stascalanalysiswith msstats%choi67/ushupo2016_short... · 2016. 3. 13. · iii. volcano)plot iv....
TRANSCRIPT
Meena Choi Department of Sta-s-cs, Purdue University Group of Prof. Olga Vitek
Sta,s,cal analysis with MSstats
Design and analysis of quan0ta0ve proteomic experiment US HUPO short course
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MSstats : statistical tool for quantitative MS proteomics
Open-source R-based package for statistical relative quantification of peptides and proteins in mass spectrometry-based proteomic experiments.
* Test proteins for differen-al abundance
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
I. Sta-s-cal model II. Results table III. Volcano plot IV. Heatmap V. Comparison plot
I. Power analysis II. Sta-s-cal power III. Sample size
I. Data transforma-on II. Intensity normaliza-on III. QC Plot IV. Feature selec-on V. Missing values imputa-on VI. Run-‐level summariza-on VII. Profile Plot VIII. Condi-on Plot
I. MSstats compa-ble experiments
II. MSstats input
MSstats
MS acquisi,on • DDA or shotgun • SRM • DIA or SWATH Workflows • label-‐free • label-‐based
• With isotope-‐labeled reference pep-des
Experimental designs • Case-‐control • Time course • Paired analysis • More than 2 condi-ons • Linear combina-on of condi-ons
Not supported • mul--‐labelling • iTRAQ
MSstats compatible experiments
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MSstats input : 10 columns are required
Run Condi-on Protein Pep-de Precursor charge Fragment
Product charge Label Subject Intensity
For DDA, ‘Fragment’, ‘ProductCharge’ can be any one value, such as NA
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
• Results from any spectral processing tool: e.g. Skyline, MaxQuant, Progenesis, OpenSWATH, Spectronaut
• Tabular .csv format
II. Intensity normaliza,on – None : no normaliza-on is performed. – equalizeMedians : make the same median of
• reference intensi-es across runs for label-‐based experiments • Endogenous intensi-es across runs for label free experiments
– Quan,le : equalize the distribu-on of • reference intensi-es across runs for label-‐based experiments • Endogenous intensi-es across runs for label free experiments
– Global Standards : applied to endogenous intensi-es. Equalize endogenous intensi-es of global standard protein across runs. Then apply the same between-‐run shi\s to the remaining endogenous proteins.
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I. Data transforma,on – Import the data for analysis – Log 2 or 10 transforma-on of intensi-es
1. Data processing and quality control (QC)
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
Quality control plots
Reference Endogenous
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MS runs
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No normaliza,on Equalize medians normaliza,on Quan,le normaliza,on
MS runs MS runs
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tensi,es
Log2-‐In
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Reference Endogenous Reference Endogenous
ü Distribu-on of intensi-es per run ü Show poten-al systema-c biases between mass spectrometry runs ü Show how the normaliza-on works for all the proteins combined
Normalization method should be changed based on your design of experiment
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Assume label-‐free SRM : -‐ most features are differently abundant
!! equalizeMedians and Quan,le normaliza-on assume constant protein abundance across all samples
Only true if you a) have a labeled reference or protein standards b) expect the majority of your proteins to be constant
Reference Endogenous
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Equalize medians normaliza,on
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All proteins
All proteins
Equalize medians normaliza,on No normaliza,on
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Equalize medians normaliza,on
MS runs
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All proteins
All proteins
!! equalizeMedians and Quan,le normaliza-on assume constant protein abundance across all samples
Only true if you a) have a labeled reference or protein standards b) expect the majority of your proteins to be constant
