Phila. • Immunology research asst 2019 Columbia University • MS, PhD, biostatistics 2024 Assistant Professor • Colorado/Emory • Biostatistics and Bioinformatics
• A great thing about academia is you get to keep learning • A great thing about biostatistics is dabbling in different scientific areas • For example, cancer biology 7
to multiple types of proteins in the tissue that are tagged • Each protein label is called a marker • Phenotypic markers: used to define cell and/or tissue type • Functional markers: inform cell function • Present across multiple cell types 11
to multiple types of proteins in the tissue that are tagged • Immunofluorescence based • Proteins stained with fluorescent antibodies then imaged using fluorescence microscopy • Mass cytometry based • Proteins tagged with metal isotypes (IMC, MIBI)
are multichannel TIFF (.tif) files • Each channel is a different protein marker • Each pixel contains a continuous intensity value for each marker • Example below with non-small cell lung cancer data • 8 channels, 3 shown (Left to Right: composite image, nuclei, CK, CD8) 14
(TME) is the area within and surrounding a tumor, including tumor cells, infiltrating immune cells, blood vessels, and other tissue • What percentages of immune cell subtypes are present before and after chemotherapy? • Do patients with high spatial clustering of B-cells and Macrophages survive longer? 15 Image from Polidoro et. al. World J. Gasteroentrol., 26(33) (2020)
package addresses this • Bioconductor ExperimentHub package as of April 2022 • 2 large datasets from my collaborators at CU-Anschutz VectraPolarisData Package 22
• Operates on pixel intensity values • Cell-level processing: work with tabular data after cell segmentation • Operates on median or mean intensity values aggregated at cell level • Segmentation • Pixel-level • Phenotyping • Pixel-level or cell-level Statistical image processing 26
to identify nucleus 2. Cell membrane or cytoplasm markers used to draw boundary around nucleus Cell segmentation 27 Cell 1 Pixel 1 Marker a Marker b Pixel n Marker a Marker b
analysis software • GUI-based, user-friendly • No manually segmented data required • GUI-based open-source software • CellProfiler, ilastik, QuPath • User-friendly for non-computer scientists/statisticians • No manually segmented data required* • Deep-learning based open-source software • Best performance* • Need manually segmented data* • Hard to adapt without computational expertise Segmentation approaches 28
expression values • Cell labeling Conceptually, goal is to create a “cut point” in marker intensities where cells are either positive or negative for a marker 29
to designate “marker positive” and “marker negative” cells • Unsupervised clustering methods • Seurat, Phenograph contain built-in software • Most developed for other single-cell analysis • Semi-supervised • GammaGateR: new approach by Xiong, Vandekar, Bioinformatics 2024 Phenotyping approaches 31
patient survival in ovarian cancer • Cell locations from an ovarian cancer tissue sample • Green cells are in tumor area • Pink cells are in stromal area • Black cells are macrophages • Steps of analysis: 1. Quantify spatial clustering of macrophages 2. Use spatial metrics as covariates in regression model How to best quantify spatial clustering? • Need to do feature extraction 33
Highly heterogeneous structure • No pixelwise correspondence across images • Extract features instead to obtain correspondence across images • These are numbers that summarize characteristics of each image • Spatial summary statistics are metrics that summarize spatial clustering Image feature extraction 34
cells in an image • Often separated by tumor/stroma • Univariate spatial summary statistics • Clustering of cells of one type • Bivariate spatial summary statistics • Co-expression or co-clustering of two cell types (e.g. T-cells and B-cells) • Ripley’s K function is a popular metric of spatial clustering 35
• The standardized average number of neighbors of a cell within radius r • Has theoretical value of 𝜋𝑟! under complete spatial randomness (CSR) • Compare observed to theoretical value to assess clustering Ripley’s K-function 36
on point processes • Locations of cells random variable • Gray cells: background cells • Red cells: immune cells • Goal is to quantify deviations from complete spatial randomness (CSR) • ! 𝐾 𝑟 − 𝐸!"# 𝐾 𝑟 used to predict patient survival 37
Nearest-neighbor G function • Examines probability of encountering a neighboring cell • Moran’s I • Can be used to quantify continuous marker intensity values • Local and global versions • Univariate and bivariate Other spatial summary statistics 38
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