Publications

Astrolabe is enabling research organizations to analyze data and reach insights faster than ever

Astrolabe successfully analyzed a large data set (2,300 samples) using an automated, standardized pipeline, and in a few hours (instead of weeks of manual work)

Astrolabe analyzed clinical data using a CyTOF panel that was customized for clinical trials

In this analysis of processing conditions, Astrolabe accurately reproduces the conclusions from the manual comparison. Additionally, Astrolabe results are available in the companion app for the paper.

This immune monitoring workflow emphasizes maximum comparability between patients, and includes automated, standardized analysis using the Astrolabe platform.

Astrolabe was utilized to identify canonical populations and how they change between normal tissue and pancreatic adenocarcinoma. In addition, the platform was central to dealing with batch effects.

Astrolabe was used to analyze whole blood samples from 20 patients and 10 healthy controls. Results were utilized in downstream statistics across several clinical covariates.

CyTOF data from 12 patients was analyzed using Astrolabe, which additionally identified relevant changes in Tregs and EMRA CD8+ T Cells.

PBMCs from 15 WM and IgM MGUS patients was analyzed using Astrolabe. Differential abundance analysis identified changes in numerous populations of interest.

The Astrolabe labeling algorithm and MDS map were used to analyze nasal polyps from chronic rhinosinusitis (CRS) patients.

A custom Astrolabe labeling hierarchy was tailored for the mouse central nervous system.

Astrolabe was used to identify canonical T cell populations, including γδ T cells, and for unsupervised clustering.

According to the authors, Astrolabe debarcoding and subset labeling correlated well with manual gating approaches.

Astrolabe was used to identify canonical T cell populations, including γδ T cells, and for unsupervised clustering.




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