Workshop on Statistical Analysis of single-cell protein data

Pacific Symposium on Biocomputing (PSB) 2024

DESCRIPTION

Immune modulation is considered a hallmark of cancer initiation and progression and immune cell density has been consistently associated with outcomes of cancer patients. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor immune microenvironment (TIME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in the TIME with enhanced reproducibility and accuracy. Generally, mIF data has been used to examine the presence and abundance of immune cells in the tumor; however, this aggregate measure assumes uniform patterns of immune cells throughout the tumor and overlooks spatial heterogeneity. Recently, the spatial contexture of the TIME has been explored with a variety of methods. In this session, speakers will present some of the state-of-the-art statistical methods for assessing the TIME from mIF data.

The workshop will consist of five 30-minute talks on state-of-the-art computational methods for spatial biology analysis of single cell protein data. There will also be a 30-minute forum for open discussion.

AGENDA

ORGANIZER:

Brooke L. Fridley, PhD, Moffitt Cancer Center

SPEAKERS:

Inna Chervoneva, PhD, Thomas Jefferson University

Simon Vandekar, PhD, Vanderbilt University

Brooke L Fridley, PhD, Moffitt Cancer Center

Julia Wrobel, PhD, University of Colorado / Emory University

Siyuan Ma, PhD, Vanderbilt University

TOPICS TO BE COVERED IN WORKSHOP:

· Overview of traditional and non-spatial statistical methods for analysis of mIF data

· Normalization and phenotyping of mIF data

· Spatial clustering of mIF data using a variety of measures (e.g., Ripley’ K, G Cross)

· Functional data analysis for mIF data

· Cell-Cell Colocalization using mixed models applied to mIF data