Equalize medians normaliza,on No normaliza,on
Assume label-‐free SRM : -‐ Need to concern normaliza,on method in design stage
How to detect features with interference? Quan-fy how parallel features
Which features are used for rela-ve quan-fica-on and differen-al expression analysis? • all features • Top3 features : highest average of log2(intensity) across runs • Features without interference
Feature selection
MS runs
Log2 -‐ intensi-es
MS runs
Log2 -‐ intensi-es
• Clear peaks have almost parallel profiles
• Features with interference has unparalleled profiles
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Log2 -‐ intensi-es
How to detect interference
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Step 1: Summarize abundance of each pep-de
Step 2: Es-mate the degree of interference = variance of residuals
Step 3: Remove features with highest interference un-l no further improvement
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Low degree of interference
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Step 1 Step 2 Step 3
Feature selection removes the features with interference
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Example DIA data
All features AOer feature selec,on
MS runs
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Log2 -‐ intensi-es
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Impute censored missing values
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Missing values • Random : technical variability • Censored : below limit of detec-on è Impute censored missing values by accelerated failure model
ü Only endogenous intensi-es ü Cut off is minimum value for each feature
• Es-mate rela-ve abundance for each protein per run • Subplot: only consider feature + run • Tukey’s median polish: Robust parameter es-ma-on method with median
across rows and columns
yijkl = µ+Runijk + Featurel + ϵijkl , where
medianijk(Runijk) = 0, medianl(Featurel) = 0, and medianijk(ϵijkl) = medianl(ϵijkl) = 0
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Run-level subplot summarization
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ACH1Reference Endogenous
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Log2−i
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ACH1
• Visualize individual observa-ons • Show the poten-al source of varia-on, such as Run, Transi-on, Condi-on • Check missing values
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Profile Plot
MS runs
Log2-‐In
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Good quality Profile plot. It shows the source of varia,on (Run, Condi,on, Transi,on)
Color : pep-de Linetype : transi-on Condi-on
Reference Endogenous
MS runs
Log2-‐In
tensi,es
Summarized intensi-es
Reference Endogenous
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0
10
20
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66 81 66 81MS runs
Log2−i
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# peptide: 3●●
●●
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FSPATHPSEGLEENYCR_3_b11_2
FSPATHPSEGLEENYCR_3_y3_1
FSPATHPSEGLEENYCR_3_y6_1
HSIFTPETNPR_2_y6_1
HSIFTPETNPR_2_y7_1
HSIFTPETNPR_2_y8_1
QLGAGSIEECAAK_2_y6_1
QLGAGSIEECAAK_2_y7_1
QLGAGSIEECAAK_2_y9_1
PLMN
Reference Endogenous
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0
10
20
30
66 81 66 81MS runs
Log2−i
nten
sitie
s
# peptide: 3●●
●●
●●
●●
●●
●●
●●
●●
●●
AGALNSNDAFVLK_2_y3_1
AGALNSNDAFVLK_2_y7_1
AGALNSNDAFVLK_2_y8_1
EVQGFESATFLGYFK_2_y4_1
EVQGFESATFLGYFK_2_y7_1
EVQGFESATFLGYFK_2_y8_1
HVVPNEVVVQR_3_b5_1
HVVPNEVVVQR_3_b6_1
HVVPNEVVVQR_3_y4_1
GELS
15
Profile Plot
Detect the problema,cal Run or Transi,on Show the missing values (Disconnec,on)
Reference Endogenous Reference Endogenous
• Hypothesis : Is there a difference in abundance between condi-on1 and condi-on2? H0 : log fold change = 0 vs. Ha : log fold change ≠ 0
• Comparisons between condi-ons are es-mated by linear mixed effect models
16
2. Group Comparison : Test for differential abundance
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
log(peak intensity)
expected reference abundance
condi-on biological replicate
random meas. error = + + +
Group comparison
yijk = µ+ Conditioni + Subject(Condition)j(i) + ψijk,where∑
