FlowRepository is a database of flow cytometry experiments where you can query and download data collected and annotated according to the MIFlowCyt standard. It is primarily used as a data deposition place for experimental findings published in peer-reviewed journals in the flow cytometry field.

A database of flow cytometry experiments where users can query and download data collected and annotated according to the MIFlowCyt data standard.

Contains preprocessed single-cell data for sketching single-cell samples. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.

• The HIV Vaccine Trials Network (HVTN) Flow Cytometry dataset (hvtn_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZZZV (http://flowrepository.org/id/FR-FCM-ZZZV).
• The preeclampsia CyTOF dataset (preeclampsia_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZYRQ (http://flowrepository.org/id/FR-FCM-ZYRQ).
• The NK-Cell CyTOF dataset (nk_cell_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/ncomms14825) was originally downloaded from (https://github.com/eiriniar/CellCnn).
• The multiple sclerosis (MS) single-cell RNA sequencing dataset of peripheral blood samples (ms_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/s41467-019-14118-w) was originally accessed from the Gene Expression Omnibus using the accession code GSE138266 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266).

Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.

This dataset contains a diverse collection of pre-processed flow cytometry data assembled to support the training and evaluation of machine learning (ML) models for the gating of cell populations. The data was curated through a citizen science initiative embedded in the EVE Online video game, known as Project Discovery. Participants contributed to scientific research by gating bivariate plots generated from flow cytometry data, creating a crowdsourced reference set. The original flow cytometry datasets were sourced from publicly available COVID-19 and immunology-related studies on FlowRepository.org and PubMed. Data were compensated, transformed, and split into bivariate plots for analysis. This datset includes: 1) CSV files containing two-channel marker combinations per plot, 2) A SQL database capturing player-generated gating polygons in normalized coordinates, 3) Scripts and containerized environments (Singularity and Docker) for reproducible evaluation of gating accuracy and consensus scoring using the flowMagic pipeline, 4) Code for filtering bot inputs, evaluating user submissions, calculating F1 scores, and generating consensus gating regions. This data is especially valuable for training and benchmarking models that aim to automate the labor-intensive gating process in immunological and clinical cytometry applications.

Processed data computed using R packages CytoGLMM and cytoeffect. Raw data available here.

The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.

link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3

The aim of this study was to describe the changes that cladribine tablets effectuate in the immunological profile of patients with relapsing remitting multiple sclerosis, and to determine if the composition of the immunological profile differs between responders and nonresponders. This dataset contains an interim analysis of the first 25 patients with a follow-up of 1 year. This dataset contains the results of the 16s-23s interspace profiling of fecal and oral samples and of the CyTOF analysis. The results of the CyTOF analysis can also be found in FlowRepository upon publication of the manuscript (https://flowrepository.org/, FR-FCM-Z8CL).

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry 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 computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. 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. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods.Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

The aim of this study was to describe the changes that cladribine tablets effectuate in the immunological profile of patients with relapsing remitting multiple sclerosis, and to determine if the composition of the immunological profile differs between responders and nonresponders. This dataset contains an interim analysis of the first 25 patients with a follow-up of 1 year. This dataset contains the results of the 16s-23s interspace profiling of fecal and oral samples and of the CyTOF analysis. The results of the CyTOF analysis can also be found in FlowRepository upon publication of the manuscript (https://flowrepository.org/, FR-FCM-Z8CL).