Working with cell screening data
Tutorials for analyzing AVITI24™ cell screening data.
AVITI24 with Cytos™ provides comprehensive multiomic, multidimensional readouts detecting cell morphology, DNA, RNA, proteins, and phosphorylation states for comprehensive insights into complete biological pathways. The following tutorials cover processing runs, refining cell segmentation, and analyzing Spatial Data Objects with the multiomics Python package and community tools.
Element Biosciences 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 cell screening
Run Bases2Fastq locally on cell screening data with Docker or a static binary on Linux or Windows.
Getting started with Cells2Stats for cell screening
Run Cells2Stats locally to generate cell statistics from AVITI24 cell screening data for downstream analysis.
Evaluating and applying alternate segmentation models
Evaluate pretrained Element and Cellpose segmentation models on AVITI24 cell screening data, run full resegmentation, and re-run Cells2Stats to regenerate the cell table.
Clustering cells with Python
Load a 3′ transcriptome table from a Spatial Data Object, filter and normalize it, and cluster cells with Leiden and UMAP in Scanpy.
Analyzing an optical pooled CRISPR screen
Call CRISPR guides, compare knockout and non-targeting cells in 3′ RNA and CellPaint morphology, and embed protein IF state from a public AVITI24 OPS dataset.
Measuring a 3′ treatment response
Run well-level pseudobulk differential expression and score a pathway in single cells from a public AVITI24 3′ transcriptome dataset.