Comments (2)
Hi!
If you have three conditions and want to look at changes between all of them, I can think of two options, based on the question you ask. Assuming that your conditions right now are a column in adata.obs
that is sth like "A,A,B,B,C,A,B,...":
If you want to know what changes between conditions (i.e. whats the difference between treatment A and treatment B), then you can remove all samples of one condition from the data and compare the other two, then repeat this 3 times (A & B, A & C, and B & C), as you described.
I'm not quite sure what you mean with your last sentence, though. The discussion you linked was about selecting a reference cell type (that is assumed to stay constant under the condition(s)), not about conditions.
If you want to know what difference the presence of a condition makes (i.e. treatment A vs. not treatment A), you can just make new (binary) columns in adata.obs
that represent this (is_A = (1,1,0,0,0,1,0,...)
) for all conditions and use these in the formula, one at a time.
You might take these results with a grain of salt, though, since your samples with is_A = 0
have either condition B or C. If sth happens in B and C, but not in A, you will get an effect on A. As long as you don't have samples where none of the conditions is present, there's no way to distinguish these interpretations.
from sccoda.
Ok perfect, thank you. That makes perfect sense, I will simply remove a condition and run the three contrasts as you suggested. You second suggestion was also informative, thank you.
from sccoda.
Related Issues (20)
- verbose option for `sample_hmc` HOT 1
- Looping over cell types to use as reference: discrepancy? HOT 11
- Importance of warnings/errors in model_scelltype_Time.sample_hmc() output HOT 1
- col_wrap in utils.visualization.boxplots HOT 1
- WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. HOT 2
- Influence of subtypes present in the dataset HOT 4
- Which tensorflow version to use? HOT 3
- Is the input data expected by scCODA the size of the cell type for each sample? HOT 1
- P value (FDR) and interpretation of "Final Parameter" HOT 2
- est_fdr did not change the result HOT 6
- WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. HOT 1
- Cell identities displaying as numbers? HOT 1
- loading data with dat.from_scanpy HOT 2
- Zero log2-fold change but credible change being identified? HOT 3
- Understanding inclusion probability HOT 4
- All cell types as reference loop error HOT 17
- Feature Request: log10 scaling for Boxplots HOT 1
- Feature Request: Automatically print significance indicator in boxplots HOT 1
- access sim_results.summary() for further analysis in R HOT 2
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from sccoda.