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Help with expected input #1
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Dear Kristoffer,
Thank you very much for your interest in our work. Here are the answers to
your questions.
1. In general we recommend users to collapse the tumor cells in each
patient, while collapsing non-malignant cells across all patients. For
example, the refGBM8 used in our paper has Patient-1-Tumor-subcluster1
,..., Patient-8-Tumor-subclusterN (60 tumor subclusters from 8 patients),
pericytes, endothelial, T cell, macrophage, oligodendrocytes (5
non-malignant cells across all patients). If there is substantial
heterogeneities in the non-malignant cells, one should treat each
cluster of non-malignant cell separately.
2. See attached pdf.
3. No minimum required. TED assumes the tumor expression in each sample is
conditional independent (conditional on input.phi).
Hope this helps.
Best,
Tinyi
…On Wed, Jan 29, 2020 at 10:20 AM Kristoffer Vitting-Seerup < ***@***.***> wrote:
Thanks for providing this tool - it looks extremely promising.
I have a couple of clarifying questions:
1. When having data from multiple patients you suggest collapsing cell
types per patient. Does that mean the input.phi matrix given to run.Ted
have X reference rows where X = p * c (and p= number of patients, and c =
number of celltypes)?
2. Could you give examples run times for some of the deconvolutions
you have done for the paper? Just so we have a ballpark number of what to
expect.
3. Is there are minimum number of bulk samples required to
deconvolute? Can TED deconvolute fx 6 samples (a 3x2 experiment)?
Cheers
Kristoffer
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Hi Tinyi Thanks for the quick answer! With regards to 2: I don't think the attachment was transfered to github? With regards to 3: Did you experiment with information sharing across bulk samples - does not seem a stretch to assume the the different cell types exist in somewhat same proportions in a cohort of bulk patients? |
Dear Kristoffer,
I have just added the runtime to the github repository. Kindly check it
out.
TED does not assume similar cell type proportion across bulk samples, which
is often not the case in TCGA samples due to the heterogeneity of the tumor
microenvironment. In fact, the posterior of the cell type fraction is so
strong, and is sufficiently driven by the mixture samples themselves.
TED does however assumes the non-malignant cells have the same expression
across bulk samples. This piece of shared information is used to update the
initial estimates of cell type fraction.
Hope this answers your question.
Best,
Tinyi
…On Fri, Jan 31, 2020 at 3:58 AM Kristoffer Vitting-Seerup < ***@***.***> wrote:
Hi Tinyi
Thanks for the quick answer!
With regards to 2: I don't think the attachment was transfered to github?
With regards to 3: Did you experiment with information sharing across bulk
samples - does not seem a stretch to assume the the different cell types
exist in somewhat same proportions in a cohort of bulk patients?
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Beautiful! That is a very nice idea of sharing only the normal cells. Impressive runtimes! Thanks! |
Dear Kristoffer,
A vignette has now been included in the github. Also, I have updated the
run.Ted function so that it will now automatically collapse, align the
genes on the common subset between reference and bulk, and normalized the
scRNA-seq reference.
Best,
Tinyi
…On Mon, Feb 3, 2020 at 4:52 AM Kristoffer Vitting-Seerup < ***@***.***> wrote:
Closed #1 <#1>.
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Thanks! :D |
Thanks for providing this tool - it looks extremely promising.
I have a couple of clarifying questions:
input.phi
matrix given torun.Ted
have X reference rows where X = p * c (and p= number of patients, and c = number of celltypes)?Cheers
Kristoffer
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