# Scvi-tools: different DEGs when comparing same data

**URL:** <https://discourse.scverse.org/t/scvi-tools-different-degs-when-comparing-same-data/3350>\
**Category:** scvi-tools\
**Tags:** integration, diff-exp, scvi\
**Created:** [November 19, 2024, 3:52pm UTC](https://discourse.scverse.org/t/scvi-tools-different-degs-when-comparing-same-data/3350 "2024-11-19T15:52:22Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![ashenflower](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.scverse.org/ashenflower/32/1467_2.png) [@ashenflower](https://discourse.scverse.org/u/ashenflower)\
**Post date:** [November 19, 2024, 3:52pm UTC](https://discourse.scverse.org/t/scvi-tools-different-degs-when-comparing-same-data/3350/1 "2024-11-19T15:52:22Z")

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Hello everybody,

I am new to single cells and differential analysis, so I am sorry if this question may sound stupid. However, I used scvi-tools to perform a differential expression analysis on the same cluster but across cells affected by two different conditions. After setting the seed using `scvi.settings.seed = 42`, my code looks like:

```auto
    idx1 = (adata.obs['leiden'] == 0) & (adata.obs['Response'] == 'No')
    idx2 = (adata.obs['leiden'] == 0) & (adata.obs['Response'] == 'Yes')
    scvi_de_noresp_vs_resp = model.differential_expression(idx1=idx1, idx2=idx2, batch_correction=True)

```

Then, I focused only on genes for which `is_de_fdr_0.05'` is `True` (referred to as “significant genes” from now on) . Now, the thing is: if I redo the same analysis, on the same exact data and with the same exact seed, just inverting the order of the idxs, such as:

```auto
    idx1 = (adata.obs['leiden'] == 0) & (adata.obs['Response'] == 'Yes')
    idx2 = (adata.obs['leiden'] == 0) & (adata.obs['Response'] == 'No')
    scvi_de_resp_vs_noresp = model.differential_expression(idx1=idx1, idx2=idx2, batch_correction=True)

```

I obtain a different list of significant genes. This is a bit surprising to me, because, to my understanding, the log fold change should be simmetrical, so ideally the abs value of the log fold change should be the same, just in opposite direction…So, I would expect to find the same list of significant genes, but this is not the case (although most of the significant genes overlap between the two analysis).

Is this behaviour expected? And if yes, why?

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<div class="post-metadata">

**Author:** ![cane11](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.scverse.org/cane11/32/241_2.png) [@cane11](https://discourse.scverse.org/u/cane11)\
**Post date:** [November 19, 2024, 4:11pm UTC](https://discourse.scverse.org/t/scvi-tools-different-degs-when-comparing-same-data/3350/2 "2024-11-19T16:11:26Z")

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Hi, DEG in scvi-tools are computed by random sampling from the underlying distribution. Fixing the random seed yields the same result for the same computation. However switching indices yields slightly different results (similar to another seed). Especially for very lowly expressed genes our estimates can be noisy and I would recommend manually removing them (maximum across both groups of estimated scale smaller than 1e-5 should be fine to filter).

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<div class="post-metadata">

**Author:** ![ashenflower](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.scverse.org/ashenflower/32/1467_2.png) [@ashenflower](https://discourse.scverse.org/u/ashenflower)\
**Post date:** [November 19, 2024, 4:26pm UTC](https://discourse.scverse.org/t/scvi-tools-different-degs-when-comparing-same-data/3350/3 "2024-11-19T16:26:32Z")

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Thank you very much for this quick answer, this is extremely helpful. So, in theory, it would be correct to expect the same results in both condition ( IImean, that the log fold change should be symmetric), right?