RUNIMC: An R-based package for imaging mass cytometry data analysis and pipeline validation
Dolcetti, L.; Barber, P. R.; Weitsman, G.; Thavaraj, S.; Ng, K.; Chan, J. N. E.; Patten, P.; Mustapha, R.; Deng, J.; Ng, T.
Show abstract
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 pathologists 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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Semi-automated background removal limits loss of data and normalises the images for downstream analysis of imaging mass cytometry data 96%
- Classification of human white blood cells using machine learning for stain-free imaging flow cytometry 95%
- OPTIMAL: An OPTimsed Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration 94%
Similar papers in this journal
- Visualization & Quality Control Tools for Large-scale Multiplex Tissue Analysis in TissUUmaps 3 95%
- The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure 94%
- Seeing or believing in hyperplexed spatial proteomics via antibodies. New and old biases for an image-based technology. 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.