Open dhimmel opened 7 years ago
Output looks like this:
acronym | entrez_gene_id | patients | tumor_mean | normal_mean | mean_diff | t_stat | mlog10_p_value | symbol |
---|---|---|---|---|---|---|---|---|
BLCA | 1 | 19 | 5.328 | 4.966 | 0.3621 | 1.062 | 0.5197 | A1BG |
BLCA | 2 | 19 | 12.48 | 15.25 | -2.765 | -10.23 | 8.202 | A2M |
BLCA | 9 | 19 | 6.339 | 6.009 | 0.3295 | 1.197 | 0.6073 | NAT1 |
BLCA | 10 | 19 | 1.008 | 0.5923 | 0.4162 | 1.472 | 0.8006 | NAT2 |
BLCA | 12 | 19 | 6.082 | 9.63 | -3.548 | -4.912 | 3.95 | SERPINA3 |
BLCA | 13 | 19 | 4.782 | 4.503 | 0.2795 | 0.3025 | 0.1159 | AADAC |
BLCA | 14 | 19 | 11.43 | 11.45 | -0.02632 | -0.2713 | 0.1028 | AAMP |
BLCA | 15 | 19 | 0.7949 | 0.5788 | 0.2161 | 1.029 | 0.4986 | AANAT |
@gwaygenomics what do you think of the plot in 5.differential-expression.ipynb
? In other words, do you see biology within?
The heatmap shows differential expression signatures for each cancer. Genes were transformed to 100 genes using NMF. Fill color represents the t-statistic.
what do you think of the plot
There is a lot going on in it! I am going to outline what it is and try to extract biology along the way.
tumor
vs. tumor adjacent
t.test(tumor, tumor_adjacent, var.equal=False)
)I think a rough description of what is going on with the genes in each component would spark more biological discussion. Another thing to keep in mind is that the "normals" are actually "tumor adjacent" and are opportunistically extracted from "nearby" tissue when the surgeon can (therefore, no GBM tumor adjacent). I think its important to not consider this "normal" (Troester et al. 2016) (to be clear, the terminology is ok, but I mean thinking about this as normal tissue could be a trap!)
A conceptual summary comparing the approach with Gross et al. would be good somewhere in the notebook - particularly if you link to that paper.
Agree with @gwaygenomics that some sort of biologically meaningful notation of the metagenes would be beneficial. Out of my element in terms of what's possible, but grouping genes by pathway initially instead of metagene could be something similar but with inherent meaning.
Along the same lines, expanded names for the cancers, rather than just TCGA acronyms would improve readability.
@gwaygenomics we're using a paired t-test, about which the following has been said:
The paired t-test calculates the difference within each before-and-after pair of measurements, determines the mean of these changes, and reports whether this mean of the differences is statistically significant. A paired t-test can be more powerful than a 2-sample t-test because the latter includes additional variation occurring from the independence of the observations. A paired t-test is not subject to this variation because the paired observations are dependent. Also, a paired t-test does not require both samples to have equal variance. Therefore, if you can logically address your research question with a paired design, it may be advantageous to do so, in conjunction with a paired t-test, to get more statistical power.
Thanks @cgreene, @ksimeono, & @gwaygenomics for the comments. Will be at least a week before I get around to addressing them.
A paired t-test is not subject to this variation because the paired observations are dependent. Also, a paired t-test does not require both samples to have equal variance.
Ah yes, good point!
Note to untrack
data/complete/differential-expression.tsv.bz2
before merging.This is something that @ksimeono -- a cancer biologist -- was interested it. It's potentially out of scope for Cognoma, but I thought it's pretty useful.
No rush to merge, just wanted to get this up here.