In biological dosimetry a radiation dose is estimated using the average number of chromosomal aberrations per peripheral blood lymphocytes. This analysis is still manually performed on 2D metaphase images depicting the 23 pairs of chromosomes because the false discovery rate of current automated detection systems is too high and variable because of sensitivity to small variations in image quality (chromosome spread, illumination variations …). Therefore, the current systems are only used to assist human experts. Designing more performant automatic and reliable chromosomal aberration detection systems has become of paramount importance to improve diagnosis speed and reduce human expertise time. Here, we propose a novel deep-learning method for automatic rare chromosomal aberration detection and uncertainty quantification. We formulate the problem as a unique regression problem requiring the minimization of a sparsity-promoting loss to reduce the false alarm rate. Furthermore, we select checkpoints at the end of each epoch during training to form a model ensemble. The resulting artificial experts are further analyzed to derive a consensus voting, similar to an agreement of human annotator rating, to provide trustworthy aberration detections and confidence intervals. A radiation dose curve is finally derived from deep learning-assisted counting of dicentrics and fragments in metaphase images, in high agreement with the reference hand-crafted curve in biological dosimetry with a promising dose estimation validation.
Dogs are a critical non-rodent species used in preclinical safety studies, particularly, in the pharmaceutical field, due to their physiological, metabolic, and immunological similarities to humans. As such, immunophenotyping of canine peripheral blood mononuclear cells (PBMCs) plays a crucial role in translational research, immune monitoring, and safety evaluations in drug development. However, the limited availability of canine-specific antibodies restricts detailed and accurate immune profiling, which is essential for advancing safety evaluations in drug development. To address this challenge, we developed a 15-marker panel for comprehensive mass cytometry-based immunophenotyping of cryopreserved canine PBMCs. This panel encompasses major leukocyte subsets, including B cells, CD4+ T helper cells, regulatory T cells, CD8+ cytotoxic T cells, memory T cell subsets, natural killer T cells, natural killer cells, dendritic cells, CD4+ monocytes, classical monocytes, and neutrophils. We utilized both extracellular and intracellular markers to facilitate in-depth immune profiling, despite the limited availability of canine-specific antibodies. The panel was thoroughly optimized in terms of marker selection, antibody clone validation, and metal isotope pairing. Additionally, by the use of mass cytometry, several channels remain unoccupied, providing flexibility for future panel expansion.
Flow cytometry is an essential component of routine hematological lab testing. Many computational methods have been proposed for the analysis of flow cytometry data, but most have focused on supervised learning for just one or a few specific disorders. To maximize clinical utility, we develop a method that enables identification of multiple common disorders and quality indicators. Our method includes a self-supervised pretraining component as well as a new, transformer-based model architecture. The self-supervised training algorithm is based on the DINO method while the model architecture is a relatively simple transformer encoder stack that includes a class (CLS) token, similar to BERT or vision-transformer models. Using a dataset of 52,625 samples obtained during routine clinical testing at our laboratory, we show that our pretraining method develops informative tube-level representations that clearly separate important diagnostic classes. We then evaluate performance on multiple downstream tasks, including sample viability estimation and five common hematological disorders. We compare our method to self-organizing maps, convolutional neural networks, attention-based multiple-instance learning models, and two varieties of set-transformer-based models, and demonstrate that our method delivers higher classification performance than other approaches.
Advances in spectral cytometry instrumentation and fluorescent reagents have led to the possibility of ultra-high-parameter panels exceeding 50 colors. However, panel size is limited in practice by unmixing-dependent spreading (UDS), a phenomenon which leads to a progressive deterioration of unmixed signal-to-noise ratios in panels that contain fluorochrome combinations with significant spectral overlap. So far, choosing spectrally compatible sets of fluorochromes that avoid UDS has been a complex and labor-intensive task involving substantial trial-and-error experimentation. Here, we provide a detailed explanation of UDS and practical strategies for handling UDS in large spectral panels. We describe the empirical hallmarks of UDS, demonstrate how to quantify its impact, and dissect its underlying mathematical cause in terms of spectral collinearity. We present practical tools derived from the regression literature that can be used to select optimal combinations of fluorochromes in a platform-agnostic fashion based on publicly available reference data, providing a general tool for spectral panel design.