i
Conditioni = 0, Subject(Condition)j(i)iid∼ N (0,σ2Subject), ψijk
iid∼ N (0,σ2ψ)
yijk = µ+ Conditioni + Subject(Condition)j(i) + ψijk,where∑
i
Conditioni = 0, Subject(Condition)j(i)iid∼ N (0,σ2Subject), ψijk
iid∼ N (0,σ2ψ)
• Log2 fold change: provides an es-mate for the difference between the two condi-ons
• Adjusted p-‐value: probability that your conclusion is false (adjusted = corrected for mul-ple tes-ng)
17
Results table for inference
●
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CISY2
DHSB
DHSD
ENO1HXKG
SUCB
0.0
2.5
5.0
7.5
10.0
−2 0 2Log2 fold change
−Log
10 (a
djus
ted
p−va
lue)
Fold change cutoff (1.5) Adj p−value cutoff (0.05)
● ● ●No regulation Down−regulated Up−regulated
T3−T1
18
More significant
Less significant
Prac-cal significance
Sta-s-cal significance
• Per comparison • All proteins • Adjusted p-‐value and log fold change
Volcano plot
19
• With all comparisons • All proteins • Adjusted p-‐value and cut-‐off log fold change
Heatmap
Proteins
T3-‐T1
T5-‐T1
T7-‐T1
Based on adjusted p-‐values, • nega-ve p-‐value : In cases of a nega-ve fold change below a specified cutoff
• posi-ve p-‐value : In case of posi-ve fold change above a a specified cutoff
(sign) Adjust p-‐value
1 0.5 -‐0.5
20
Comparison plot
Represents log fold changes for mul-ple comparisons per protein • With all comparisons • Per protein • Log fold change and CI
Log2-‐Fold Ch
ange
Comparison
T3-‐T1 T7-‐T1 T5-‐T1
21
3. Design sample size : Design of future experiment
Use informa-on (variance es-ma-on) from current study to a) es-mate the power of your analysis b) calculate the required sample size for follow-‐up experiments
Parameters: • Power (sensi-vity): probability of detec-ng the effect (usually ≥ 0.80) • Sample size: number of biological replicates • FDR: probability of rejec-ng the null hypothesis even though it is true (usually ≤ 0.05) • Variance: sample variance es-mated from current experiment • Effect size: minimum devia-on from the null hypothesis that you hope to detect (log fold change)
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
22
Statistical power estimation
Sta,
s,cal pow
er =
Prob
ability(detec,n
g true
FC)
Visualizes the power of an analysis at any desired fold change with a fixed number of biological replicates and FDR
0.0
0.2
0.4
0.6
0.8
1.0
1.05 1.1 1.15 1.2 1.25 1.3
Desired fold change
Powe
r
Number of replicates is 3FDR is 0.01
23
Sample size calculation
Visualizes the minimal number of biological replicates required to observe a desired fold change at a fixed sta-s-cal power and FDR
05
1015
2025
3035
1.05 1.1 1.15 1.2 1.25 1.3
0 0.001 0.001 0.002 0.003 0.005Coefficient of variation, CV
Desired fold change
Min
imal
num
ber o
f bio
logi
cal r
eplic
ates FDR is 0.01
Statistical power is 0.9
24
• Includes • R version, loaded so\ware libraries, Op-ons selected by the user, Data structure MSstats
recognizes, Comple-on of intermediate analysis steps, Warning messages • Help troubleshoot poten-al problems
4. Progress report : msstats.log
25
Design of follow-‐up study
Data processing and Quality control
Experimental design
Group comparison
MSstats
MSstats as an external tool in Skyline
• For the beginner of R or other sta-s-cal tools, we can do sta-s-cal analysis with default op-ons through Skyline easily.
• In R, more op-ons and func-ons are available.
• Select normaliza-on method
• High quality feature selec-on
• Select which groups to compare
• Select sample size or power calcula-on
• Specify the desired FDR
MSstats functions in Skyline
Data : Rat-plasma for Risk of heart disease
27
Each Protein
High salt (Disease) Low salt (Healthy)
Sub1 … Sub7 Sub8 … Sub14 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3
Pep*Tran1 X X X … X X X X X X … X X X
Pep*Tran2 X X X … X X X X X X … X X X
Pep*Tran3 X X X … X X X X X X … X X X
• Label-‐free SRM experiment • High salt (7) vs. Low salt (7) • 3 Technical replicates • Total 42 injec-ons (Runs) • 48 proteins • Comparison : High Salt – Low Salt (Disease-‐Healthy)
Rat Plasma : label-‐free SRM
Examples of inconsistent (poor quality?) peptides
28
Rat Plasma : label-‐free SRM
Profile plot show the problema,c pep,des or transi,ons. We need to check what happen in this pep,de.
