Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.

ABSTRACT: Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: han

SUBMITTER: Hickey JW 

PROVIDER: S-EPMC8415085 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature