Trang Le
#math graduate. Postdoc fellow with Jason Moore.
and some comments on figures...
Indexing cell composition to baseline reveals
sample clustering by DOL.
Similar for plasma cytokines/chemokine concentration
Significant
Non-significant
x 2
x 2
x 8
x 8
Normalized cell counts
Plasma concentration
Similar for proteins and metabolites:
differences in protein composition ↑ as time ↑
Figure 4 is not very informative. I would use histograms or stacked bar charts for each comparison. Caveat: node sizes not shown.
DOL3 vs. DOL0
DOL7 vs. DOL0
Metabolome
Proteome
Transcriptome
Novel nodes
strongly associated across data types
poorly associated across data types
Features identified by DIABLO were more strongly enriched for known biological pathways.
expected
UpSet plots
Associations between data types were strongest at DOL1.
Meta integration shows significant congruence between conclusions from different platforms.
In PNG validation set, DIABLO predicted DLO well.
Functional network of DIABLO-selected features highlighted pathways lated to inteferon signaling, complement and neutrophil, as in Gambian cohort.
The transcriptomics data presented in this publication were submitted to the NCBI Gene Expression Omnibus under accession numbers GSE111404 and GSE123070. All other datasets including immune phenotyping, Luminex, metabolomics and proteomics data were archived on ImmPort (https://immport.niaid.nih.gov/home) under accession numbers SDY1256 and SDY1412.
By Trang Le
Presentation on 2019-09-12 for the journal club, Kim Lab.