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● ●CSSLLWAGAAWLR_2 NLGVVVAPHALR_2
NP_001007697
NLGVVVAPHALR
29
Rat Plasma : label-‐free SRM
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● ●CSSLLWAGAAWLR_2 NLGVVVAPHALR_2
NP_001007697
CSSLLWAGAAWLR
30
Rat Plasma : label-‐free SRM
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● ●CSSLLWAGAAWLR_2 NLGVVVAPHALR_2
NP_001007697
Log2 FC and variation are different between before and after removing peptides
31
Rat Plasma : label-‐free SRM
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● ●CSSLLWAGAAWLR_2 NLGVVVAPHALR_2
NP_001007697
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 1 ● CSSLLWAGAAWLR_2
NP_001007697
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 1 ● NLGVVVAPHALR_2
NP_001007697
All features Only NLGV (red lines) Only CSSL (black lines)
log2FC SE Adj p-‐value log2FC SE Adj p-‐value log2FC SE Adj p-‐value
Result -‐2.6701 0.2214 <0.0001 0.8750 0.2399 0.0066 -‐6.2187 0.4152 <0.0001
Endogenous
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Diseased Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 1 ● NLGVVVAPHALR_2
NP_001007697
Featuares without interference
log2FC SE Adj p-‐value
Result 1.094 0.3880 0.0087
32
Examples of inconsistent peptides Rat Plasma : label-‐free SRM
Profile plot show inconsistent pa]ern per pep,des. We need to check that is there any measurement problem.
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s# peptide: 3 ● ● ●AIAYLNTGYQR_2 DLTGFPQGADQR_2 TVEHPFSVEEFVLPK_2
NP_036620
DLTGFPQGADQR
33
Rat Plasma : label-‐free SRM
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 3 ● ● ●AIAYLNTGYQR_2 DLTGFPQGADQR_2 TVEHPFSVEEFVLPK_2
NP_036620
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 3 ● ● ●AIAYLNTGYQR_2 DLTGFPQGADQR_2 TVEHPFSVEEFVLPK_2
NP_036620
AIAYLNTGYQR
34
Rat Plasma : label-‐free SRM
35
Log2 FC and variation are quite different depending on peptides.
Rat Plasma : label-‐free SRM
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 1 ● AIAYLNTGYQR_2
NP_036620
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 3 ● ● ●AIAYLNTGYQR_2 DLTGFPQGADQR_2 TVEHPFSVEEFVLPK_2
NP_036620
Endogenous
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Disease Healthy
0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● ●DLTGFPQGADQR_2 TVEHPFSVEEFVLPK_2
NP_036620
Endogenous
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0
10
20
30
21 42MS runs
Log2−i
nten
sitie
s
# peptide: 2 ● DLTGFPQGADQR_2 ● TVEHPFSVEEFVLPK_2
NP_036620
All features Only DLTG and TVEH Only AIAY
log2FC SE Adj p-‐value log2FC SE Adj p-‐value log2FC SE Adj p-‐value
Results 2.0642 0.2966 <0.0001 0.6167 0.1137 0.0005 5.0812 0.7390 <0.0001
Features without interference
log2FC SE Adj p-‐value
Results 0.8535 0.2334 0.001
36
External tool in Skyline
- From MSstats external tool webpage or ‘Tool store’ - Automatic installations for all related software and packages - One-click analysis
- Tutorial is available (https://skyline.gs.washington.edu/labkey/skyts/home/software/Skyline/tools/details.view?name=Msstats)
37
msstats.org and MSstats google group
- News about Msstats - 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
Acknowledgements
Purdue University o Prof. Olga Vitek o Mike Cheng
o Tsung-‐Heng Tsai
38
University of Washington o Prof. Mike MacCoss and Lab o Brendan MacLean o Yuval Boss
ETH Zürich o Prof. Ruedi Aebersold o Prof. Bernd Wollscheid o Maria Pavlou o Isabell Bludau
Centre for Genomic Regulation o Eduard Sabido o Cristina Chiva
UCSF o Erik Verschueren
39
Related works
Poster 038 : A system Suitability Monitoring Method of LC MS/MS Proteomic Experiment, Eralp Dogu Poster 041 : Rela-ve Protein Quan-fica-on in Mass Spectrometry-‐based Proteomics – A Split Plot Approach, Meena Choi Poster 046 : Nonlinear Regression Avoids Overly Op-mis-c Assay Characteriza-on, Cyril Galitzine Oral presenta,on : Monday 10:40am-‐10:55am gMODs : An Open Source Data Analysis Tool for Quan-fying the Differen-al Site Occupancy of Therapeu-c Protein Modifica-on : Tsung-‐Heng Tsai Parallel evening workshop : Monday 6:30 pm – 8 pm Computa-on and sta-s-cs for quan-ta-ve proteomics, Grand C