Feature-based Comparison of Flow Cytometry Data

174 Pages·2017·6.23 MB·English

Feature-based Comparison of Flow
Cytometry Data
by
Alice Yue
B.Sc., University of Victoria, 2015
Thesis Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
in the
School of Computing Science
Faculty of Applied Science
(cid:13)c Alice Yue 2017
SIMON FRASER UNIVERSITY
Summer 2017
All rights reserved.
However, in accordance with the Copyright Act of Canada, this work may be
reproduced without authorization under the conditions for “Fair Dealing.”
Therefore, limited reproduction of this work for the purposes of private study,
research, education, satire, parody, criticism, review and news reporting is likely
to be in accordance with the law, particularly if cited appropriately.
Approval
Name: Alice Yue
Degree: Master of Science (Computing Science)
Title: Feature-based Comparison of Flow Cytometry
Data
Examining Committee: Chair: Joseph Peters
Professor
Cedric Chauve
Senior Supervisor
Professor
Department of Mathematics
Simon Fraser University
Ryan Brinkman
Co-supervisor
Professor
Department of Medical Genetics
University of British Columbia
Leonid Chindelevitch
Supervisor
Assistant Professor
School of Computing Science
Simon Fraser University
Kay C. Wiese
Internal Examiner
Associate Professor
School of Computing Science
Simon Fraser University
Date Defended: 8 August 2017
ii
Abstract
Flow cytometry (FCM) bioinformatics is a sub-field of bioinformatics, aimed at devel-
oping effective and efficient computational tools to store, organize, and analyze high-
throughput/dimensional FCM data. Flow cytometers are capable of analyzing thousands
of cells per second for up to 40 features. These features primarily signal the presence of
different proteins on cells in the bloodstream. Hence contributing large amounts of data
towards the big biological data paradigm. The data that a flow cytometer outputs from a
biological sample, is called a FCS file.
The International Mouse Phenotyping Consortium (IMPC) is a collaboration between 23
international institutions and funding organizations. Its aim is to decipher the function
of 20,000 mouse genes. IMPC is doing so by breeding mice with a certain gene knocked
out (KO), cancelling the function of that gene. In turn, FCM is used to measure the
immunological changes correlated to this knockout.
Many tools exist to classify FCS files. However, there is a lack of tools to conduct unsu-
pervised clustering of FCS files. One goal of IMPC is to compare and contrast KO genes,
hence IMPC becomes a prime motivation for this problem.
As such, this thesis outlines a data processing pipeline used to isolate features for each FCS
file. We then test the different types of features extracted on a benchmark data set from
the FlowCAP-II challenge, containing data from healthy persons and patients with AML
(acute myeloid leukemia). We then evaluate how well these features separate out FCS files
of different origin (i.e. healthy vs AML).
Keywords: Bioinformatics; Flow Cytometry; Feature Design
iii
Dedication
To my mother.
iv
Acknowledgements
I would like to show my gratitude to my senior and co-supervisors Dr. Cedric Chauve
and Dr. Ryan Brinkman. Thank you for the opportunity to learn and solve meaningful
problemstogether–takingthetimeformanythoughtprovokingdiscussions, andyourmost
generous support and patience throughout my masters’ education. I am honoured to work
with you, as my supervisors and as life mentors.
Iwouldalsoliketoexpressmyappreciationstomydefencecommitteeforyourinvaluable
feedback. Thank you to my supervisory committee member, Dr. Leonid Chindelevitch for
always providing inspiring food for thought during the bioinformatics course and program
meetings, Dr. Kay Wiese for your encouragement and for agreeing to examine my super
long MSc thesis, and Dr. Joseph Peters for being the defence chair and your patience
towards my un-finish-able list of algorithm questions.
Last but not least, thank you to my family, friends, and lab-mates for your companion-
ship, and support.
v
Table of Contents
Approval ii
Abstract iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of Tables ix
List of Figures xii
1 Introduction and Background 1
1.1 Flow Cytometry (FCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Mass Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Flow Cytometry (FCM) Bioinformatics . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Motivation and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Flow Cytometry (FCM) Bioinformatics 10
2.1 FCM Data Processing Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Cell Population Identification . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.1 FCS file Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.2 Biomarker Cell Population Identification . . . . . . . . . . . . . . . . 18
2.3 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Data and Methods 20
3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 IMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 FlowCAP-II AML . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
vi
3.2 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 Compensation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.2 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.3 Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Cell Population Identification . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1 Automated Gating . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2 Cell Population Enumeration . . . . . . . . . . . . . . . . . . . . . . 31
3.3.3 Cell Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Cell Count Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5 Feature Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5.1 Absolute Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5.2 Phenodeviant features . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5.3 Trimmed Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6 Distance Matrix Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.7 Distance Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.7.1 Clustering and Classification Methods . . . . . . . . . . . . . . . . . 45
3.7.2 Scoring Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Results 51
4.1 Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.1.1 External Validation of Classification Results . . . . . . . . . . . . . . 53
4.1.2 Internal and External Validation of Clustering Results . . . . . . . . 57
4.1.3 Internal Validation of Distance Matrices . . . . . . . . . . . . . . . . 58
4.2 AML vs Healthy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.1 External Validation of Classification Results . . . . . . . . . . . . . . 60