Tumors that develop in the bone exhibit poor responses to most treatment options, including advanced immunotherapies. Despite its immune-rich composition, the bone marrow microenvironment often fails to suppress tumor progression and instead promotes tumor growth and immune evasion, although the underlying mechanisms remain poorly understood. To investigate tumor-induced remodeling of the bone marrow immune environment and characterize tumor-infiltrating immune populations, we developed a high-dimensional 22-marker spectral flow cytometry panel for use in preclinical models of bone and bone marrow cancers. The panel enables profiling of major immune populations including CD4+ T and CD8+ T cells, B cells, Natural Killer cells, dendritic cells, monocytes, macrophages and neutrophils, and incorporates activation, exhaustion and immune checkpoint markers. Panel optimization was performed using spleen, freshly isolated bone marrow and tibial osteosarcoma tissue from adult BALB/c mice. While optimized for murine osteosarcoma and bone marrow tissue, this panel provides a robust and adaptable platform for high-dimensional immune profiling across solid tumors and hematological malignancies.
Increasing evidence suggests connection between mitochondrial dysfunction and platelet-related disorders, necessitating developing of a high throughput method for assessing the functioning of mitochondria in platelets. Here we develop an approach to monitor the membrane potential of mitochondria simultaneously with intracellular calcium signaling in platelets using continuous flow cytometry. Platelets were loaded with Fura Red and DiOC6(3), which did not affect intracellular calcium signaling and the realization of platelet functional responses. The values were normalized using the negative controls with CCCP in order to make the signal independent of the platelet size and the number of mitochondria. Stimulation with ADP and TRAP-6 produced transient mitochondrial hyperpolarization, whose amplitude was directly proportional to the calcium mobilization. Platelets from patients with Wiskott-Aldrich syndrome (WAS) demonstrated reduced membrane potential of mitochondria in resting platelets in comparison to healthy donors, but their ability to increase the activity of mitochondria upon stimulation was not impaired.
Bispecific T cell engagers bring together T cells with malignant B cell targets to enable synapse formation and disease eradication of hematological malignancies. The proportion of T cells and their ability to bind to their targets varies between patients and may be a biomarker of response. In this study, we developed a method to detect T and B cell conjugates, as measured by conventional flow cytometry, and confirmed using imaging flow cytometry. After bispecific antibody engagement, multiple T cells were bound to each target cell in complexes comprising CD4+, CD8+, and CD4+ and CD8+ T cells. Imaging flow cytometry confirmed that the number and size of multimers increased significantly in the presence of bispecific antibodies. Conventional cytometry misidentified the memory phenotype of T cells bound to target cells due to augmented co-expression of CD45RA and CCR7 cell surface memory markers. Imaging flow cytometry verified conventional flow cytometry data showing increased T:B synapse and multimer formation after incubation with bispecific T cell engagers. Therefore, both flow cytometry platforms are suitable for identification of T cell: target cell conjugates.
High-dimensional cytometry, such as mass cytometry (CyTOF), measures protein expression in single cells. When paired with AI-enhanced data analytics, it facilitates the discovery of immune biomarkers that can assist in diagnosing and treating immune-related diseases. However, fluctuating instrument readouts, known as batch effects, can obscure biological patterns. To enhance our previously reported EPIC immune atlas platform, we developed the ImmuneMapBuilder app to integrate, annotate, and cluster CyTOF data. To evaluate biological stratification and batch effects in clustering results, we created a two-step visualization method, called Group Similarity Analysis (GSA). First, multidimensional immune profiles are projected into two-dimensional embeddings to reveal similarities between samples. Second, silhouette analysis of embedding coordinates quantifies cohesion within technical and biological groups. We illustrate the scope and effectiveness of GSA using six batch normalization methods across three datasets, demonstrating that batch-effect correction enhances the separation of age groups and tissue types. The goals of batch normalization are increased biological and decreased technical GSA scores, both of which correlate with decreased Earth Mover's Distance (EMD), an alignment indicator of signal distributions. To dissect the contributions of individual marker misalignments to batch effects, we introduce a mix-and-match strategy that combines normalized and raw channels. GSA also helps to compare meta-clustering outputs. In a case study, we confirmed the significance of CD62L expression in T cells for age-based stratification. Overall, GSA is a versatile method to evaluate clustering results from cytometry data.
