Model results could be stored in another SE. The contrasts are treated as
samples, and stuff like p-values, effect sizes, etc as assays. Question is
whether those should just be tacked onto a MAE, or kept as separate
objects, or stored along with the MAE in a larger analysis-level workflow
object.
I think analysis of multiassay experiments often will consists of
integration following assay-specific models. Not necessarily, but it will
be a usecase. Organizing multiple model fits together could be useful, for
downstream comparison / integration.
Say you find DMRs and DE genes. Now you want t
That's great help, Levi, I will try your suggestions.
thank you,
francesco
Il 25/10/2017 00:28, Levi Waldron ha scritto:
OK, I think I'm understanding better now. The best immediate solution that
I can think of is a SummarizedExperiment for each signatures database, then
pasting those Summarize
On Mon, Oct 23, 2017 at 9:15 PM, Kasper Daniel Hansen <
kasperdanielhan...@gmail.com> wrote:
> Are you discussing statistics of the same dimension as the data (unusual)
> or summary statistics? We should think about a MAE version of summary
> statistics, but that is not captured in current represe
OK, I think I'm understanding better now. The best immediate solution that
I can think of is a SummarizedExperiment for each signatures database, then
pasting those SummarizedExperiments together with a MultiAssayExperiment.
Something like this:
set.seed(1)
statvals <- matrix(rnorm(100), ncol=5)
r
Thank you!
Fig 1 shows the pipeline for a single database of pathways, but we
used 10 different databases (GO, KEGG, Reactome...). Currently we use
all of MSigDB, which includes 24 subcategories, and we have a matrix
of ES and a matrix of pvalues for each. You always have the same drugs
over colum
On Oct 24, 2017 6:14 AM, "Francesco Napolitano" wrote:
I'm converting gene expression profiles to "pathway expression
profiles" (https://doi.org/10.1093/bioinformatics/btv536), so for each
pathway I have an enrichment score and a p-value. I guess it would be
like modeling gene expression data whe
Just realized my answer yesterday went to Francesco and not the list:
Since it sounds like you have two matrices of the same dimensions, why not
represent these as two assays in a SummarizedExperiment? E.g.:
> statvals <- matrix(rnorm(100), ncol=5)
> pvals <- pnorm(statvals)
> library(Summarized
I'm converting gene expression profiles to "pathway expression
profiles" (https://doi.org/10.1093/bioinformatics/btv536), so for each
pathway I have an enrichment score and a p-value. I guess it would be
like modeling gene expression data where limma-like preprocessing was
performed, so you have a
Hi,
thanks for the suggestion. The point is that I have multiple assays, each
of which is made of statistic-p-value pairs. Within each assay, the two
matrices have of course same rows and columns, but different assays will
have different rows (same columns). So I should flatten everything and
mode
Are you discussing statistics of the same dimension as the data (unusual)
or summary statistics? We should think about a MAE version of summary
statistics, but that is not captured in current representation I would say.
Best,
Kasper
On Mon, Oct 23, 2017 at 4:50 PM, Vincent Carey
wrote:
> no ans
no answers yet? would it work to put your matrices as separate assays in a
SummarizedExperiment?
as long as they are conformant in dimensions and dimnames I think that
would work. That
SummarizedExperiment would then work well in an MAE.
On Mon, Oct 23, 2017 at 1:00 PM, Francesco Napolitano
wro
Hi,
I'm trying to build a MultiAssayExperiment. However, in my case each
assay should ideally include two matrices: one with a statistic and
another one with the corresponding p-value. I'm currently managing
each of them simply as a list of two matrices, but assay class expects
table-like data. I
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