part i: normalization & summarization - comparison ... · part i: normalization &...
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Part I: Normalization & Summarization -Comparison
Lieven Clement
Proteomics Data Analysis Shortcourse
statOmics, Krijgslaan 281 (S9), Gent, Belgium [email protected]
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 [email protected] 2/5
Median Summarization vs Peptide Based ApproachMedian Summarization
statOmics, Ghent University [email protected] 3/5
Median Summarization vs Peptide Based ApproachPeptide Based Approach
statOmics, Ghent University [email protected] 3/5
Mean Summarization vs Peptide Based Approach
Median Peptide-based
3 UPS and 0 yeast 23 UPS and 1 yeast
statOmics, Ghent University [email protected] 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 [email protected] 5/5