Hereditary spherocytosis (HS) is the most common congenital red blood cell membrane disorder, characterized by structural protein defects that lead to hemolytic anemia. Although several diagnostic tests exist, including osmotic fragility tests (OFTs), acidified glycerol lysis test (AGLT), and the EMA-binding test (EMA), each presents specific limitations regarding sensitivity, specificity, or technical requirements. Flow cytometric osmotic fragility testing (OFT-FCM) emerges as a promising complementary assay, offering a standardized workflow and rapid turnaround time. We conducted a retrospective study including 106 subjects (20 HS patients and 86 healthy controls) recruited at Hospital Clínic de Barcelona between September 2024 and September 2025. Clinical and laboratory data were collected, and all participants underwent OFT, AGLT, EMA, and OFT-FCM using two acquisition protocols (300 and 214 s). Logistic regression and receiver operating characteristic curve analysis were performed to evaluate diagnostic performance and determine optimal cut-off values. HS patients exhibited significantly altered hematologic parameters compared with controls, including higher reticulocyte counts, red cell distribution width, and mean corpuscular hemoglobin. The EMA-binding test demonstrated high specificity (100%) but lower sensitivity (57.9%). OFT achieved high sensitivity (>97%) but low specificity (<47%). AGLT showed balanced accuracy (sensitivity 68.4%, specificity 96.1%). OFT-FCM yielded areas under the curve of 0.85 for both protocols, with optimal thresholds providing specificities of 95-100% and sensitivities of 57-59%. No significant differences were observed between OFT-FCM and EMA performance. OFT-FCM effectively discriminates HS patients from healthy controls and showed diagnostic performance comparable to EMA and favorable relative to classical OFT and AGLT in this cohort, while offering practical advantages in terms of workflow simplicity and turnaround time, and supporting its use as a complementary flow-cytometric assay within the diagnostic work-up of HS.
Our knowledge of the immune system continues to expand at a rapid pace, and this coupled with technological advances now enables us to interrogate both the breadth and the depth of the immune response at levels without precedent. This has also facilitated rapidly integrating some of this carefully vetted knowledge into clinical practice. Notable examples of these advances include successfully harnessing the therapeutic potential of the immune system (immunotherapy), as well as an expanding menu of clinical flow-cytometry laboratory tests to assess the phenotype and function of the cellular immune response. This has also given rise to an emerging sub-discipline called "Immune-Health", with its premise undergirded by the notion that the surveillance capacity and sentinel nature of the immune response might enable the immune system to serve as a reliable barometer of overall health of the individual. At its core, immune-health entails defining baseline immune characteristics for each individual so that perturbations in this baseline signature can serve as clinically actionable biomarkers that might predict the onset, help monitor the progression and potentially mitigate the effects of the underlying disease process. Defining appropriate reference-ranges (RR) for key cellular immune parameters constitutes one of the essential building-blocks of the concept of immune-health. Establishing pediatric RR for cellular correlates of immune-health and disease is a time-consuming and labor-intensive process, and consequently only a select few specialty laboratories at some children's hospitals (with a well-established immunodeficiency/immunedysregulation clinical service) and a couple of large national reference laboratories in the United States (US) have invested their time and effort into this endeavor. Furthermore, the lack of standardization in the definition of immune subsets has also complicated this effort. In 2012, the Human Immunophenotyping Consortium (HIPC) established by the National Institute of Allergy and Infectious Diseases (NIAID)-Division of Allergy, Immunology and Transplantation, published a benchmark study that attempted to standardize the definitions for several cellular immune parameters. In general, Europe has led the charge in advancing these standardization efforts, while similar efforts in the US have been rather sparse, and currently there's a marked paucity of US based studies describing the establishment of pediatric reference ranges (RR) for T and B cell subsets based on the HIPC standardization initiative. In this report, we describe the results of the endeavor, at a large, free-standing children's hospital in the US with a busy clinical immunology service, to establish pediatric reference ranges for naïve and memory T and B cell subsets largely adapted from the subset definitions outlined by the HIPC.
