Description

Unsupervised machine learning for cell type identification in multiplexed imaging using protein expression and cell neighborhood information without ground truth

Input

name:type
description
pattern

meta:map

Groovy Map containing sample information e.g. [ id:'test']

img_data:file

Quantification table with single cells as rows, markers (e.g. CD3 or CD8 but names do not have to match exactly) and X/Y coordinates as columns

*.csv

signature:file

Signature Matrix containing the definition of cell types according to markers

*.csv

high_thresholds:file

csv file with user-defined probability high thresholds for anchor cell (row 1) and index cell (row 2) definition

*.csv

low_thresholds:file

optional csv file with user-defined probability low thresholds for anchor cell (row 1) and index cell (row 2) definition

*.csv

Output

name:type
description
pattern

celltypes

meta:map

Groovy Map containing sample information e.g. [ id:'sample1', single_end:false ]

*results.csv:file

File with final celltype annotations concatenated to the original input quantification, due to the mechanism its non-deterministic

*.csv

quality

*quality.csv:file

File with final calculated marker probabilities for inspection, non-deterministic

*.csv

versions

versions.yml:file

File containing software versions

versions.yml

Tools

celesta
Apache-2.0

Automate unsupervised machine learning cell type identification using both protein expressions and cell spatial neighborhood information

github.com/SchapiroLabor/mcmicro-celesta github.com/SchapiroLabor/mcmicro-celesta/blob/main/README.md https://github.com/plevritis-lab/CELESTA 10.1038/s41592-022-01498-z
maintainers
Github user LukasHats
Github user ArozHada
get in touch
Ask a question on Slack Open an issue on GitHub