Ranking candidate genes from Ranking candidate genes from - - PowerPoint PPT Presentation

Ranking candidate genes from Ranking candidate genes from perturbation experiments

Niko Beerenwinkel

Gene ranking

• Goal: Identify (or prioritize) genes that affect readout, i.e.,

i l d i bi l i l f i t t are involved in biological process of interest

• Issues
• Noise (readout, siRNA specificity)
• Design (siRNA library, replicates, validation screens)
• Limited resources
• Procedure
• Normalization: quantiles z-score error models

Normalization: quantiles, z score, error models

• Rank by normalized readout or p-value

2

RSA: Redundant siRNA activity analysis

• Rank all siRNAs (wells) by readout
• Assign p value to each gene based on the rank distribution of all
• Assign p-value to each gene based on the rank distribution of all

siRNAs targeting it (hypergeometric model)

3

König et al, 2007

Comparing gene rankings

• Intersection metric
• Spearman’s footrule
• 4

Stable variables

• Let Λ be the set of all (reasonable) cut-offs for a given

ranking (i e λ ∈ Λ is a regularization parameter) ranking (i.e., λ ∈ Λ is a regularization parameter).

• The set of selected genes

i f ti f th l I

ˆ Sλ = ˆ Sλ(I)

is a function of the samples I.

• For a given threshold π, the set of stable variables is

ˆ Sstable =

½

k : max

λ∈Λ P

³

k ∈ ˆ Sλ´ ≥ π

¾

• P can be estimated by sub- or re-sampling

½

λ∈Λ

³ ´ ¾

• P can be estimated by sub- or re-sampling.

5

Stability selection (Meinshausen & Bühlmann, 2010)

• Under certain assumptions, the expectation of the number

f f l l l t d i bl V i b d d b

• f falsely selected variables V is bounded by

( ) 1 q2

Λ

E(V ) ≤ 1 2π − 1 qΛ p

where p is the total number of genes, and

h

| ˆλ( )|

i

the expected number of stable genes

qΛ = E

h

| ∪λ∈Λ Sλ(I)|

i

the expected number of stable genes.

• In practice we can set π and Λ to control false positives
• In practice, we can set π and Λ to control false positives.

6

Data sets

• Hardt lab

S l ll i h ll

• Salmonella screen in human cells
• 19,000 genes
• Read-out: infection rate

Read out: infection rate

• ~4 different siRNAs per gene, no replicates
• Merdes lab (Saj et al., Dev Cell, 2010)
• Notch screen in Drosophila
• 12,000 genes
• Read-out: Notch activity
• 4 replicates
• 4 replicates
• Secondary and in vivo validation screens

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Salmonella screen: ranking

• Quantile normalization
• Rankings (Kendall’s tau distance):

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Salmonella screen: stability

Λ Λ Λ Λ

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Notch screen: raw data

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Notch screen: quantile normalization

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Notch screen: normalization

• Raw data
• Quantiles

cor Quantiles rrelation

• Z-scores

12

Notch screen: ranking

• Quantile-normalized
• Ranking distance (Kendall’s tau)

13

Notch screen: reproducibility

• Leave-one-out:

R ki b d Ranking based on three replicates validated with fourth validated with fourth replicate C t ff 300 f

• Cut-off 300 for

validation

14

Notch screen: average leave-one-out ROC curves for different normalizations different normalizations

15

Notch screen: ROC analysis of validation screen

• Secondary screen of 900 genes
• Focus on down regulation

All 12 000 T 254 All 12,000 genes Top 254 genes

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Notch screen: stability, in vivo validation

• Median ranking of top 2000 genes

Λ

Median 20

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Conclusions

• Both quantile and z-score normalzation improve correlation

d d ibilit and reproducibility.

• Selecting stable genes complements selection of high-

i scoring genes.

• Stable sets quantify reproducibility of being among top k in

ranking

• Upper bound on expected number of false positives
• RSA produced fairly unstable sets

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Acknowledgements

• Computational Biology Group, www.cbg.ethz.ch

J li Si b

• Juliane Siebourg
• Edgar Delgado-Eckert

C ll b

• Collaborators
• Gunter Merdes (D-BSSE, ETH Zurich)
• Wolf-Dietrich Hardt (D-BIOL, ETH Zurich)
• InfectX consortium
• Funding
• InfectX, SystemsX.ch

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