Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational-based interactive analyses.

The wide possibilities opened by the developments of multi-parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi-dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi-stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi-Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi-stained samples and allows improving multi-parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry.

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.

Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.

Computational prediction of manually gated rare cells in flow cytometry data.

Rare cell identification is an interesting and challenging question in flow cytometry data analysis. In the literature, manual gating is a popular approach to distill flow cytometry data and drill down to the rare cells of interest, based on prior knowledge of measured protein markers and visual inspection of the data. Several computational algorithms have been proposed for rare cell identification. To compare existing algorithms and promote new developments, FlowCAP-III put forward one computational challenge that focused on this question. The challenge provided flow cytometry data for 202 training samples and two manually gated rare cell types for each training sample, roughly 0.02 and 0.04% of the cells, respectively. In addition, flow cytometry data for 203 testing samples were provided, and participants were invited to computationally identify the rare cells in the testing samples. Accuracy of the identification results was evaluated by comparing to manual gating of the testing samples. We participated in the challenge, and developed a method that combined the Hellinger divergence, a downsampling trick and the ensemble SVM. Our method achieved the highest accuracy in the challenge.

Deep inspiratory breath-hold radiotherapy on a Helical Tomotherapy unit: Workflow and early outcomes in patients with left-sided breast cancer.

Introduction: The clinical implementation of deep inspiratory breath-hold (DIBH) radiotherapy to reduce cardiac exposure in patients with left-sided breast cancer is challenging with helical tomotherapy(HT) and has received little attention. We describe our novel approach to DIBH irradiation in HT using a specially designed frame and manual gating, and compare cardiac substructure doses with the free-breathing (FB) technique. Material and methods: The workflow incorporates staggered junctions and a frame that provides tactile feedback to the patient and monitoring for manual cut-off. The treatment parameters and clinical outcome of 20 patients with left-sided breast cancer who have undergone DIBH radiotherapy as a part of an ongoing prospective registry are reported. All patients underwent CT scans in Free Breathing (FB) and DIBH using the in-house Respiframe, which incorporates a tactile feedback-based system with an indicator pencil. Plans compared target coverage, cardiac doses, synchronizing treatment with breath-hold and avoiding junction repetition. MVCT scans are used for patient alignment. Results: The mean dose (Dmean) to the heart was reduced by an average of 34 % in DIBH-HT compared to FB-HT plans (3.8 Gy vs 5.7 Gy). Similarly, 32 % and 67.8 % dose reduction were noted in the maximum dose (D0.02 cc) of the left anterior descending artery, mean 12.3 Gy vs 18.1 Gy, and mean left ventricle V5Gy 13.2 % vs 41.1 %, respectively. The mean treatment duration was 451.5 sec with a median 8 breath-holds; 3 % junction locations between successive breath-holds were replicated. No locoregional or distant recurrences were observed in the 9-month median follow-up. Conclusion: Our workflow for DIBH with Helical-Tomotherapy addresses patient safety, treatment precision and challenges specific to this treatment unit. The workflow prevents junction issues by varying daily breath-hold durations and avoiding junction locations, providing a practical solution for left-sided breast cancer treatment with HT.

Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.

The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.