Bioimage Informatics for Systems Pharmacology
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1 Bioimage Informatics for Systems Pharmacology Authors : Fuhai Li Zheng Yin Guangxu Jin Hong Zhao Stephen T. C. Wong Presented by : Iffat chowdhury
2 Motivation Image is worth for phenotypic changes identification High resolution microscopy, fluorescent labeling Rich in terms of information of biological processes Bioimage informatics
3 Bioimage informatics
4 Image based Studies Example Multicolor cell imaging-based studies Live-cell imaging-based studies Neuron imaging-based studies C. elegans imaging-based studies
5 Image based Studies Example Multicolor cell imaging-based studies Live-cell imaging-based studies Neuron imaging-based studies C. elegans imaging-based studies
6 Multicolor cell imaging-based studies Multiple fluorescent markers Feature extraction Drosophila cell Softwares : CellProfiler, Fiji, Icy, GcellIQ, PhenoRipper
7 Multicolor cell imaging-based studies
8 Multicolor cell imaging-based studies Multiple fluorescent markers Feature extraction Drosophila cell Softwares : CellProfiler, Fiji, Icy, GcellIQ, PhenoRipper
9 Image based Studies Example Multicolor cell imaging-based studies Live-cell imaging-based studies Neuron imaging-based studies C. elegans imaging-based studies
10 Live-cell imaging-based studies Progression, proliferation, migration of cell Dynamic behaviors of cells Live Hela cell images Softwares : CellProfiler, Fiji, BioimageXD, Icy, CellCognition, DCellIQ, TLM-Tracker
11 Live-cell imaging-based studies
12 Live-cell imaging-based studies Progression, proliferation, migration of cell Dynamic behaviors of cells Live Hela cell images Softwares : CellProfiler, Fiji, BioimageXD, Icy, CellCognition, DCellIQ, TLM-Tracker
13 Image based Studies Example Multicolor cell imaging-based studies Live-cell imaging-based studies Neuron imaging-based studies C. elegans imaging-based studies
14 Neuron imaging-based studies To study brain functions and disorders Use super-resolution microscope Softwares : NeurphologyJ, NeuronJ, NeuriteTracer, NeuriteIQ, NeuronMetrics, NeuronStudio, Vaa3D
15 Neuron imaging-based studies
16 Neuron imaging-based studies To study brain functions and disorders Use super-resolution microscope Softwares : NeurphologyJ, NeuronJ, NeuriteTracer, NeuriteIQ, NeuronMetrics, NeuronStudio, Vaa3D
17 Neuron imaging-based studies
18 Image based Studies Example Multicolor cell imaging-based studies Live-cell imaging-based studies Neuron imaging-based studies C. elegans imaging-based studies
19 Caenorhabditis elegans imaging-based studies Common animal model for drug and target discovery Consists of only hundred of cells Embryonic development
20 Caenorhabditis elegans imaging-based studies Source : Wikipedia
21 Caenorhabditis elegans imaging-based studies Common animal model for drug and target discovery Consists of only hundred of cells Embryonic development
22 Bioimage informatics
23 Bioimage informatics
24 Object Detection Detect the locations of individual objects Facilitate the segmentation by giving the position and initial boundary information Two types of object detection : 1. Blob structure detection 2. Tube structure detection
25 Blob Structure Detection Nuclei detection Distance transformation Seeded watershed Intensity information Gradient vector
26 Blob Structure Detection
27 Blob Structure Detection Nuclei detection Distance transformation Seeded watershed Intensity information Gradient vector
28 Tube Structure Detection Intensity remains constant Centerline detection Edge detectors Machine-learning
29 Tube Structure Detection
30 Tube Structure Detection Intensity remains constant Centerline detection Edge detectors Machine-learning
31 Tube Structure Detection
32 Bioimage informatics
33 Object Segmentation Delineate boundaries of objects Threshold segmentation Fuzzy-C-Means method Watershed algorithm Active contour model Level set representation Voronoi segmentation Graph cut method Softwares : CellProfiler, Fiji, Ilastik, SLIC
34 Object Segmentation
35 Object Segmentation Delineate boundaries of objects Threshold segmentation Fuzzy-C-Means method Watershed algorithm Active contour model Level set representation Voronoi segmentation Graph cut method Softwares : CellProfiler, Fiji, Ilastik, SLIC
36 Object Segmentation Figure taken from
37 Object Segmentation
38 Object Segmentation Delineate boundaries of objects Threshold segmentation Fuzzy-C-Means method Watershed algorithm Active contour model Level set representation Voronoi segmentation Graph cut method Softwares : CellProfiler, Fiji, Ilastik, SLIC
