statistical analysis with msstats

Meena ChoiGroup of Prof. Olga Vitek

Department of Statistics, Purdue University

Statistical analysis with MSstats

Skyline User Meeting 2014 Baltimore

2

Outline

1. MSstats : statistical tool for quantitative MS proteomics

1. Workflow of MSstats

2. MSstats as an external tool

2. Integration of Skyline improves analysis workflow

1. User interface

2. Checking quality of features

3. Different workflows require different

1. statistical models

2. normalization

4. How to access MSstats

3

MSstats : statistical tool for quantitative MS

proteomics

MSstats 2.0

Open-source R-based package for statistical relative quantification of

peptides and proteins in mass spectrometry-based proteomic experiments.

4

MSstats workflow : Experimental design

QC andNormalization

Statisticalmodeling

Experimental design

Model-basedconclusion

Design offollow-up

study

Type of experimental design

- Label-free workflows or workflows that use stable isotope labeled reference

proteins and peptides

- SRM, DDA or shotgun, DIA or SWATH

- Comparisons of experimental conditions or times, or paired design

Input format

RunConditionProtein PeptidePrecursor

charge FragmentProductcharge Label Subject Intensity

5

MSstats workflow : QC and normalization

Data preparation

- Formatting

- Visualization : 2 plots

- Normalization : equalize medians, quantile, with standard protein

Reference Endogenous

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3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30

MS runs

Lo

g2−

inte

nsitie

s

IDHCReference Endogenous

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

0

10

20

30

3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30

MS runs

Lo

g2−

inte

nsitie

s

IDHC

QC andNormalization

Statisticalmodeling

Experimental design


Design offollow-up

study

MS runs

Log2

-In

ten

siti

es

MS runs

Log2

-In

ten

siti

es

Reference Endogenous Reference EndogenousQC plot : All proteins Profile plot : One protein

6

MSstats workflow : Statistical modeling

- Account for

- different design of

experiment

- technical replicates

- pattern of missing

values

- any special aspect

- 55 unique linear mixed

models are used

- 16 fixed effects models

- 16 fixed effects models

for unequal variance

- 23 random effects

models

QC andNormalization

Statisticalmodeling

Experimental design


Design offollow-up

study

7

MSstats workflow : Statistical modeling

Verify the assumptionsDeviations from independence or from constant variance are often mistaken for deviations

from Normality


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3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30

MS runs

Lo

g2−

inte

nsitie

s

PMG2 Per features

Normal quantiles

Qu

anti

les

of

resi

du

als

QC andNormalization

Statisticalmodeling

Experimental design


Design offollow-up

study

MS runs

Log2

-In

ten

siti

es


8

MSstats workflow : Model-based conclusion

Model-based group comparison

- Quantify the uncertainty

- Adjust p-values to control FDR

Relative protein quantification

- by sample

- by condition

T3−

T1

T5−

T1

T7−

T1

ACONACH1CISY1DHSAFUMHHXKAIDH1IDH2MDHCMDHMODO1ODO2SDH3SUCAODPBENO1DHSBHXKGSUCBCISY2ACEAF16PIDHCMASYADH2PDC5HXKBK6PF2ADH1ENO2ALFALDH6DHSDG6PIK6PF1KPYK1KPYK2ODPAPDC1PDC6PGKPMG1PMG2PMG3ADH4

QC andNormalization

Statisticalmodeling

Experimental design


Design offollow-up

study

Log2 Fold Change

-Lo

g2 (

adju

ste

d p

-val

ue)

9

MSstats workflow : Design of follow-up

studies

Use the dataset to improve :

