Part I: Normalization & Summarization -Comparison

Lieven Clement

Proteomics Data Analysis Shortcourse

statOmics, Krijgslaan 281 (S9), Gent, Belgium lieven.clement@ugent.be

Comparison of FC estimates upon summarization

Problems

strong peptide-effects

6= peptides/sample

6= # peptides/sample

non-random missingness

median maxLFQ peptideBased-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

CPTAC B vs A

log2

FC

statOmics, Ghent University lieven.clement@ugent.be 2/5

Median Summarization vs Peptide Based ApproachMedian Summarization

statOmics, Ghent University lieven.clement@ugent.be 3/5

Median Summarization vs Peptide Based ApproachPeptide Based Approach

statOmics, Ghent University lieven.clement@ugent.be 3/5

Mean Summarization vs Peptide Based Approach

Median Peptide-based

3 UPS and 0 yeast 23 UPS and 1 yeast

statOmics, Ghent University lieven.clement@ugent.be 4/5

Mean Summarization vs Peptide Based Approach

Median MaxLFQ Peptide-based

-2 -1 0 1 2 3

01

23

45

6

B vs A

Log2 Fold Change

-log10(P-value)

real log2FC

Sign. YeastSign. UPS

3 UPS and 0 yeast 7 UPS and 0 yeast 23 UPS and 1 yeast

statOmics, Ghent University lieven.clement@ugent.be 5/5