Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

Czech, E.; Aksoy, B. A.; Aksoy, P.; Hammerbacher, J.

doi:10.1101/460980

Home/Archive/bioRxiv

bioRxiv · 10.1101/460980

Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

Czech, E.·Aksoy, B. A.·Aksoy, P.·Hammerbacher, J.

View original source ↗Export metadata ↓

Abstract

BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.\n\nResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.\n\nConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.

Source connections

Published article ↗

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Czech, E., Aksoy, B. A., Aksoy, P., Hammerbacher, J.. 2018-11-04. Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. https://doi.org/10.1101/460980

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Accelerating discovery and integration from RNA to Protein with R2T2P

Integrating transcriptomics and proteomics data remains an unmet challenge in the post-genomics era, discouraging researchers from pursuing integrative approaches to molecular biology research. Leveraging the power of translation profiling, we present RNA to Translation to Protein (R2T2P), a computational platform integrating genome-guided transcriptome assembly with identification of translation and proteins, including a tailored differential analysis framework and support for multiple reference transcriptomes. We applied R2T2P to investigate a cellular model of facioscapulohumeral muscular dystrophy (FSHD), where ectopic expression of the DUX4 retrogene in skeletal muscle triggers significant alterations in transcription and RNA metabolism. We uncovered hundreds of uncharacterized, highly overexpressed RNAs, including antisense and alternative transcripts, and quantified their contribution to translatome and proteome. Re-analysis of expression datasets from patient-derived myotubes and muscle biopsies validated this alternative transcriptome in FSHD patients, adding a reservoir of bona-fide novel molecular actors in a rare disease. Available at https://github.com/nfdata-omics/R2T2P/, R2T2P is a flexible and powerful workflow in modern integrative biology.

bioinformatics↗

bioRxiv2026

MorphoANOVA: Dissecting Histology-Gene Expression Relationships in Spatial Transcriptomics

Spatial transcriptomics (ST) measures the expression of hundreds to thousands of genes within tissue architecture, often accompanied by histology images. However, it remains challenging to systematically characterize which genes exhibit associations with tissue morphology, whether these associations vary spatially, and which genes show additional spatial expression patterns beyond those captured by morphology. Here we introduce MorphoANOVA, a gene-wise statistical framework for characterizing morphology--expression relationships across genes measured by ST. Using nested spatially varying coefficient models and sequential ANOVA, MorphoANOVA assesses slide-wide associations with an H\&E-derived morphology feature, location-specific changes in these associations, and additional spatial variation after accounting for the selected feature. The resulting statistics enable gene prioritization and pathway enrichment analysis, while spatial component maps support the interpretation of individual genes. Simulations demonstrate MorphoANOVA's ability to distinguish the modeled components and recover their spatial patterns. Applications to human cortex and breast cancer identify biological programs associated with different morphology--expression patterns and reveal distinct relationships with cell composition. Across three HER2-positive breast cancer patients, invasive-cancer marker genes consistently exhibit stronger evidence of location-specific morphology associations. MorphoANOVA thus provides a framework for systematically interpreting spatial gene-expression patterns in relation to tissue morphology.

bioinformatics↗

bioRxiv2026

Evaluation effects can rival reported concordance gains in cross-cancer survival prediction

Studies of cross-cancer learning for survival prediction from tumour omics have reported concordance gains of 0.020 and 0.045, but how such gains compare with evaluation effects remains unclear. We examined these effects across 13 TCGA cancers (5,761 patients, 1,526 disease-specific deaths), using five repeats of five-fold cross-validation and paired bootstrap intervals. When model selection did not use test data, our borrowing model gained 0.009 (95% interval 0.001 to 0.016) over its target-fitted reference when cancers received equal weight (post hoc). Random-effects pooling gave a gain of 0.002 (-0.002 to 0.006), with most weight assigned to cancers where the model made little use of the pretrained score. Choosing the fusion weight on test splits raised the apparent equal-weight gain to 0.017. We reimplemented a published method, VAECox, which showed no pretraining gain over random initialisation at the last or validation-selected training step. Choosing each network's best test step, as the published code does, raised concordance by 0.03 to 0.05 across the 13-cancer and ten-cancer overall-survival settings, but barely changed the difference between networks. In a constructed post hoc experiment, choosing the best test step only for the pretrained network changed the equal-weight mean difference from -0.004 to +0.045 (+0.035 to +0.054). Across seven multi-omics comparisons, estimates from single five-fold runs differed from their cancer's five-run mean by at least 0.02 in 11% to 42% of runs. Concordance was about 0.02 lower at held-out tissue source sites. Interpreting a reported gain therefore requires each model's selection rule, variation across repeated partitions, the weighting of cancers and the evaluation population.

bioinformatics↗