- subject selection : matching

- resource allocation : blocking

- calculation of sample size

- use information from current study

51

015

20

25

1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5

0.001 0.002 0.005 0.006 0.012

Coefficient of variation, CV

Desired fold change

Min

ima

l num

be

r o

f b

iolo

gic

al re

plic

ate

s Number of peptides is 3

Number of transitions is 5

FDR is 0.01

Statistical power is 0.9

0.0

0.2

0.4

0.6

0.8

1.0

1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5

Desired fold change

Pow

er

Number of replicates is 3

Number of peptides is 3

Number of transitions is 5

FDR is 0.01Stat

isti

cal p

ow

er

=P

rob

abili

ty(d

ete

ctin

g tr

ue

FC

)QC and

NormalizationStatisticalmodeling

Experimental design


Design offollow-up

study

Desired fold change

Min

imal

# o

f b

iolo

gica

l re

plic

ate

s

Desired fold change

10

Outline





1. User interface




2. normalization


11

Motivation : many users are not familiar with

R

M.M. Matzke et al. Proteomics 2013

• Many requests from users

• Improve usability issue

– User interface from

installation to analysis

– User help

12

MSstats as an external tool

1. Automatic installation in skyline 2. New option ‘MSstats’ under Tools

3. MSstats functions - GUI- Output and results will be saved automatically

13

Skyline and MSstats provide complementary

QCVisualization in Skyline


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1 2 3 4 5 1 2 3 4 5

MS runs

Log2−

inte

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s

# peptide: 3 ● ● ●GLAAGLDPNTATAGELAR_2 HLPEHAIVQFVK_3 LVGIVTEADIAR_2

Visualization in MSstats

• Example dataset in ‘Advanced peak picking models’ Skyline tutorial.

• SRM with stable isotope labeled reference peptides

MS runs

Log2

-In

ten

siti

es


Re

ten

tio

n T

ime

Pe

ak A

rea

%Replicate

Replicate

Retention time

Inte

nsi

ty


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2 5 2 5

MS runs

Log2−

inte

nsitie

sBetween run variation points to poor quality

peaks

Profile plot from MSstats

Chromatography from Skyline

MS runs

Log2

-In

ten

siti

es


Ret

en

tio

n T

ime

Pe

ak A

rea

%

Replicate Replicate 14

Retention time

Inte

nsi

ty


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5

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2 5 2 5

MS runs

Log2−

inte

nsitie

sRefinement for picking peaks improves the

quality

Profile plot from MSstatsChromatography from Skyline

MS runs

Log2

-In

ten

siti

es


Ret

en

tio

n T

ime

Pe

ak A

rea

%

Replicate Replicate15

Retention timeIn

ten

sity


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0

10

20

30

77 199 215 77 199 215

MS runs

Lo

g2−

inte

nsitie

s

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●

DIAPTLTLYVGK_2_y8_1

DIAPTLTLYVGK_2_y9_1

DIAPTLTLYVGK_2_y9_2

VTSIQDWVQK_2_y4_1

VTSIQDWVQK_2_y5_1

VTSIQDWVQK_2_y6_1

HPT


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0

10

20

30

77 199 215 77 199 215

MS runs

Lo

g2−

inte

nsitie

s

●

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●

●

●

●

DIAPTLTLYVGK_2_y8_1

DIAPTLTLYVGK_2_y9_1

DIAPTLTLYVGK_2_y9_2

VTSIQDWVQK_2_y4_1

VTSIQDWVQK_2_y5_1

VTSIQDWVQK_2_y6_1

HPT

16

Truncated peaks also introduce between run

variation

with truncated peaks :log2FC=-0.342, adjust p-value=0.0277

without truncated peaks :log2FC=-0.155, adjust p-value=0.4999

MS runs

Log2

-In

ten

siti

es

MS runs

Log2

-In

ten

siti

es



Retention time

Inte

nsi

ty

Retention time

Inte

nsi

ty

17

Feature selection is available in MSstats

Oral session : MOG 3:50pm, Statistical Elimination of Spectral Features with Large Between-Run Variation Enhances Quantitative Protein-Level Conclusions in Experiments with Data-Independent Spectral Acquisition (Lin-Yang Cheng)

Endogenous

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1 2 3 4 5 6 7

0

5

10

15

20

25

3 6 9 12 15 18 21

MS runs

Lo

g2−

inte

nsitie

s

Endogenous

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1 2 3 4 5 6 7

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MS runs

Lo

g2−

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Original spike-in DIA dataAfter feature selection, informative features are selected.

