMethods and resultsThe study cohort consisted of 2472 endomyocardial biopsy slides originating from three major US transplant centres. The ‘Computer-Assisted Cardiac Histologic Evaluation (CACHE)-Grader’ pipeline was trained using an interpretable, biologically inspired, ‘hand-crafted’ feature extraction approach. From a menu of 154 quantitative histological features relating the density and orientation of lymphocytes, myocytes, and stroma, a model was developed to reproduce the 4-grade clinical standard for cellular rejection diagnosis. CACHE-grader interpretations were compared with independent pathologists and the ‘grade of record’, testing for non-inferiority (δ = 6%). Study pathologists achieved a 60.7% agreement [95% confidence interval (CI): 55.2–66.0%] with the grade of record, and pair-wise agreement among all human graders was 61.5% (95% CI: 57.0–65.8%). The CACHE-Grader met the threshold for non-inferiority, achieving a 65.9% agreement (95% CI: 63.4–68.3%) with the grade of record and a 62.6% agreement (95% CI: 60.3–64.8%) with all human graders. The CACHE-Grader demonstrated nearly identical performance in internal and external validation sets (66.1% vs. 65.8%), resilience to inter-centre variations in tissue processing/digitization, and superior sensitivity for high-grade rejection (74.4% vs. 39.5%, P < 0.001).ConclusionThese results show that the CACHE-grader pipeline, derived using intuitive morphological features, can provide expert-quality rejection grading, performing within the range of inter-grader variability seen among human pathologists.

New Insights Into Mitochondrial DNA Reconstruction and Variant Detection in Ancient Samples.

Ancient DNA (aDNA) studies are frequently focused on the analysis of the mitochondrial DNA (mtDNA), which is much more abundant than the nuclear genome, hence can be better retrieved from ancient remains. However, postmortem DNA damage and contamination make the data analysis difficult because of DNA fragmentation and nucleotide alterations. In this regard, the assessment of the heteroplasmic fraction in ancient mtDNA has always been considered an unachievable goal due to the complexity in distinguishing true endogenous variants from artifacts. We implemented and applied a computational pipeline for mtDNA analysis to a dataset of 30 ancient human samples from an Iron Age necropolis in Polizzello (Sicily, Italy). The pipeline includes several modules from well-established tools for aDNA analysis and a recently released variant caller, which was specifically conceived for mtDNA, applied for the first time to aDNA data. Through a fine-tuned filtering on variant allele sequencing features, we were able to accurately reconstruct nearly complete (>88%) mtDNA genome for almost all the analyzed samples (27 out of 30), depending on the degree of preservation and the sequencing throughput, and to get a reliable set of variants allowing haplogroup prediction. Additionally, we provide guidelines to deal with possible artifact sources, including nuclear mitochondrial sequence (NumtS) contamination, an often-neglected issue in ancient mtDNA surveys. Potential heteroplasmy levels were also estimated, although most variants were likely homoplasmic, and validated by data simulations, proving that new sequencing technologies and software are sensitive enough to detect partially mutated sites in ancient genomes and discriminate true variants from artifacts. A thorough functional annotation of detected and filtered mtDNA variants was also performed for a comprehensive evaluation of these ancient samples.

Computational flow cytometric analysis to detect epidermal subpopulations in human skin

BackgroundThe detection and dissection of epidermal subgroups could lead to an improved understanding of skin homeostasis and wound healing. Flow cytometric analysis provides an effective method to detect the surface markers of epidermal cells while producing high-dimensional data files.MethodsA 9-color flow cytometric panel was optimized to reveal the heterogeneous subgroups in the epidermis of human skin. The subsets of epidermal cells were characterized using automated methods based on dimensional reduction approaches (viSNE) and clustering with Spanning-tree Progression Analysis of Density-normalized Events (SPADE).ResultsThe manual analysis revealed differences in epidermal distribution between body sites based on a series biaxial gating starting with the expression of CD49f and CD29. The computational analysis divided the whole epidermal cell population into 25 clusters according to the surface marker phenotype with SPADE. This automatic analysis delineated the differences between body sites. The consistency of the results was confirmed with PhenoGraph.ConclusionA multicolor flow cytometry panel with a streamlined computational analysis pipeline is a feasible approach to delineate the heterogeneity of the epidermis in human skin.

Identifying novel prognostic markers and genotype-phenotype associations in endometrioid endometrial carcinoma by computational analysis of histopathological images

Hematoxylin and eosin stained slides are routinely used for the diagnosis and grading of endometrioid endometrial carcinoma (EEC). These images present a high degree of cellular heterogeneity, which may contain clinically relevant information such as prognosis and is difficult to be quantified objectively by eyes. Besides traditional microscopic image assessment, a lot of effort has been put in molecular characterization of tumors. How molecular events manifest at tumor tissue level is not well understood. In this paper, we investigated whether quantitative morphological features extracted from histopathological images are associated with patient survival and somatic mutation of genes in EEC using the multi-modality data from The Cancer Genome Atlas. A computational image analysis pipeline was developed to extract image features that characterize the size, shape, staining, and density of cell nuclei. For prognosis prediction, we built a prognostic model based on the image features. In a validation set, the risk score predicted by our model was an independent prognostic factor for overall survival in a multivariate Cox proportional hazards model (hazard ratio with 95% confidence interval: 3.38 [1.55–7.37], p = 2.15e−3). To link tumor tissue morphology with somatic mutation, a two-sided Mann-Whitney U test was used to compare the distribution of each feature between mutated and nonmutated cases for frequently mutated genes. We found that TP53 and TTN were significantly associated with tissue morphological changes. These findings show the promising potential of computational histopathology image analysis in predicting patient survival and exploring genotype-phenotype associations.

Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation.

We report on the hydrothermal polymerization (HTP) of polyimide (PI) networks using the medium H2O and the comonomers 1,3,5-tris(4-aminophenyl)benzene (TAPB) and pyromellitic acid (PMA). Full condensation is obtained at minimal reaction times of only 2 h at 200 °C. The PI networks are obtained as monoliths and feature thermal stabilities of >500 °C, and in several cases even up to 595 °C. The monoliths are built up by networks of densely packed, near-monodisperse spherical particles and annealed microfibers, and show three types of porosity: (i) intrinsic inter-segment ultramicroporosity (<0.8 nm) of the PI networks composing the particles (∼3–5 μm), (ii) interstitial voids between the particles (0.1–2 μm), and (iii) monolith cell porosity (∽10–100 μm), as studied via low pressure gas physisorption and Hg intrusion porosimetry analyses. This unique hierarchical porosity generates an outstandingly high specific pore volume of 7250 mm3 g−1. A large-scale micromorphological study screening the reaction parameters time, temperature, and the absence/presence of the additive acetic acid was performed. Through expert interpretation of hundreds of scanning electron microscopy (SEM) images of the products of these experiments, we devise a hypothesis for morphology formation and evolution: a monomer salt is initially formed and subsequently transformed to overall eight different fiber, pearl chain, and spherical morphologies, composed of PI and, at long reaction times (>48 h), also PI/SiO2 hybrids that form through reaction with the reaction vessel. Moreover, we have developed a computational image analysis pipeline that deciphers the complex morphologies of these SEM images automatically and also allows for formulating a hypothesis of morphology development in HTP that is in good agreement with the manual morphology analysis. Finally, we upscaled the HTP of PI(TAPB–PMA) and processed the resulting powder into dense cylindrical specimen by green solvent-free warm-pressing, showing that one can follow the full route from the synthesis of these PI networks to a final material without employing harmful solvents.

Environmental Atlas of Prokaryotes Enables Powerful and Intuitive Habitat-Based Analysis of Community Structures.

SummaryThe recent prevalence of high-throughput sequencing has been producing numerous prokaryotic community structure datasets. Although the trait-based approach is useful to interpret those datasets from ecological perspectives, available trait information is biased toward culturable prokaryotes, especially those of clinical and public health relevance, and thus may not represent the breadth of microbiota found across many of Earth's environments. To facilitate habitat-based analysis free of such bias, here we report a ready-to-use prokaryotic habitat database, ProkAtlas. ProkAtlas comprehensively links 16S rRNA gene sequences to prokaryotic habitats, using public shotgun metagenome datasets. We also developed a computational pipeline for habitat-based analysis of given prokaryotic community structures. After confirmation of the method effectiveness using 16S rRNA gene sequence datasets from individual genomes and the Earth Microbiome Project, we showed its validness and effectiveness in drawing ecological insights by applying it to six empirical prokaryotic community datasets from soil, aquatic, and human gut samples.

GeneTEFlow: A Nextflow-based pipeline for analysing gene and transposable elements expression from RNA-Seq data.

Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent research highlights the important role of TEs in the embryogenesis, neurodevelopment, and immune functions. However, there is a lack of a one-stop and easy to use computational pipeline for expression analysis of both genes and locus-specific TEs from RNA-Seq data. Here, we present GeneTEFlow, a fully automated, reproducible and platform-independent workflow, for the comprehensive analysis of gene and locus-specific TEs expression from RNA-Seq data employing Nextflow and Docker technologies. This application will help researchers more easily perform integrated analysis of both gene and TEs expression, leading to a better understanding of roles of gene and TEs regulation in human diseases. GeneTEFlow is freely available at https://github.com/zhongw2/GeneTEFlow.

Automatic Incident Triage in Radiation Oncology Incident Learning System.

The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports.

Conventional and Computational Flow Cytometry Analyses Reveal Sustained Human Intrathymic T Cell Development From Birth Until Puberty.

The thymus is the organ where subsets of mature T cells are generated which subsequently egress to function as central mediators in the immune system. While continuously generating T cells even into adulthood, the thymus does undergo involution during life. This is characterized by an initial rapid decrease in thymic cellularity during early life and by a second age-dependent decline in adulthood. The thymic cellularity of neonates remains low during the first month after birth and the tissue reaches a maximum in cellularity at 6 months of age. In order to study the effect that this first phase of thymic involution has on thymic immune subset frequencies, we performed multi-color flow cytometry on thymic samples collected from birth to 14 years of age. In consideration of the inherent limitations posed by conventional flow cytometry analysis, we established a novel computational analysis pipeline that is adapted from single-cell transcriptome sequencing data analysis. This allowed us to overcome technical effects by batch correction, analyze multiple samples simultaneously, limit computational cost by subsampling, and to rely on KNN-graphs for graph-based clustering. As a result, we successfully identified rare, distinct and gradually developing immune subsets within the human thymus tissues. Although the thymus undergoes early involution from infanthood onwards, our data suggests that this does not affect human T-cell development as we did not observe significant alterations in the proportions of T-lineage developmental intermediates from birth to puberty. Thus, in addition to providing an interesting novel strategy to analyze conventional flow cytometry data for the thymus, our work shows that the early phase of human thymic involution mainly limits the overall T cell output since no obvious changes in thymocyte subsets could be observed.