RUNIMC: An R-based package for imaging mass cytometry data analysis and pipeline validation

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 pathologist 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.

Immunomodulatory biomimetic nanoparticles target articular cartilage trauma after systemic administration

“…The sections were ablated with Hyperion (Fluidigm) for data acquisition. IMC image analysis was carried out as previously described [ 49 ]. Briefly, two random forest models for classification (based on cell type) and regression (based on relative position of a pixel within a cell) were generated on a subset of the study images, training the algorithm to detect cells based on the expression of main lineage markers (i.e., lymphoid markers, CD4 and CD8, myeloid markers, Ly6G and F4/80, and the non-immune marker α-SMA) and creating a prediction map for each cluster.…”

CytoPb: Segmentation-free and threshold-free analysis of highly multiplexed images of biological tissue

“…In Figure 2 the results from this method are compared with three data sets with different acquisition and segmentation methods: Bone marrow IMC using the Steinbock analysis package 2 , Head and Neck Cancer tissue using RUNIMC 7 , and the Colorectal Cancer tissue CODEX data and method of Schurch et al 8 Efforts were made to match the cell-type gating strategies. The segmentation methods included corrections for batch effects between samples, CytoPb use no corrections.…”