error in calculating biadjacency_matrix

[dnyansagar]

Hi I am following pipeline from the GLUE publication to get the regulatory inference. In that attempt, I am at /GLUE/tree/master/experiments/RegInf)/s03_peak_gene_validation.py However I get following error

========================================================== Clustering metacells...

ValueError Traceback (most recent call last) /tmp/ipykernel_3936564/4102559450.py in 1 corr = biadjacency_matrix( 2 utils.metacell_corr( ----> 3 rna, atac, "X_UMAP", n_meta=200, skeleton=dist_graph, method="spr" 4 ), genes.index, peaks.index, weight="corr", dtype=np.float32 5 )

~/utils.py in metacell_corr(rna, atac, use_rep, n_meta, skeleton, method) 26 def metacell_corr(rna, atac, use_rep, n_meta=200, skeleton=None, method="spr"): 27 print("Clustering metacells...") ---> 28 rna_agg, atac_agg = get_metacells_paired(rna, atac, use_rep, n_meta=n_meta) 29 print("Computing correlation...") 30 return _metacell_corr(rna_agg, atac_agg, skeleton=skeleton, method=method)

~/utils.py in get_metacells_paired(rna, atac, use_rep, n_meta) 15 kmeans.train(rna.obsm[use_rep]) 16 _, rna.obs["metacell"] = kmeans.index.search(rna.obsm[use_rep], 1) ---> 17 atac.obs["metacell"] = rna.obs["metacell"].to_numpy() 18 rna_agg = scglue.data.aggregate_obs(rna, "metacell") 19 atac_agg = scglue.data.aggregate_obs(atac, "metacell")

~/miniconda3/envs/mypython3/lib/python3.7/site-packages/pandas/core/frame.py in setitem(self, key, value) 3610 else: 3611 # set column -> 3612 self._set_item(key, value) 3613 3614 def _setitem_slice(self, key: slice, value):

~/miniconda3/envs/mypython3/lib/python3.7/site-packages/pandas/core/frame.py in _set_item(self, key, value) 3782 ensure homogeneity. 3783 """ -> 3784 value = self._sanitize_column(value) 3785 3786 if (

~/miniconda3/envs/mypython3/lib/python3.7/site-packages/pandas/core/frame.py in _sanitize_column(self, value) 4507 4508 if is_list_like(value): -> 4509 com.require_length_match(value, self.index) 4510 return sanitize_array(value, self.index, copy=True, allow_2d=True) 4511

~/miniconda3/envs/mypython3/lib/python3.7/site-packages/pandas/core/common.py in require_length_match(data, index) 530 if len(data) != len(index): 531 raise ValueError( --> 532 "Length of values " 533 f"({len(data)}) " 534 "does not match length of index "

ValueError: Length of values (80789) does not match length of index (73872)

Does 'rna' and 'atac' use_rep need to be the same size?

Can someone please explain the error

[Jeff1995]

Thanks for the report!

The scripts in /GLUE/tree/master/experiments/RegInf include comparisons with other regulatory inference methods which are unnecessary for end users, and cannot be run as is. In this case the corr = biadjacency_matrix(...)) computes correlation-based regulatory inference as one of these comparisons. Here we were using paired RNA and ATAC data so the utils.metacell_corr function only works with paired multi-omics profiles. The data you are working on seems to be unpaired (cell number differs across RNA and ATAC). That's why it's throwing this error.

A dedicated regulatory inference tutorial with no unnecessary code is still in the works (see #15). It should be ready by the end of this month. Sorry for the inconvenience. I will let you know then.

[dnyansagar]

Thank you Jeff for your reply. Please let me know when the dedicated tutorial is ready. I look forward to it. In the meantime I plan to create subset of bigger object to match the size of the smaller object for integration. Do you think this approach will work or should be theoretically done?

[Jeff1995]

Of course! I'll let you know.

For the second question, the GLUE training process ensures that smaller objects is upsampled to match bigger objects, so I think there is no need to subset the bigger object.

[dnyansagar]

Thank you Jeff for new tutorial.

[Jeff1995]

You are welcome! The new tutorial requires an updated version (v0.3.0) of scglue. I've released it on PyPI, but still having some problems with the bioconda update. Hopefully I can get it working in a few days.

Let me know if you have further problems with the new tutorial though :)