X-linked agammaglobulinemia (XLA) is most often caused by impaired Bruton's tyrosine kinase (BTK) function. Rapid laboratory support is needed when genetic findings are inconclusive or pending. We developed a flow-cytometric assay to quantify intracellular BTK protein in monocytes, aiding the diagnosis of XLA. A stain index (SI) reference range was established using healthy donors (n = 25; SI median 1.14, range 1.01-1.66). ROC analysis defined a diagnostic cutoff of SI = 1.79, providing 95% sensitivity and 100% specificity compared to patients with pathogenic BTK variants (n = 40). Using this cutoff, abnormal BTK expression was detected in 38/40 genetically confirmed XLA patients. All patients with variants of uncertain/likely pathogenic significance (n = 17) had SI values above the cutoff. Bone marrow analysis in a patient subset showed a developmental arrest at the Pre-BI stage. The assay offers a rapid, clinically applicable readout to support XLA diagnosis and carrier evaluation in females, effectively complementing genetic testing.
Central nervous system involvement in multiple myeloma (CNS-MM) is a rare complication usually observed in advanced relapse or refractory settings. The detection of malignant plasma cells in the cerebrospinal fluid (CSF) on the basis of morphology and flow cytometry (FCM) confirms this extra-medullary lesion. The records of 459 MM patients treated between 2016 and 2025 were reviewed, identifying four cases with CSF involvement. CSF and bone marrow samples were processed and analyzed on the basis of morphology and FCM using a dedicated 9-color panel to detect malignant plasma cells. Interleukin-6 (IL-6) and interleukin-10 (IL-10) concentrations were also measured in the CSF using Cytometric Bead Assay. Three patients were in complete remission when CSF analysis revealed CNS-MM. Two patients presented no cerebral lesions, although spinal cord compression was observed for one of them. Two patients presented interference in FCM analysis, supporting the penetration of daratumumab into the CSF. Interleukin measures in the CSF showed low or undetectable IL-10 levels but elevated IL-6 concentrations in some cases. Overall prognosis was poor, with the death of three patients and only one remaining alive at 2 years. CNS-MM could be the first sign of relapse among patients previously in complete remission. It should be envisaged in the case of any myeloma patient with unexplained neurological symptoms, even when imaging is negative. Flow-cytometry panels need to be optimized for plasma-cell detection, especially among patients treated with anti-CD38 antibodies. Identifying patients at risk remains crucial, but prognostic biomarkers such as IL-6 still require validation.
Flow cytometry-based TBNK immunophenotyping is widely used to assess immune status in research, clinical diagnostics, and cell therapy development. Biological control materials such as PBMCs often serve as physiologically relevant controls, but suffer from considerable variability due to donor differences, limited stability, and fluctuations in antigen expression levels between lots. These factors make it challenging to achieve consistent assay performance, especially in longitudinal or multi-site environments. TBNK Cell Mimic (synthetic controls in PhenoCyte product line, developed by Slingshot Biosciences; henceforth referred to as TBNK Cell Mimic in this manuscript) are polymer-based cell mimics engineered to provide defined scatter properties, controlled antigen density, and stable subset ratios. Their scatter profiles are designed to be biologically relevant and comparable to those of native leukocyte populations. In this study, we performed a comprehensive analytical validation of a TBNK immunophenotyping assay using these TBNK Cell Mimics as standardized reference controls. Validation parameters included repeatability, intermediate precision, accuracy, linearity, specificity, robustness, stability, and carryover. The TBNK Cell Mimic met all predefined acceptance criteria, demonstrating ≤ 5% CV for intra- and inter-assay precision and R2 values > 0.998 across the linearity range. Accelerated stability studies performed at 25°C and 37°C showed < 5% variation in population frequencies, supporting the material's suitability for extended quality monitoring. These results indicate that TBNK Cell Mimic provides a consistent and reproducible reference material that can support assay validation and routine performance assessment. While not a replacement for biological samples when evaluating donor-specific or viability-dependent biology, their stability and lot-to-lot consistency offer a practical tool for reducing technical variability and improving harmonization across instruments, operators, and testing sites.
Image-activated cell sorting (IACS) enables high-throughput cell classification by linking cellular morphology to physiology. While integrating advanced artificial intelligence (AI) can enhance the capture of subtle morphological heterogeneity, AI models inevitably introduce greater computational complexity when addressing complex problems, leading to increased analysis latency and latency instability. High latency implies longer chip lengths, while latency instability leads to incorrect sorting timing. To address this challenge, IACS can utilize field-programmable gate array (FPGA) as a stable, low-latency image analysis tool. Here, we developed a two-stage FPGA processing system for cellular data acquisition and real-time AI inference, utilizing the high-level synthesis (HLS) framework. By deploying a customized U-Net model on the AMD-Xilinx accelerator card and integrating hardware acceleration modules including activation function approximation and pixel-level convolution acceleration, we achieved a stable segmentation latency of 3.06 milliseconds (ms) at a 272 MHz clock frequency and delivered a processing throughput of up to 16,601 frames per second (fps). Using the morphological parameters obtained after segmentation, we successfully separated deformed HeLa cells from normal cells and distinguished colorectal cells, red blood cells, HeLa cells, and microspheres. This work provides IACS with a stable and low-latency image processing solution.
