Working with Cytoprofiling Data
AVITI24 with Teton™ CytoProfiling provides comprehensive multiomic, multidimensional readouts detecting cell morphology DNA, RNA, proteins, and phosphorylation stats for comprehensive insights into complete biological pathways. You might feel out of your element operating different tools to unlock the analysis power of cytoprofiling data. The collected tutorials describe how to use AVITI24 output data with common community tools.
Explore the data and let the insights unfold!
Running CellProfiler with AVITI24 Data
CellProfiler is an open-source cellular morphology and analysis tool. AVITI24 already uses CellProfiler modules to generate morphology metrics, but you can reanalyze AVITI24 cytoprofiling data or include other CellProfiler modules to your analysis.
Integrating AVITI24 Data with Seurat
Seurat is a package in the software R that enables the analysis and investigation of single-cell multiomic data. This tutorial describes how to convert AVITI24 output data into a format for use with Seurat.
Using Python for Cell Clustering
Single-cell measurements are capable of examining the heterogeneity of cell populations within a single condition or across treatments. One way to quantify and analyze these heterogeneous subpopulations is to group the cell measurements into discrete clusters and then identify the cluster characteristics.
Performing Resegmentation and Cell Assignment Using Python
If you want to improve the segmentation of your data, you can rerun segmentation with the AVITI24 output data. One common framework for resegmentation is Cellpose, which lets you apply various pretrained segmentation models or customize one for your data. Additionally, you can try a different pre-trained Element Biosciences segmentation model than was used on instrument.