Inference of Signaling Networks Using Quantitative Morphological Signatures:

Download Presentation

Inference of Signaling Networks Using Quantitative Morphological Signatures:

Loading in 2 Seconds...

- 89 Views
- Uploaded on
- Presentation posted in: General

Inference of Signaling Networks Using Quantitative Morphological Signatures:

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Inference of Signaling Networks Using Quantitative Morphological Signatures:

Parallel Computing Framework

Oaz Nir18.337JMay 13, 2008

Challenges in Describing Signaling Networks

1Connectivity

2 Flow of Signaling Information

3 Subcellular Distribution of Local Networks

Cell Morphology = Signaling State

1

“stress fibers”

2

3

GTP

4

Rho

Rho Activation

5

h

Active Rho Morphological Signature = F1, F2, F3,…Fn

7

6

8

9

normal

perimeter

“ruffles/lammelipodia”

“spiky”

GTP

GTP

Rac

Cdc42

Cdc42 Activation

Rac Activation

d

Active Rac Morphological Signature = F1, F2, F3,…Fn

Active Cdc42 Morphological Signature = F1, F2, F3,…Fn

Transmembrane Receptors

Polarity Complexes

GEF

GAP

GAP

GEF

GEF

GAP

GAP

GEF

G

GEF

GPCRs/G-proteins

GAP

AdhesionStructures

GAP

G

GEF

GEF

GAP

G

GAP

GEF

GEF

GAP

G

GAP

GEF

GEF

GAP

GAP

GEF

GEF

GAP

GAP

GEF

GEF

GAP

Actin regulators

Lipid Regulators

GAP

GEF

GEF

GAP

GAP

GEF

GEF

GAP

GEF

GEF

GAP

MT regulators

Actin/MT coordinators

Understanding how Signaling Networks that Regulate Morphology are Organized and How Information Flows through these Networks

RhoGTPases

Acquiring Morphological Signatures from Complex Images

3. CellSegmenter

2. Image Acquisition (GFP)

features

cell “x1”condition “a1”

Normalized feature values

cell “x2” condition “a1”

…

N Treatment Conditions (TCs)

1. Cell Culturing+ GFP+/- dsRNA+/- Gene overexpression

GFP

Cell Segment

Ruffle Area

Edge

Process Area

Drainage Area

Half Mass fr. Centroid

Half Mass fr. Boundary

Gaussian Fit

Low Smooth/Best Ellipse Fit

High Smooth/Best Ellipse Fit

x=0.358y=0.357=-0.248

DAPIGFPF-Actin

All features

B

A

Feature n

Feature y

Feature x

Reduce Dimensionality

B

A

B

Feature a

A

Classifier (test)

Feature b

Feature c

Raw Morphological Data and Data Reduction

145 phenotypic features

Using Feature Graphs to Model Single-cell Distributions

i

j

dsRNA x

3i,3j,-4k

Feature values: i,j,k

-3i,-3j,-4k

i

j

dsRNA y

k

4i,4j,-4k

Feature values: i,j,k

2i,2j,-2k

- Create structures that allow inference of signaling pathways
- Utilize single-cell data
- Linear correlations are fast and easy to compute
- Graphs on the same vertex set are comparable by various algorithms from graph theory

Draw a vertex for each feature (neural network classifier)

For each dsRNA treatment, defined a graph as follows:

Draw an edge if the correlation between corresponding features among single cell data exceeds a threshold

C

A

B

Inference Based on Feature Graphs

This is the unknown signaling network we will infer. For this slide, assume we know the signaling network ahead of time

Intuition for Inference Based on Feature Graphs

Question: What is the relationship between feature graphs of genes in a signaling pathway?

[F1, F2]

RNAi Gene C

Expect feature graph to have a relatively small number of edges

Feature graph is approximately the intersection of the feature graphs for RNAi A and RNAi B

RNAi Gene A

[F1, F2, F4]

[F1, F2, F3]

Expect feature graph to have relatively large number of edges

RNAi Gene B

Expect feature graph to have a relatively large number of edges

Focus on Details of Feature Graph Construction

F=150 features per cell

(future data sets will be larger)

For each FG, need to compute linear correlation for C=50 data points for all F*(F-1)/2=150*149/2 pairs of features.

Since there are N = 250 TCs, there are a total of N*F*(F-1)/2 linear correlations to compute.

N =250 Treatment Conditions (TCs)

Draw a vertex for each feature (neural network classifier)

Drosophila Data Set

For each dsRNAtreatment (TC), defined a graph as follows:

C = 50 cells per TC

Draw an edge if the correlation between corresponding features among single cell data exceeds a threshold

Focus on Details of Feature Graph Construction

F=150 features per cell

For each FG, need to compute linear correlation for C=50 data points for all F*(F-1)/2=150*149/2 pairs of features.

How to compute all pairwise correlations efficiently?

matmul of FxC and CxF

Computation is dominated by matmul of FxC and CxF

N =250 Treatment Conditions (TCs)

Drosophila Data Set

matmul of Fx1 and 1xF

C = 50 cells per TC

**Matlab built-in “corr” does not work with ppeval

Parallelize in Dimension of TCs

Speed-up?

Parallel

Serial

What if the TCs Have Different Numbers of Cells (C)?

What if the TCs Have Different Numbers of Cells (C)?

Serial

Parallel

Summary and Conclusions

- Feature graph construction depends on computation of numerous linear correlations
- Parallelization was implemented
- But speed-ups were not realized (why not?)
- In fact, slower because of time required to move data to/from the server
- Speed-ups are realized for *very* large data sets because the server can handle larger data more smoothly than a typical PC. But this is not due to parallelization, rather due to hard drive usage.

- Why didn’t parallelization result in gains in speed?
- Interactive Supercomputing doesn’t preallocate matrices in Matlab
- Structure of problem?
- Coding?

Acknowledgments:

Chris Bakal

Bonnie Berger

John Aach

Norbert Perrimon

George Church