MS runs

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MS runs

Endogenous Endogenous


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New in MSstats (beta) : automated feature

selection


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All features Refinement from Skyline

After automated feature selection

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Reference Endogenous Reference Endogenous

Reference Endogenous Reference Endogenous

19

Outline





1. User interface




2. normalization


20

Different workflows require different statistical models

DDA SRM DIA

DDA SRM DIA

Model options Interference=FALSEequalFeatureVar=FALSE

Interference=TRUEequalFeatureVar=TRUE

Interference=FALSEequalFeatureVar=FALSEFeatureSelection=TRUE

Normalization Normalization=globalStandard

Normalization=equalMediansor =globalStandard

Normalization=Quantileor =globalStandard

Internal standards are always good for quality control

Endogenous

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0

5

10

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25

3 6 9 12 15 18 21

MS runs

Lo

g2−

inte

nsitie

s

MS runs

Log2

-In

ten

siti

es

MS runs

Log2

-In

ten

siti

es

MS runs

Log2

-In

ten

siti

es

Reference EndogenousEndogenous Endogenous

21

Outline





1. User interface




2. normalization


22

External tool in Skyline

- Downloads from Feb 2014 : 1543

- From MSstats external tool webpage or ‘Tool store’

- Automatic installations for all related software and packages

- One-click analysis

23

Bioconductor

- MSstats v2.3.0 under BioC 3.0

- Unique downloads from Oct 2013 : 1073

Take advantage of options and modify the data easily.

- New functionality : Quantification for sample, feature selection

- Detailed options for all plots

- More options for design sample size function

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msstats.org and MSstats google group

- News about Msstats

- MSstats.daily is available : development version available

- Tutorials for different workflows (under ‘WORKFLOWS’)

- Example datasets with R-scripts

- Related publications

- Announce new release

or news in the mailing

list

- Question and answer

- Discussion and

suggestion

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Biomedical investigations using MSstats

• Ruth Hüttenhain et al. "Reproducible Quantification of Cancer-Associated Proteins in Body Fluids Using Targeted Proteomics". Sci Transl Med 4, 142ra94 (2012);

• Ruth Hüttenhain et al. "N-Glycoprotein SRMAtlas: A resource of mass-spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications". MCP

• Surinova et al. "Automated SRM data analysis workflow for large scale proteomic studies." Nature Methods

• Sabido et al., "Targeted proteomics of the eicosanoid biosynthetic pathway completes an integrated genomics-proteomics-metabolomics picture of cellular metabolism." MCP

• Sabido et al., "Targeted proteomics analysis of the insulin-pathway and central metabolism reveals substantial, strain-specific proteome changes after sustained high-fat diet in C57B6/J and 129Sv mice." Molecular Systems Biology

• SRM course, Zurich (July 2013, February 2014)

• Targeted Quantitative Proteomics Course, Seattle (April 2014)

• US HUPO (Baltimore 2013, Seattle 2014)

• 8th European Summer School in Proteomics, Brixen, Italy (August

2014)

• EMBO practice course on targeted proteomics, Barcelona, Spain

(September 2014)

• Workshop on Targeted Proteomics, Korea (October 2014)

• Workshop on Targeted Proteomics, India (December 2014)

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Short courses to learn more about MSstats

Acknowledgements

Purdue University

o Prof. Olga Vitek

o Mike Cheng

o Veavi Chang

o Tim Clough

o Danni Yu

o Kyle Bemis

o April Harry

o Robert Ness

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University of Washington

o Prof. Mike MacCoss and Lab

o Brendan MacLean

o Yuval Boss

ETH Züricho Prof. Ruedi Aebersold

o Olga Schubert

o Hannes Röst

o Ruth Hüttenhain

o Silvia Surinova

o Eduard Sabido

o Matondo Mariette

University of Zürich

o Meliana Riwanto

statistical analysis with msstats

Documents