Changed to decoupler for pseudobulking (#141) #153
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View / edit / reply to this conversation on ReviewNB Zethson commented on 2023-02-03T14:13:21Z Uhm, why are they all outcommented? alitinet commented on 2023-02-03T14:28:26Z oh thanks, forgot to remove, we don't need it any more |
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View / edit / reply to this conversation on ReviewNB Zethson commented on 2023-02-03T14:13:22Z Line #2. adata_pb = dc.get_pseudobulk(adata, sample_col='sample', groups_col='cell_type', layer='counts', min_prop=0.2, min_smpls=3) Quite a long line, I'd add a line break after every parameter. |
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View / edit / reply to this conversation on ReviewNB Zethson commented on 2023-02-03T14:13:23Z Line #1. sc.pp.normalize_total(adata_pb, target_sum=1e4) What made you do this? Think most normalize to millions? alitinet commented on 2023-02-03T14:29:53Z following decoupler tutorial here https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html Zethson commented on 2023-02-03T14:31:05Z I'd not change this without discussing it with Soroor |
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View / edit / reply to this conversation on ReviewNB Zethson commented on 2023-02-03T14:13:24Z The dimensions are now very very different.
Before: 16 x 15710 Now: 16 x 2435
Intended? Could you explain this, please? alitinet commented on 2023-02-03T14:41:34Z It comes from https://decoupler-py.readthedocs.io/en/latest/generated/decoupler.get_pseudobulk.html#decoupler.get_pseudobulk params min_prop=0.2 and min_smpls=3, which filter out genes that are expressed in <20% of all cells and genes that are expressed in <3 samples alitinet commented on 2023-02-03T14:43:41Z I'm not sure if it'd better to make these more permissive |
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View / edit / reply to this conversation on ReviewNB Zethson commented on 2023-02-03T14:13:25Z The new plot looks pretty different from the old one. What happened? alitinet commented on 2023-02-03T14:41:49Z Didn't notice, thanks! will fix |
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oh thanks, forgot to remove, we don't need it any more View entire conversation on ReviewNB |
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following decoupler tutorial here https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html View entire conversation on ReviewNB |
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I'd not change this without discussing it with Soroor View entire conversation on ReviewNB |
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It comes from https://decoupler-py.readthedocs.io/en/latest/generated/decoupler.get_pseudobulk.html#decoupler.get_pseudobulk params min_prop=0.2 and min_smpls=3, which filter out genes that are expressed in <20% of all cells and genes that are expressed in <3 samples View entire conversation on ReviewNB |
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Didn't notice, thanks! will fix View entire conversation on ReviewNB |
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I'm not sure if it'd better to make these more permissive View entire conversation on ReviewNB |
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Hey @soroorh, we changed to decoupler for pseudobulk creation which filters out genes that are expressed in <20% of all cells and genes that are expressed in < 3 samples. After this step, we are left with 2435 genes out of original 15710 genes. Do you think it would make sense to make this filtering step more permissive or is it ok as it is? Also, for pb normalization, should we use 1e6 (as before) or 1e4 (from decoupler tutorial) as the normalizing factor? |
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Hey @alitinet. Since you are using I would go with 1e6, as this is closer to how counts-per-million (CPM) is computed in edgeR. |
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@alitinet this chapter should then also use the pertpy dataloader for the kang dataset! https://pertpy.readthedocs.io/en/latest/usage/data/pertpy.data.kang_2018.html#pertpy.data.kang_2018 |
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