CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry DOI Creative Commons
Md. Hasanul Banna Siam, Md Akkas Ali,

Donald Vardaman

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 7, 2024

Abstract 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 Streamlit, incorporates libraries Scanpy for single-cell analysis, Pandas and PyArrow efficient handling, Matplotlib, Seaborn, Plotly creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging annotation. Using CAFE, demonstrated analysis human PBMC dataset 350,000 cells identifying 16 distinct cell clusters. can generate publication-ready figures in real time via interactive slider controls dropdown menus, eliminating the need coding expertise making HD accessible all. licensed under MIT freely available at https://github.com/mhbsiam/cafe .

Язык: Английский

CAFE: An Integrated Web App for High-Dimensional Analysis and Visualisation in Spectral Flow Cytometry DOI Creative Commons
Md. Hasanul Banna Siam, Md Akkas Ali, Satwik Acharyya

и другие.

Опубликована: Янв. 7, 2025

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 Streamlit, incorporates libraries Scanpy for single-cell analysis, Pandas and PyArrow efficient handling, Matplotlib, Seaborn, Plotly creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging annotation. Using CAFE, demonstrated analysis human PBMC dataset 350,000 cells identifying 16 distinct cell clusters. can generate publication-ready figures in real time via interactive slider controls dropdown menus, eliminating the need coding expertise making HD accessible all. licensed under MIT freely available at https://github.com/mhbsiam/cafe.

Язык: Английский

Процитировано

0
CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry DOI Creative Commons
Md. Hasanul Banna Siam, Md Akkas Ali,

Donald Vardaman

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 7, 2024

Abstract 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 Streamlit, incorporates libraries Scanpy for single-cell analysis, Pandas and PyArrow efficient handling, Matplotlib, Seaborn, Plotly creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging annotation. Using CAFE, demonstrated analysis human PBMC dataset 350,000 cells identifying 16 distinct cell clusters. can generate publication-ready figures in real time via interactive slider controls dropdown menus, eliminating the need coding expertise making HD accessible all. licensed under MIT freely available at https://github.com/mhbsiam/cafe .

Язык: Английский

Процитировано

0