Half-Time Seminar 2017
Daniel Morgan
"Inference" -> variable selection
GeneSPIDER
tune, run, eval inference algs:
Regularization helps fit linear model:
Glmnet/LASSO/ElasticNet,
T/LSCO
RNI, ARACNe (mutual info)
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bootstrap -> resampling method estimating variance from approx. distr.
--> build confidence/support/reliability btwn runs
SNR, K, IAA
MCC,AUROC -> performance measure based on various ratio of TP, TN, FP, FN
Terms:
Generation and Simulation Package for Informative Data ExploRation
via some 200 networks & 600 expression sets
consisting of 4 different topologies
with varied SNR,
IAA degrees,
and sizes
Robust Network Inference  decouples the model selection problem from parameter estimation; is very harsh but among the best methods when noise is low
focuses on mutual information between links in a link by link fashion rather than upon entire system as a whole. Also disregards self-regulating elements
Least Absolute Shrinkage & Selection Operator: minimizes RSS by penalizing |coefficient| rather than their square, thus harshest (zeros possible)
Fit cases to regression line minimizing difference on X and XandY axis
with self loops ∴ null ARACNe
Nested Bootstrapping
for reliable GRN Inference
???
Nested Bootstrapping
for reliable GRN Inference
Threshold
(T)LSCO
Glmnet - LASSO
ARACNe
???
Nested Bootstrapping
for reliable GRN Inference
middle density
biological time series dataset
collapse from 16k genes to 28
Bolasso
Bolsco
size: 28 links: 89 density: 0.1135204
Inferring interactions of 40 genes gravitating around MYC oncogene
1. qRTPCR of40 genes, singly & doubly knocked down via siRNA
​--> gene fold change & variance of expression measures
2. Linear Model
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Y: expression data
A: network
P: perturbation matrix
E: input noise estimate
F: output noise estimate
Inferring interactions of 40 genes gravitating around MYC oncogene
1. qRTPCR of40 genes, singly & doubly knocked down via siRNA
​--> gene fold change & variance of expression measures
Inferring interactions of 40 genes gravitating around MYC oncogene
Inferring Link & Sign
consensus of high confidence based on nested boostrap
Inferring interactions of 40 genes gravitating around MYC oncogene
Inferring interactions of 40 genes gravitating around MYC oncogene
Bolasso
Bolsco
Different methods return networks of similar size based on 5% FDR cutoff
MYC, B.Sub, synthetic data
MYC and B.Sub data
add wrappers to GS