LinAlgError when running dyn.tl.dynamics()

[Ckenen]

When I run dyn.tl.dynamics(adata, group="group", NTR_vel=True), it will raise LinAlgError: SVD did not converge.
However, if I do not provide group parameter, not error will be raised.
But I need M_s and velocity_S in my downstream analysis.
How can I fixed this problem?

Detail as follow:
LinAlgError Traceback (most recent call last)
Cell In [150], line 2
1 # dyn.tl.dynamics(adata, NTR_vel=True)
----> 2 dyn.tl.dynamics(adata, group="group", NTR_vel=True)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/tools/dynamics.py:521, in dynamics(adata, filter_gene_mode, use_smoothed, assumption_mRNA, assumption_protein, model, est_method, NTR_vel, group, protein_names, concat_data, log_unnormalized, one_shot_method, fraction_for_deg, re_smooth, sanity_check, del_2nd_moments, cores, tkey, **est_kwargs)
518 warnings.simplefilter("ignore")
520 if experiment_type.lower() in ["one-shot", "one_shot"]:
--> 521 est.fit(one_shot_method=one_shot_method, **est_kwargs)
522 else:
523 # experiment_type can be kin also and by default use
524 # conventional method to estimate k but correct for time
525 est.fit(**est_kwargs)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/velocity.py:1368, in ss_estimation.fit(self, intercept, perc_left, perc_right, clusters, one_shot_method)
1357 if cores == 1:
1358 for i in tqdm(range(n_genes), desc="estimating gamma"):
1359 (
1360 k[i],
1361 k_intercept[i],
1362 _,
1363 k_r2[i],
1364 _,
1365 k_logLL[i],
1366 bs[i],
1367 bf[i],
-> 1368 ) = self.fit_gamma_stochastic(
1369 self.est_method,
1370 U[i],
1371 S[i],
1372 US[i],
1373 S2[i],
1374 perc_left=perc_left,
1375 perc_right=perc_right,
1376 normalize=True,
1377 )
1378 else:
1379 pool = ThreadPool(cores)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/velocity.py:1690, in ss_estimation.fit_gamma_stochastic(self, est_method, u, s, us, ss, perc_left, perc_right, normalize)
1688 bs, bf = None, None
1689 if est_method.lower() == "gmm":
-> 1690 k = fit_stochastic_linreg(u[mask], s[mask], us[mask], ss[mask])
1691 elif est_method.lower() == "negbin":
1692 phi = compute_dispersion(s, ss)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/utils_velocity.py:420, in fit_stochastic_linreg(u, s, us, ss, fit_2_gammas, err_cov)
418 else:
419 cov = np.diag(E.var(1))
--> 420 cov_inv = np.linalg.pinv(cov)
422 # generalized least squares
423 xy, xx = 0, 0

File <array_function internals>:5, in pinv(*args, **kwargs)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:2002, in pinv(a, rcond, hermitian)
2000 return wrap(res)
2001 a = a.conjugate()
-> 2002 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
2004 # discard small singular values
2005 cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)

File <array_function internals>:5, in svd(*args, **kwargs)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:1660, in svd(a, full_matrices, compute_uv, hermitian)
1657 gufunc = _umath_linalg.svd_n_s
1659 signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1660 u, s, vh = gufunc(a, signature=signature, extobj=extobj)
1661 u = u.astype(result_t, copy=False)
1662 s = s.astype(_realType(result_t), copy=False)

File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:97, in _raise_linalgerror_svd_nonconvergence(err, flag)
96 def _raise_linalgerror_svd_nonconvergence(err, flag):
---> 97 raise LinAlgError("SVD did not converge")

LinAlgError: SVD did not converge

[Inthedawn1001]

Hi Ckenen, I'm new to this area and try to analysis some public available data using Dynamo, and earlier I ran to the same error:
LinAlgError: SVD did not converge
In my case I extracted a subgroup of cells which are close to each other in a broad lineages from an integrated data, but failed to preprocess them and calculate RNA velocities by using dyn.tl.dynamics.
When I checked the adata.X object I used thoroughly, I found all the values in adata.X are log1p processed because in the earlier step they are scaled and normalized by Scanpy commands and did not save the original values which are very likely what Dynamo preprocess command requires to obtain the downstream variables like M_s and M_u, etc.
So I simply replaced the subset adata.X value with the original value in the raw loom files, and went smoothly in preprocess and dyn.tl.dynamics section.