BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding

Abstract

Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE’s pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters.

Seeing or believing in hyperplexed spatial proteomics via antibodies. New and old biases for an image-based technology

“…BRAQUE introduces an innovative data pre-processing step, Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median [19]. It is therefore able to further subdivide signals in the low range and feed these discretized values to the DRAAP.…”

“…BRAQUE [19], an acronym for Bayesian Reduction for Amplified Quantization in UMAP Embedding, has been developed for the global analysis of individual cells in tissue sections stained in IF with multiple biomarkers and uses dimensionality reduction algorithms. Is a Python pipeline for automated cluster enhancing, identification, and characterization.…”

“…We applied to the HubMap dataset BRAQUE [19], a dimensionality reduction algorithms-based analytical pipeline (DRAAP) designed for the spatial Ab-based proteomic data in multiplex, which do not requires pre-definition of positive signal thresholds. BRAQUE was able to identify 12 clusters containing CK+ cells for a total of 16,698 cells out of 188,450 (9.24%) (Table 1, Supplementary Tables and Supplementary Fig.…”

“…By analyzing a public human lymph node dataset with a custom bioinformatic pipeline, BRAQUE [19], we found that the human eye is dispensable for the analysis of in situ hyperplexed multistainings.…”

Quantitative multiplex immunohistochemistry with colorimetric staining (QUIVER) may still benefit from MILAN

“…Tissue antigenic epitopes are preserved: after more than 30 cycles of staining and stripping the signal intensity variations are less than 10% for six of eight antibodies tested (the other two increased the signal) (see Suppl; Fig. 1 in [ 16 ] and [ 22 ]). This has been demonstrated by quadruplicate 10-cycle experiments [ 2 ], compared to a single cycle in the Shahidehpour, et al manuscript.…”