Table of Contents

INTRODUCTION …………………………………………………………………………………………… 1
Bulk vs. single-cell profiling …………………………………………………………………… 1
Approaches to characterizing axes of variation among a collection of cells …. 5
Principal Component Analysis (PCA) ………………………………………………. 6
t-Distributed Stochastic Neighbor Embedding (t-SNE) ……………………….. 7
Uniform Manifold Approximation and Projection (UMAP) …………………. 8
Tree-based approaches …………………………………………………………………. 9
Diffusion maps …………………………………………………………………………… 10
PHATE ……………………………………………………………………………………… 11
Characterizing axes of variation among a collection of multicellular cancer
specimens …………………………………………………………………………………………… 11
Hypothesis ………………………………………………………………………………………….. 15
Specific Aims ………………………………………………………………………………………. 15
Aim 1: Develop a robust tool for uncovering axes of variation among
single-cell biospecimens ………………………………………………………………. 15
Aim 2: Characterize the differing effects of 233 small-molecule inhibitors
on breast cancer epithelial–mesenchymal transition (EMT) ………………. 15
Aim 3: Characterize the immune cell subpopulational variation among
melanomas and among clear-cell renal cell carcinomas (ccRCCs) ……… 15
MATERIALS AND METHODS …………………………………………………………………….. 16
The PhEMD analytical approach ………………………………………………………….. 16
Data collection and processing ……………………………………………………………… 22
Py2T cell culture and stimulation ………………………………………………….. 22
Small-molecule inhibitors …………………………………………………………….. 23
Chronic kinase inhibition screen …………………………………………………… 23
Cell collection ……………………………………………………………………………. 24
Metal-labeled antibodies ……………………………………………………………… 24
Mass-tag cellular barcoding and antibody staining ………………………….. 25
Mass cytometry data processing ……………………………………………………. 25
In-depth analysis of breast cancer EMT cell-state space and drug-inhibitor
manifold from a single mass cytometry run …………………………………………… 26
Integrating batch-effect correction to compare 300 EMT inhibition and
control conditions measured in five experimental runs …………………………… 27
Intrinsic dimensionality analysis of the EMT perturbation state space …….. 28
Imputing the effects of inhibitions based on a small measured dictionary …. 29
Incorporating drug-target binding specificity data to extend the PhEMD
embedding and predict the effects of unmeasured inhibitors on TGFβ
induced breast cancer EMT …………………………………………………………………. 30
Predicting drug-target binding specificities based on PhEMD results from
EMT perturbation experiment ……………………………………………………………… 32
Generation and analysis of dataset with known ground-truth branching
structure …………………………………………………………………………………………….. 34
Analysis of melanoma single-cell RNA-sequencing dataset ……………………… 35
Analysis of clear cell renal cell carcinoma dataset…………………………………… 35
Statistical methods ………………………………………………………………………………. 36
Data availability ………………………………………………………………………………….. 36
Code availability …………………………………………………………………………………. 36
Author contributions …………………………………………………………………………… 37
RESULTS …………………………………………………………………………………………………….. 38
Overview of PhEMD ……………………………………………………………………………. 38
Comparing specimens pairwise using Earth Mover’s Distance (EMD) …….. 39
Evaluating accuracy of PhEMD in mapping multi-specimen, single-cell
dataset with known ground-truth structure …………………………………………… 41
Assessing the differing effects of selected drug perturbations on EMT in
breast cancer ………………………………………………………………………………………. 43
Batch effect correction in multi-run EMT experiment ……………………….. 44
Cell-subtype definition via manifold clustering ……………………………….. 47
Constructing and clustering the EMD-based drug-inhibitor manifold…… 50
Analyzing EMT perturbations measured in a single CyTOF run …………….. 52
Cell subtype definition via manifold clustering ………………………………… 53
Constructing and clustering the EMD-based drug-inhibitor manifold…… 55
Imputing the effects of inhibitors based on a small measured dictionary ….. 58
Validating the PhEMD embedding using external information on similarities
between small-molecule inhibitors ………………………………………………………… 60
Predicting the effects of three selected inhibitors on breast cancer EMT
relatively to the effects of measured inhibitors based on known drug-target
binding specificities…………………………………………………………………….. 60
Imputing the single-cell phenotypes of three unmeasured inhibitors based
on drug-target similarity to measured inhibitors ……………………………….. 62
Predicting drug-target binding specificities based on PhEMD results from
EMT perturbation experiment ………………………………………………………. 63
PhEMD highlights manifold structure of tumor specimens measured using
CyTOF and single-cell RNA-sequencing ……………………………………………….. 64
DISCUSSION ……………………………………………………………………………………………….. 69
REFERENCES ……………………………………………………………………………………………… 73
SUPPLEMENTARY TABLES ……………………………………………………………………….. 80