4.2.2 Internal and External Validation for Clustering Results . . . . . . . 64
4.2.3 Internal Validation of Distance Matrices . . . . . . . . . . . . . . . . 71
5 Discussion 85
5.1 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1.1 Node vs Edge Features . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1.2 Phenodeviant vs Absolute Features . . . . . . . . . . . . . . . . . . . 86
5.1.3 Trimmed vs Un-trimmed Features . . . . . . . . . . . . . . . . . . . 86
5.1.4 Louvain Clustering Score Exceptions . . . . . . . . . . . . . . . . . . 87
5.2 Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2.1 Node vs Edge Features . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.2 Trimmed vs Un-trimmed Features . . . . . . . . . . . . . . . . . . . 88
5.3 Distance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6 Conclusion and Perspectives 90
vii
6.1 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.2 Results and Discussion Summary . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.4 Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4.1 FCM Data Analysis Pipeline Improvements . . . . . . . . . . . . . . 92
6.4.2 Fine-tuning Phenodeviant Features . . . . . . . . . . . . . . . . . . . 92
6.4.3 FCS file Cluster Comparison . . . . . . . . . . . . . . . . . . . . . . 92
Bibliography 94
Appendix A Code 103
Appendix B Results (Figures) 104
B.1 Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
B.1.1 Internal and External Validation of Clustering Results . . . . . . . . 105
B.1.2 Internal Validation of Distance Matrices . . . . . . . . . . . . . . . . 115
B.1.3 Figures by Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
B.1.4 Figures by Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
B.1.5 Figures by Distance Metric . . . . . . . . . . . . . . . . . . . . . . . 121
Appendix C Results (Tables) 124
C.1 Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
C.1.1 External Validation of Classification Results . . . . . . . . . . . . . . 126
C.1.2 Internal and External Validation of Clustering Results . . . . . . . . 128
C.1.3 Internal Validation of Distance Matrices . . . . . . . . . . . . . . . . 133
C.2 AML vs Healthy (Average of All Panels) . . . . . . . . . . . . . . . . . . . . 135
C.2.1 External Validation of Classification Results . . . . . . . . . . . . . . 135
C.2.2 Internal and External Validation of Clustering Results . . . . . . . . 139
C.2.3 Internal Validation of Distance Matrices . . . . . . . . . . . . . . . . 155
viii
List of Tables
Table 2.1 ExamplesofCellPopulationIdentificationToolscategorizedintothree
types: Supervised Classification, Unsupervised Clustering, and Auto-
mated Gating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Table 3.1 Co-occurrenceTrue/FalsePositive/NegativeDefinitionforanytwoFCS
files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Table C.1 External Validation of Classification Results for the FlowCAP-II data
set on the Panels variable: K-NN scores . . . . . . . . . . . . . . . . . 126
Table C.2 InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the Panels variable: Spectral Clustering scores . . . . . 128
Table C.3 InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the Panels variable: Louvain Clustering scores . . . . . 129
Table C.4 InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the Panels variable: Agglomerative Clustering scores . 130
Table C.5 InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the Panels variable: K-medoid Clustering scores . . . . 131
Table C.6 Internal Validation of Distance Matrices for the FlowCAP-II data set
on the Panels variable: Distance Matrix scores . . . . . . . . . . . . . 133
Table C.7 External Validation of Classification Results for the FlowCAP-II data
set on the AML vs Healthy variable: K-NN scores for average of All
Panels (Trimmed Absolute Features) . . . . . . . . . . . . . . . . . . 135
Table C.8 External Validation of Classification Results for the FlowCAP-II data
set on the AML vs Healthy variable: K-NN scores for average of All
Panels (Trimmed phenodeviant Features) . . . . . . . . . . . . . . . . 136
Table C.9 External Validation of Classification Results for the FlowCAP-II data
set on the AML vs Healthy variable: K-NN scores for average of All
Panels (Un-trimmed Absolute Features) . . . . . . . . . . . . . . . . . 137
Table C.10External Validation of Classification Results for the FlowCAP-II data
set on the AML vs Healthy variable: K-NN scores for average of All
Panels (Un-trimmed phenodeviant Features) . . . . . . . . . . . . . . 138
ix
Table C.11InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Spectral Clustering scores
for average of All Panels (Trimmed Absolute Features) . . . . . . . . 139
Table C.12InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Spectral Clustering scores
for average of All Panels (Trimmed phenodeviant Features) . . . . . . 140
Table C.13InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Spectral Clustering scores
for average of All Panels (Un-trimmed Absolute Features) . . . . . . . 141
Table C.14InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Spectral Clustering scores
for average of All Panels (Un-trimmed phenodeviant Features) . . . . 142
Table C.15InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Louvain Clustering scores
for average of All Panels (Trimmed Absolute Features) . . . . . . . . 143
Table C.16InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Louvain Clustering scores
for average of All Panels (Trimmed phenodeviant Features) . . . . . . 144
Table C.17InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Louvain Clustering scores
for average of All Panels (Un-trimmed Absolute Features) . . . . . . . 145
Table C.18InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Louvain Clustering scores
for average of All Panels (Un-trimmed phenodeviant Features) . . . . 146
Table C.19InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Agglomerative Clustering
scores for average of All Panels (Trimmed Absolute Features) . . . . . 147
Table C.20InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Agglomerative Clustering
scores for average of All Panels (Trimmed phenodeviant Features) . . 148
Table C.21InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Agglomerative Clustering
scores for average of All Panels (Un-trimmed Absolute Features) . . . 149
Table C.22InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: Agglomerative Clustering
scores for average of All Panels (Un-trimmed phenodeviant Features) 150
Table C.23InternalandExternalValidationofClusteringResultsfortheFlowCAP-
II data set on the AML vs Healthy variable: K-medoid Clustering
scores for average of All Panels (Trimmed Absolute Features) . . . . . 151
x