We completed an international, multicenter, randomized, open-label Phase I/IIb trial assessing the safety and preliminary efficacy of transendocardial injection of autologous expanded CD34+ cells (ProtheraCytes) in patients after acute myocardial infarction (AMI; NCT02669810). A multicenter, randomized, controlled Phase III study is now being initiated. To support release of ProtheraCytes clinical batches, we validated two flow cytometry methods for accurate quantification of CD34/CD45+ cells (stem cell enumeration-SCE method) and characterization of accessory leukocyte subsets (monocytes, granulocytes, and B, T, NK lymphocytes-accessory populations immunophenotyping method). All the recovery rates for both methods, with calculations derived from QC materials specifications, met the acceptance criteria, based on precision assessment according to ICH Q2(R2), European Pharmacopeia (Ph. Eur. 2.7.23 and Ph. Eur. 2.7.24), and ISHAGE guidelines. In addition, the precision results (repeatability and intermediate precision) were lower than 28.3% (≤ 30% for accessory populations immunophenotyping method) and lower than 13.5% (≤ 25% for SCE method). Finally, a perfect linearity was demonstrated for SCE method across 1.7-2622.5 cells/μL with coefficient of determination (R2) of linear regression above 0.99 and matrix effects nearly negligible for both methods. The specificity, precision and accuracy of these methods were proven in the analysis of six determinations per operator in three different series. Altogether, these results indicate a good accuracy and precision of the proposed methods determining absolute counts, viability, and proportions of live CD34/CD45+ cells and accessory populations. This validated flow cytometry assay will be implemented for release testing in the forthcoming Phase III clinical trial of ProtheraCytes in post-AMI patients.
The introduction of full spectral technology in flow cytometry has facilitated access to an increasing number of markers to define cell subsets with higher precision. Cell sorting has a unique advantage to combine high throughput single cell analysis and recovery of rare live single cells for further downstream multi-omics analysis. Many studies have focused on advancing high dimensional single cell analysis; however, strategies to maximize cell sorting recovery in the context of deep immunophenotyping remain poorly defined. In this study, we evaluated sort performance in a six-way simultaneous cell sort setup. We modified a protocol using counting beads to assess absolute count in different sort decision criteria or modes. We demonstrate that the number of events collected can vary as much as 20% from the values indicated by the sort counter and is dependent on sort mode. Using the absolute count assay, we confirmed optimal conditions for six-way sorting of diverse human peripheral blood cell subsets defined by a 35-color panel and delineated pitfalls that can ultimately lead to suboptimal yield. Together, these findings provide novel insights into optimization of sort performance for advanced sorting and introduce a new approach for refining strategies for the simultaneous isolation of complex or rare cell subsets.
