Closed mainharryHR closed 1 month ago
A good place to learn is this guide from the developers of edgeR: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873980/
Milo uses largely the same syntax and format for model contrasts.
A good place to learn is this guide from the developers of edgeR: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873980/
Milo uses largely the same syntax and format for model contrasts.
Thank you for reply. I went through the paper and multiple comparison vignette many times. https://bioconductor.org/packages/release/bioc/vignettes/miloR/inst/doc/milo_contrasts.html
My above pipeline always gives me the identical results. I am almost sure testNhoods(Bcells_milo, design=~ Cancer, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = mod.contrasts)
, model.contrasts = mod.contrasts might be not working. In addition, the published papers (2 decent papers) only use the design
, but not model.contrasts
, for their multiple comparisons by subsetting.
I have been struggling with this part for several days. It will be great if someone could help me to figure out.
I really appreciate your kind help and great packages
Dear Mike, I am wondering if you have good strategies to compare 3 conditions: A, B, C?
I tried the subsetting strategies, the results make sense. But subsetting will not consider the multiple comparisons, which might be not a perfect way. Here comes results from the subsetting strategies.
Here comes the mod.contrast
contrast.all <- c("CancerControl - CancerNo", "CancerControl - CancerCRC", "CancerNo - CancerCRC")
model <- model.matrix(~ 0 + Cancer, data=design_matrix)
mod.contrasts <- makeContrasts(contrasts=contrast.all, levels = model)
But after testNhoods, I got the identical results no matter how I change the mod.contrasts[c("CancerControl"), c("CancerControl - CancerCRC")
, Plus the results are all negative LogFC , which does not make senses as well.
ControlVSNo <- testNhoods(Bcells_milo, design=~ Cancer, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = mod.contrasts[c("CancerControl"), c("CancerControl - CancerNo")])
Any comments are welcome! Thank you very much.
GitHub issues are for reporting issues with software, such as bugs and errors. I suggest you post an issue on the Bioconductor forum to explain what you seek to do and ask for advice there.
Thanks Mike,
I think this is a bug in the MiloR package. Even in the official multiple comparison vignette, the results are identical after multiple comparisons . I am experiencing the exact same problems.
Thank you very much for looking into that with your team member.
Have a great day! Best, Harry
If you try running each contrast separately rather than a vector do you get a different result? Fundamentally, Milo doesn't do anything to the model contrasts, it just hands them straight over to edgeR for the QL F-test.
OK - the issue is in the vignette, not Milo - the vignette passes mod.contrasts
to testNhoods
when this should be all.contrasts
. Milo converts the vector of contrasts into the correct format internally using the limma
function makeContrasts
. Therefore, you should pass contrast.all
to testNhoods
, not mod.contrasts
. The vignettes have been updated on the devel branch.
If you try running each contrast separately rather than a vector do you get a different result? Fundamentally, Milo doesn't do anything to the model contrasts, it just hands them straight over to edgeR for the QL F-test.
Thank you for information. When subsetting, I have different results.
I am curious how to compute the spacialFDR for multiple comparison?
For multiply comparision, P value can be adjusted by "BH", I guess each row will be assumed as one condition? or cell type?
Many thanks
OK - the issue is in the vignette, not Milo - the vignette passes
mod.contrasts
totestNhoods
when this should beall.contrasts
. Milo converts the vector of contrasts into the correct format internally using thelimma
functionmakeContrasts
. Therefore, you should passcontrast.all
totestNhoods
, notmod.contrasts
. The vignettes have been updated on the devel branch.
contrast.all <- c("CancerControl - CancerNo", "CancerControl - CancerCRC", "CancerNo - CancerCRC")
model <- model.matrix(~ 0 + Cancer, data=design_matrix)
mod.contrasts <- makeContrasts(contrasts=contrast.all, levels = model)
contrast.res <- testNhoods(Bcells_milo, design=~ Cancer, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = contrast.all)
,
I made the change accordingly, but it gives me errors. Could you pinpoint which code should I modify?
You need to provide the code AND the errors - it's impossible to help and debug any problems without the appropriate information.
Next, you need to make sure that what you are doing is consistent. The model contrasts uses the convention of ~ 0 + Variable
but you're passing ~Variable
into testNhoods
<- these are not equivalent behaviours.
I strongly recommend you re-read the guide to making good design matrices, as well as the limma/edgeR guides on contrast, before proceeding further.
Hi Mike
I am in a similar boat. And I think your suggestion to past contrast.all instead of mod.contrast fixed the spurious log fold change calculations. However I want to make sure its correctly set up. I am pasting a mock example
contrast_list <- c('Group_varGroup1 - Group_varGroup2','Group_varGroup4 - Group_varGroup3')
contrast.res_Group1vsGroup2 <- testNhoods(milo_obj, design=~ 0 + Group_var, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = contrast_list[1])#first comparison
contrast.res_Group4vsGroup2 <- testNhoods(milo_obj, design=~ 0 + Group_var, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = contrast_list[2])#first comparison```
Hi @dravichandar - yes that looks correct. You can also pass contrast_list
in as a single vector to test both contrasts - but this will return an omnibus test p-value/SpatialFDR; LFCs for each test are returned on each nhood.
I am trying to study how different cancer types and sex affects the cell abundances in different cell types.
design_matrix <- data.frame(colData(Bcells_milo))[, c("patientNumber", "Cancer", "Sex")]
contrast.all <- c("CancerControl - CancerNo", "CancerControl - CancerCRC", "CancerNo - CancerCRC")
model <- model.matrix(~ 0 + Cancer, data=design_matrix)
mod.contrasts <- makeContrasts(contrasts=contrast.all, levels=model)
ControlVSNo <- testNhoods(Bcells_milo, design=~ Cancer, design.df=design_matrix, fdr.weighting="graph-overlap", model.contrasts = mod.contrasts[c("CancerControl"), c("CancerControl - **CancerNo**")])
ControlVSNo %>% arrange(SpatialFDR) %>% head()
ControlVSNo <- annotateNhoods(Bcells_milo, ControlVSNo, coldata_col = "hairuV2") plotDAbeeswarm(ControlVSNo, group.by = "Cell types", alpha = 0.1)
I tried different combination of following: model.contrasts = mod.contrasts[c("CancerControl"), c("CancerControl - CancerCRC")] , they give me the same results. I feel the
model.contrasts
is not working properly.Any comments are welcome.
Many thanks. Harry