Working with cytoprofiling data

AVITI24 with Teton™ CytoProfiling provides comprehensive multiomic, multidimensional readouts detecting cell morphology, DNA, RNA, proteins, and phosphorylation states for comprehensive insights into complete biological pathways. The following tutorials describe how to use AVITI24 output data with common community tools.

Note:

Element does not provide support for community-developed tools and makes no guarantees regarding their function or performance. Contact tool developers with any questions.

Getting started with Bases2Fastq for cytoprofiling

Run Bases2Fastq locally on cytoprofiling data with Docker or a static binary on Linux or Windows.

Getting started with Cells2Stats for cytoprofiling

Run Cells2Stats locally to generate cell statistics from AVITI24 cytoprofiling data for downstream analysis.

Running CellProfiler with AVITI24 data

Re-analyze AVITI24 cytoprofiling data or add morphology metrics using open-source CellProfiler modules.

Integrating AVITI24 data with Seurat

Convert AVITI24 cytoprofiling output into a Seurat-compatible format for single-cell multiomic analysis in R.

Using Python for cell clustering

Use Python and the Leiden algorithm to cluster AVITI24 cytoprofiling cells and visualize populations in a UMAP.

Performing resegmentation and cell assignment using Python

Apply Cellpose or Element segmentation models in Python to improve cell segmentation on AVITI24 output.