ResolVI Corrected Counts for Segmented Visium HD Data

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1

Hi,

I’m trying to understand the correct method for running ResolVI on Visium HD data that has been segmented using proseg as opposed to 8μM bins used in the reproducibility jupyter notebooks.

I’m currently following the tutorial which creates the generated expression as follows:

samples_corr = resolvi.sample_posterior(
model = resolvi.module.model_corrected,
return_sites = [‘px_rate’],
summary_fun = {‘post_sample_q50’: np.median},
num_samples = 3,
summary_frequency = 30,
)
samples_corr = pd.DataFrame(samples_corr).T

samples = resolvi.sample_posterior(
model = resolvi.module.model_residuals,
return_sites = [‘mixture_proportions’],
summary_fun = {‘post_sample_means’: np.mean},
num_samples = 3,
summary_frequency = 100,
)
samples = pd.DataFrame(samples).T

adata.layers[“generated_expression”] = samples_corr.loc[“post_sample_q50”, “px_rate”]

Is this the appropriate approach to take for cell segmented Visium HD data? Does this produce a ‘dense estimate of gene expression’ as opposed to counts that are corrected per cell? Also are the generated expression counts able to used with scran for normalization, and downstream spatial clustering approaches?

2

Hi, if you want to have an output that can be treated similar to original counts (such as using screen), use “obs” instead of “px_rate”. “px_rate” are the generated counts similar to what we propose to use in scVI. I wouldn’t feed those into scran. For spatial clustering you can use both of these counts, however I would usually recommend getting the latent representation and use this as replacement for steps such as PCA. You might want to check some of our scVI and scANVI tutorials to get a better overview of scvi-tools.

3

resolvae = resolvi
samples_corr = resolvae.sample_posterior(
model = resolvae.module.model_corrected,
return_sites = [“obs”],
summary_fun = {“post_sample_q50”: np.median},
num_samples = 3,
summary_frequency = 30,
)
samples_corr = pd.DataFrame(samples_corr).T

If I run this the samples_corr dataframe returned is empty. Is there something wrong with my input?

4

Almost:

samples_corr = resolvae.sample_posterior(
    model=resolvae.module.model_corrected,
    return_sites=['obs'],
    num_samples=10, return_samples=False, batch_size=1000, summary_frequency=10, return_observed=True, summary_fun=summary_fun)

Will do the trick, so just setting return_observed and it will output obs (this variable is observed during training - pyro computes a reconstruction loss and returning it has to be manually defined).