39 Bioimage Informatics
40 Object Tracking Study dynamic behaviors Three approaches : 1. Model evolution based 2. Spatial-temporal volume segmentation based 3. Segmentation based
41 Object Tracking
42 Object Tracking Study dynamic behaviors Three approaches : 1. Model evolution based 2. Spatial-temporal volume segmentation based 3. Segmentation based
43 Object Tracking
44 Object Tracking Study dynamic behaviors Three approaches : 1. Model evolution based 2. Spatial-temporal volume segmentation based 3. Segmentation based
45 Object Tracking
46 Model Evolution Based Cell / nuclei are detected first Boundary comes next Contour model Different objects get different colors
47 Spatial-Temporal Volume Segmentation Based 2D image sequences as 3D Level set segmentation approaches
48 Segmentation Based First detected and then segmented Tracking is dependent of segmentation and detection Association Filters may be used
49 Bioimage Informatics
50 Image Visualization Fiji, Icy, BioimageXD are for higher dimensional data NeuronStudio for neuron image analysis Farsight and vaa3d for microscopy images For customize tools, Visualization Toolkit helps.
51 Numerical Features Quantitative measuring Four quantitative features 1. Wavelet feature 2. Geometry feature 3. Zernike feature 4. Haralick texture feature
52 Numerical Features Wavelet feature : characterize the images in both scale and frequency domain. Geometry feature : describe the shape and texture features. Zernike feature : projection and the use of Zernike moment. Haralick texture feature : use grey-level matrices
53 Phenotype Identification Cell cycle phase identification User defined phenotype, identification and classification
54 Cell Cycle Phase Identification Automated cell cycle phase identification is needed to calculate the dwelling time of individual cells in each phase. SVM, K-nearest neighbors, Bayesian classifiers Can be done during segmentation and tracking.
55 Cell Cycle Phase Identification
56 Cell Cycle Phase Identification Automated cell cycle phase identification is needed to calculate the dwelling time of individual cells in each phase. SVM, K-nearest neighbors, Bayesian classifiers Can be done during segmentation and tracking.
57 User Defined Phenotype, Identification and Classification Exhibit novel phenotype and unpredicted behaviors. Gaussian Mixture Model with statistics Clustering analysis Classifiers
58 User Defined Phenotype, Identification and Classification
59 User Defined Phenotype, Identification and Classification Exhibit novel phenotype and unpredicted behaviors. Gaussian Mixture Model with statistics Clustering analysis Classifiers
60 Multidimensional Profiling Analysis Clustering analysis SVM-based multivariate profiling analysis Factor-based multidimensional profiling analysis Subpopulation-based heterogeneity profiling analysis
61 Multidimensional Profiling Analysis Clustering analysis SVM-based multivariate profiling analysis Factor-based multidimensional profiling analysis Subpopulation-based heterogeneity profiling analysis
62 Clustering Analysis Experimental perturbations Softwares : Cluster 3.0, Java TreeView
63 Multidimensional Profiling Analysis Clustering analysis SVM-based multivariate profiling analysis Factor-based multidimensional profiling analysis Subpopulation-based heterogeneity profiling analysis
64 SVM-basheed Multivariate Profiling Analysis Wells with treated cells compared to wells with untreated cells. The differences are indicated by the outputs of SVM One is the accuracy and another is the normal vector of the hyperplane.
65 SVM-basheed Multivariate Profiling Analysis
66 Multidimensional Profiling Analysis Clustering analysis SVM-based multivariate profiling analysis Factor-based multidimensional profiling analysis Subpopulation-based heterogeneity profiling analysis
67 Factor-based Multidimensional Profiling Analysis Correlation of the features within the group and between the groups. Redundancy can be removed by factor analysis. Six factors representing nuclei size, DNA replication, chromosome condensation, nuclei morphology etc. by factor analysis.
68 Multidimensional Profiling Analysis Clustering analysis SVM-based multivariate profiling analysis Factor-based multidimensional profiling analysis Subpopulation-based heterogeneity profiling analysis
69 Subpopulation-based Heterogeneity Profiling Analysis Heterogeneous behavior within a cell population. GMM model to divide into subpopulation.
70 Exercises!!!
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