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Copy of The Milgram Experiment

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KSENIA ANUFRIEVA

on 23 August 2015

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Transcript of Copy of The Milgram Experiment

How to make an ideal experimental design
Project:
Experimental design


Normalization:

Why?

To correct for systematic differences between
samples on the same slide, or between slides, which do not represent true biological variation between samples







1) No consensus on best normalization method

2) Replication and standards are essential for identifying and reducing the effect of variability in any experimental assay




The most common normalization approaches:

tvn (Total Volume Normalization)
median normalization
cyclic loess
vsn (Variance Stabilizing Normalization)
quantile normalization

TVN
VSN
Protein carbonylation
as marker of diseases
2D -gel: why is it important to normalize data?
Comparing replicates for IS
Cell death-Tissue Injury

aging and diseases
before
Goals of experimental design:

1) to arrive at a clear
answer to the question of interest (expending a minimum amount of resources)

2) to attain maximum information

3) to utilizeуefficiently existing information
before experiment:
blocking, randomization, design structure

after experiment:
normalization, batch correction, error estimation

blocking
randomization
Comparing results (IS)
How to deal with repeated measurements?

How can we explore the different sources of variation?
linear regression:
“fixed” effects
indivual “random” effects

"fixed effects"
"random effects"
"fixed" effects
+
"random" effects


Mixed Models
Why do we use Mixed Models?
Is this just
biological
variation???
TVN normalized data
Mixed Models - R package lme4
GParea = GPareaOfPopulation
+ (1|Sample)
+ (1|labPerson/labPeriod)
+ (1|labPeriod)
+ measurementError
Our model:
Mixed Models - Unnormalized Data
Mixed Models - TVN Normalized Data
Mixed Models - Quantile Normalized Data
Mixed Models - Median Normalization
Mixed Models - Batch Effect (TVN Normalized Data)
A little travel through our Data...
Biological variance between age and sex
Conclusions
With the accurate experimental design, a cautious normalization, batch effect correction and error estimation, we are ready to start analyzing our data.
Mixed Models - Batch Correction (TVN Normalized Data)
Our group:
Thank you all
GParea = GPareaOfPopulation
+ Age
+ Sex
+ (1|SampleSpecific)
+ (1|labPerson/labPeriod)
+ (1|labPeriod )
+ measurementError

before "normal"ization after "normal"ization
Our group:
batch correction
Full transcript