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View Code? Open in Web Editor NEWAnalysis code for "Perturbation-response genes reveal signaling footprints in cancer gene expression"
License: Other
Analysis code for "Perturbation-response genes reveal signaling footprints in cancer gene expression"
License: Other
preliminary results looked like the expected RPPA phosph. do not correlate with mutations
could be interesting to look at this, but TCGA data is doubtful to work
is outdated
prerequesites
kinds of models
generation of null models
calc
should do volcano with covariate, maybe highlight in addition VHL that is only mutated in KIRC so we have another angle as well
Fit using pathway factors:
speed_linear
(w/o intercept)Fit using perturbed vs basal for each pathway:
speed_matrix
; VEGF surv increase?!MAPK += EGFR
- MAPK drug assocs > EGFR for MEKiFit using one matrix with 1
/-1
coefficients:
MAPK += EGFR
EGFR MEKi resistance (because non-MEK EGFR targets, esp. PI3K)MAPK, PI3K += EGFR
- mut assocs good (TP53, BRAF); EGFR survival increase (why?)Right now, GO and Reactome associations are conditioned on the best SPEED association to show whether or not they can be explained by the response-genes alone.
We could go one step further: make the statement that SPEED explains most of the GO/Reactome associations, but not the other way around. This would require a conditioning of SPEED scores on either/both of GO/Reactome association scores.
Also, it might make sense to condition on all pathways/all significant pathways - not sure, this might just get too many random correlations with 10 conditioning vars.
Overall, this would make the statement that SPEED outperforms the others stronger.
but around 5, 9, etc.
blocking #3
Cell line scores for different signatures
Associations with
when accessing the TCGA data directly, they provide references to which batch patients belong to; in addition, barcodes provide information about the collection and analysis centres
this info could be used to remove batch effects, if the TCGA didn't already do so - should talk to someone who worked with the data for longer + see if adjustment changes the survival associations
need to impute them using sig-only neighbourhood
does that change the assocs?
also, speed in general should work better with mutation-heavy tumors and CNA-heavy - can we find something interesting there?
For now it's almost only a recap of the results; add a bit more about when pathway expression, individual signatures are useful
Discuss a bit about the limitations
Should probably still add:
Z-scores: min 2 arrays in basal condition, average for perturbed
Format: yaml
---
id : <pathway.accession.#>
accession : <arrayexpress accession>
platform : <arrayexpress platform id>
pathway : <out of set below>
cells : <human-readable cell description>
treatment : <human-readable treatment description>
effect : activating|inhibiting
hours : <int> number of hours treatment
control :
- <list of control arrays>
perturbed :
- <list of perturbed arrays>
...
Pathways included:
2-4 features, glmnet
Model creation
QC
see what's going on here
na.omit()
for zscores leaves 5k genes, top assoc is p53 ~ 1e-19, some EGFR ~ 1e-14.
for 20k genes is is the same, that's a bit surprising
Right now, I scale the resampled scores per experiment.
some survival times are 0 w/ both status alive/deceased, this should not be
run clustering
clustering analysis
clustering plots
quite hard to find a specific script right now
maybe some crosstalk to explicitly model?
to compare against, at least
PI3K_variantTP53_R282W
and enrichment of this variant in some cancers (BRCA)
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