PT  - JOURNAL ARTICLE
AU  - Luigi Dolcetti
AU  - Paul R Barber
AU  - Gregory Weitsman
AU  - Selvam Thavaraj
AU  - Kenrick Ng
AU  - Julie Nuo En Chan
AU  - Piers Patten
AU  - Rami Mustapha
AU  - Jinhai Deng
AU  - Tony Ng
TI  - RUNIMC: An R-based package for imaging mass cytometry data analysis and pipeline validation
AID  - 10.1101/2021.09.14.460258
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.09.14.460258
4099  - http://biorxiv.org/content/early/2021/09/15/2021.09.14.460258.short
4100  - http://biorxiv.org/content/early/2021/09/15/2021.09.14.460258.full
AB  - We propose a novel pipeline for the analysis of imaging mass cytometry data, comparing an unbiased approach, representing the actual gold standard, with a novel biased method. We made use of both synthetic/ controlled datasets as well as two datasets obtained from FFPE sections of follicular lymphoma, and head and neck patients, stained with a 14 and 29-markers panels respectively. The novel pipeline, denominated RUNIMC, has been completely developed in R and contained in a single package. The novelty resides in the ease with which multi-class random forest classifier can be used to classify image features, making the pathologist’s and expert classification pivotal, and the use of a random forest regression approach that permits a better detection of cell boundaries, and alleviates the necessity of relying on a perfect nuclear staining.Competing Interest StatementThe authors have declared no competing interest.