MI_normalization

University of Michigan tutorial- segmentation, normalization, and phenotyping

Transcript

1. Statistical Image Processing Segmentation, normalization, and cell phenotyping 1
2. • Pixel level processing: work with multichannel tiff files directly • 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 • Normalization • Pixel-level or cell-level • Phenotyping • Pixel-level or cell-level Statistical image processing 2
3. Cell segmentation 3
4. Identifies cells and nuclei in image 1. Nucleus channel used to identify nucleus 2. Cell membrane or cytoplasm markers used to draw boundary around nucleus Segmentation 4
5. • Proprietary software • inForm by Akoya, Halo automated image analysis software • GUI-based, user-friendly • No manually segmented data required • GUI-based open-source software • CellProfiler, ilastik • 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 5
6. Segmentation- Mesmer 6 Greenwald et. al. Nature Biotechnology, 2022.
7. Cell phenotyping 7
8. Cell phenotyping is the process of identifying cell types from marker expression values • Conceptually, goal is to create a “cut point” in marker intensity values where cells are either positive or negative for a marker • Can be performed at pixel or cell level Phenotyping 8
9. Cell types by immune markers Thanks to Brooke Fridley and Alex Soupir at the Moffitt Cancer Center for this image
10. • Can be unsupervised, but often requires expert intervention for cell annotation and validation • Not specific to multiplex imaging • Flow and mass cytometry, single-cell RNA-seq • Multiplex imaging has unique challenges • Segmentation error leads to phenotyping error • Hard to differentiate between “marker positive” and “marker negative” Phenotyping 10
11. • Marker intensity distributions are highly right skewed • Often no clear bimodality • Sensitive to upstream image processing • Normalization • Cell segmentation Phenotyping challenges for MI data 11
12. • Marker intensity distributions are highly right skewed • Often no clear bimodality • Sensitive to upstream image processing • Normalization • Cell segmentation Phenotyping challenges for MI data 12
13. • Marker gating • Determine cutpoint in marker intensity histogram 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 • inForm/Halo proprietary software use manual gating to guide phenotyping • MAUI/CU-Anschutz: Deep-learning based pixel-level • Astir-: Deep-learning based cell-level Phenotyping approaches 13
14. Image normalization 14
15. The slide-to-slide problem 15 • Tissues placed on a slide, each contains (10s to 100s) of images • Several slides are imaged in the same experiment • Multiple sources of noise introduced each time: optical effects, instrument parameter tuning, different times of staining for antibodies • Large batch effects!
16. Image transformation, normalization, and batch correction are used to make the data more appropriate for downstream analysis by removing non-biological biases in marker intensity distributions • Transformations: log, arcsinh, square root • Make data more normally distributed, do not adjust for systematic effects • Normalization: adjusts distribution of marker intensities in each slide, image, or channel separately to make distributions appear more similar • Batch correction: explicitly removes systematic bias using variables that account for processing steps Normalizing multiplex imaging data
17. • Normalization makes the same tissue appear similar across slides • Challenging in MI because • No gold standard exists • Slides differ in their composition Evaluating methods for normalization and batch correction 17 Unnormalized Normalized MAP06025 MAP00083 MAP03361 Bottom of crypt Interior of crypt Stroma Top of crypt
18. • Histograms reveal differences in images intensities across subjects and slides • Harris et. Al 2022 explored combinations of batch correction and normalizations • Developed new framework for evaluating quality of normalization • R package mxnorm Batch effects in multiplex imaging data 18 Harmonization method Transformation /Normalization
19. • Most spatial-omics data lack ground truth for evaluating slide effects • Harris et. al 2022 established one 1. Alignment of marker densities 2. Cell phenotyping discordance 3. Proportion of variance due to slide • Mean division is simple and worked well Batch correction evaluation framework 19 Alignment of marker densities
20. • Most spatial-omics data lack ground truth for evaluating slide effects • Harris et. al 2022 established one 1. Alignment of marker densities 2. Cell phenotyping discordance 3. Proportion of variance due to slide • Mean division is simple and worked well Batch correction evaluation framework 20 Cell phenotyping discordance
21. • Most spatial-omics data lack ground truth for evaluating slide effects • Harris et. al 2022 established one 1. Alignment of marker densities 2. Cell phenotyping discordance 3. Proportion of variance due to slide • Mean division is simple and worked well Batch correction evaluation framework 21 Proportion of variance due to slide
22. 22 • Allows users to easily allow evaluate normalization procedures in their own data • Allows default normalization options (from Harris et al. 2022) or user specified • mxnorm can be used to evaluate new normalization methods in future papers
23. • Functional markers are not used to define phenotypes • Inform cell function and can be present be present across multiple cell types • Interested in differences in expression of functional expression across cell or patient populations of interest • whether abundance of PD-L1 positive cells differ between responders and non-responders to an immunotherapy • Still should be normalized! • For analysis, using continuous valued intensity is better than thresholding • Methods for differential expression analysis for ST data can be used • Seal et. al. 2022 provides a method to cluster based on marker densities Analysis of functional markers 23