Pediatric immune thrombocytopenia (ITP) is a heterogeneous autoimmune disorder with variable clinical outcomes. While most children experience spontaneous remission, 10 to 20% progress to chronic ITP. Early prediction to chronicity remains a clinical challenge. This study aimed to develop and validate a logistic regression-based model integrating immune features to predict chronicity in ITP among children. In this prospective cohort study, we stratified 108 pediatric ITP patients into newly diagnosed with remission (n = 48), progressors (n = 13), and chronic ITP (n = 47). We analyzed peripheral blood using multicolor flow cytometry for 79 immune subsets. We recorded clinical variables including bleeding symptoms, platelet count, and recent infections/vaccinations. We performed multivariable logistic regression using immune parameters alone and in combination with clinical features. Model performance was assessed via 5-fold cross-validation and validated on the progressor cohort. Patients with Chronic ITP Exhibited a Distinct Immunological Feature Characterized by Reduced CD4/CD8 Ratio, na & iuml;ve Th and na & iuml;ve Tc Cell and Increased Effector Memory, Tc Cell Percent, Senescent T Cell (CD57+, PD1+), B Memory and Class Switched Memory B Cells. The Immune-Only Model Identified Reduced na & iuml;ve Cytotoxic T Cells [CD8(+)CD27(+)CD45RA(+)] and Increased Double-Negative B Cells [CD19(+)CD27(-)IgD(-)] as Key Predictors (Accuracy 71.6%, Area under the Receiver Operating Characteristic Curve (AUC) 0.761, p < 0.001), Correctly Classifying 8/13 (61.5%) Progressors. In the Combined Model, Increased Frequencies of Class-Switched Memory (CSW) B Cells [CD19 + CD27 + IgM-IgD-], Terminally Differentiated Effector Cytotoxic [CD8(+)CD28(-)] T Cells, more Specific Double Negative (DN) B Cells Population of CD21-/lowCD38-Cells[CD19 + CD21-/Low CD38-CD27-IgD-] and Clinically, Epistaxis and Absence of Abrupt Onset of Bleeding Were Associated with Chronicity, Improving Overall Accuracy 82.8%, (AUC 0.905, p < 0.001) and Correctly Classifying 10/13 (76.92%) Progressors. Integration of clinical features with detailed immune profiling enables early and accurate prediction of chronic ITP in children. These findings support its potential in selecting appropriate management strategies warranting validation in larger multicentric cohorts.
Two flow cytometry methods are used for stem cell (CD34+) enumeration; single platform (SP) and dual platform (DP). While several studies reported comparable results, others suggested superiority of the SP method. This study evaluated variations between both methods using a modified workflow. A total of 54 fresh and thawed specimens, including mobilized peripheral blood, apheresis products, and umbilical cord blood, were analyzed using both methods. High concordance between SP and DP methods was observed for absolute viable CD34+ counts in fresh and thawed specimens (p = 0.088 and 0.427, respectively), as well as for CD34+ viability (p = 0.085 and 0.801). Absolute viable WBC counts were comparable between methods in thawed specimens (p = 0.124), whereas a modest statistical variation was observed in fresh specimen group (p = 0.039), largely influenced by umbilical cord blood samples. Variation in absolute viable CD34+ counts remained within clinically acceptable limits, with median variations of 2.4 for fresh and 1.4 for thawed samples. SP and DP methods demonstrated high concordance for absolute viable CD34+ enumeration and CD34+ viability in fresh and thawed specimens. Although a modest variation in viable WBC counts was observed in fresh samples, this did not affect CD34+ enumeration and remained clinically acceptable. While SP provides a standardized approach, the DP method offered greater gating flexibility, with fewer technical resources required, and was approximately 70% more cost-effective, supporting its use as a practical alternative in appropriate laboratory settings.
Rapid advancements in mass and flow cytometry technologies have allowed researchers to generate and analyze high-dimensional single cell datasets, often utilizing upwards of 40 protein markers. Such high-parameter cytometry is increasingly used in longitudinal immunological studies, but technical variations across experimental batch runs can confound biological signals. To mitigate the impact on downstream analyses, many studies include reference control samples in every run, and several approaches exist to adjust for batch effects. However, tools that objectively identify problematic batches and markers present within a dataset are limited. We introduce CytoBatchFlagR, a comprehensive and interpretable tool designed to flag batch-related problems at the marker and cell cluster level based on robust statistical evaluations. Batch and marker variations are assessed based on median signal intensities of negative and positive cell populations and positive cell frequencies, along with Earth Mover's Distance (EMD) of signal intensity distributions. Additionally, CytoBatchFlagR identifies cell type specific batch problems via unsupervised clustering. The tool is suitable for mass and flow cytometry datasets where it objectively detects distinct types of batch issues. We developed and tested CytoBatchFlagR using three cytometry datasets to demonstrate its utility and performance. We also demonstrated CytoBatchFlagR's effectiveness in assessing datasets that include or lack reference controls. CytoBatchFlagR improves quality control by enabling objective identification of technical variations that may impact downstream analysis in high-parameter cytometry data. The tool uses a series of complementary metrics to identify potential batch-related problems at the marker and cell population level and presents the results through interpretable visualizations. This allows users to make informed decisions about whether to apply batch correction or exclude specific batches or markers from downstream analyses. CytoBatchFlagR is freely available as R scripts, with documentation and a tutorial to help users get started.
