# Presentation: 394038

## DAnTEData Analysis Tool Extension

- Ashoka Polpitiya

## Where does DAnTE fit in?

- AMT Pipeline(LC-MS)

- Multialign

- DAnTE

- Biological Conclusions

- Normalize raw abundances and rollup to proteins using RRollup, QRollup, and ZRollup

- QRollup

- Other CSV
- files
- (microarray)

## DAnTE

## Analysis Flow in DAnTE

**Data Loading**
-  Peptide abundance
-  Peptide-Protein relations
-  Factors

**Variance Stabilization**
-  log2 or log10
-  Bias (additive/multiplicative)

**Replicate Normalization**
-  Linear Regression
-  Loess
-  Quantile

**Global Normalization**
-  Central tendency
-  Median absolute Deviation     (MAD)

**Investigative Plots**
-  Histograms
-  Boxplots
-  Correlation diagrams
-  MA Plots

**Impute Missing**
-  Substitute
-  Average
-  KNNimpute
-  SVDimpute etc.

**Rollup**
-  RRollup
-  ZRollup
-  QRollup
-  Rollup Plots

**Statistical Tests**
-   ANOVA
-  Mix Models

**Misc Analytical Methods**
-  PCA
-  PLS
-  Heatmaps (kmeans, hierachical)

**Other Features**
-  Filter ANOVA results
-  Save session

## Goals of a downstream tool in Proteomics

- Identify problematic datasets
- Normalize
- Remove systematic bias and variation due to technical artifacts
- Rolling up to proteins
- Hypothesis testing and feature discovery
- Fixed effects (treatment)
- Random effects (different LC columns, Batch)
- Unbalanced data (due to missing)
- PCA / PLS
- Clustering (Hierarchical / K-means)

## Goals of a downstream tool in Proteomics

- Identify problematic datasets
- Correlation Plots

- Dataset Names

- Dataset Names

- Outlier dataset

- Color legend with overlaid histogram of correlation values

## Goals of a downstream tool in Proteomics

- Identify problematic datasets
- Normalize
- Remove systematic bias and variation due to technical artifacts

- Dataset 1

- Dataset 2

- Dataset 1

- Raw

- Normalized

## Goals of a downstream tool in Proteomics

- Identify problematic datasets
- Normalize
- Remove systematic bias and variation due to technical artifacts
- Rolling up to proteins

- Datasets

- Raw

- Scaled

## Example dataset

- 3 Burn (human) samples and 3 Control samples.
- Each sample was run in duplicates, therefore 12 datasets.

- Burn

- Sham
- (control)

## Example dataset

- Group datasets using “Factors”
- Gender
- Sample type
- Technical replicate
- Biological Replicate
- Factors for Burn data
- _Condition_:             Burn / Sham
- _Replicates_: 1,1,2,2,3,3,4,4,5,5,6,6

- Burn

- Sham
- (control)

## Outline of the Analysis of Data

- Load data
- Initial diagnosis with plots
- Define factors
- Normalize
- Within a Factor
- Linear regression
- Loess
- Quantile
- Global
- MAD
- Mean Centering
- Rollup
- ANOVA

## Data loading example

## Correlations

- Dataset Names

- Dataset Names

- Outlier dataset

- Color legend with overlaid histogram of correlation values

## Normalizing - Box Plot Views

- Abundance

- Dataset Names

## Diagnostic plots for Linear Regression

- Raw

- After regression

- Regressing one dataset vs. second dataset

## Rolling Up Peptides to Protein Abundance

- Raw peptide abundances vs. dataset (for 1 protein)

- Scaled peptide abundances for this protein’s 5 peptides

- Scaled abun., outliers removed with Grubb’s test

- Median protein abundance (dark black line)

## Heatmaps of Protein Abundance

- Hierarchical clustering of rows

- K-means clustering of rows (using 5 clusters)

- Proteins

- Datasets

- Datasets

- Burn

- Sham

- Burn

- Sham

## Heatmaps of Peptide Abundance

- Hierarchical clustering of rows and columns

- Various conditions ??

## Complete DAnTE Feature List

- Data loading with peptide-protein group information
- Log transform
- Factor Definitions
- Normalization
- Linear Regression
- Loess
- Quantile normalization
- Median Absolute Deviation (MAD) Adj.
- Mean Centering
- Missing Value Imputation
- Simple
- mean/median of the sample
- Substitute a constant
- Advance
- Row mean within a factor
- kNN method
- SVDimpute
- Save tables / factors / session

## Plots
Histograms
Boxplots
Correlation plots
MA plots
PCA/PLS plots
Protein rollup plots
Heatmaps
Rolling up to Proteins
Reference peptide based scaling (RRollup)
Z-score averaging (ZRollup)
QRollup
Statistics
ANOVA
Simple 1-way
N-Way (provisions for unbalanced data)
Random effects (multi level) models (REML)
Q-values
Filters

- Histograms
- Boxplots
- Correlation plots
- MA plots
- PCA/PLS plots
- Protein rollup plots
- Heatmaps
- Rolling up to Proteins
- Reference peptide based scaling (RRollup)
- Z-score averaging (ZRollup)
- QRollup
- Statistics
- ANOVA
- Simple 1-way
- N-Way (provisions for unbalanced data)
- Random effects (multi level) models (REML)
- Q-values
- Filters

- Complete DAnTE Feature List

- \\floyd\Software\DAnTE

## To be added...

- Tests for normality, Nonparametric tests, post-hoc tests
- Incorporating an interactive heatmap control
- SMART-AMT, peptide prophet
- Protein Quality metrics
- Improve rollup methods to cluster and differentiate protein isoforms
- Alan Dabney’s work
- Network algorithms / Cytoscape

## Acknowledgements

- Weijun Qian
- Deep Jaitly
- Vlad Petyuk
- Josh Adkins
- Tom Metz
- Stephen Callister
- Brian LaMarche
- Ken Auberry
- Matt Monroe
- and the rest of the informatics group
- Joel Pounds
- Susan Varnum
- Bobbie-Jo Webb-Robertson

- Active users!
- Kim Hixson
- Josh Turse
- Charles Ansong
- Feng Yang
- Bryan Ham
- Christina Sorensen
- Angela Norbeck
- Sam Purvine
- Nathan Manes
- Jon Jacobs

- Gordon Anderson
- Dick Smith
- ... and the group.