### Author:  [@Stefan](https://discourse.scverse.org/u/Stefan)
#### Post date: [March 12, 2021, 1:17pm UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/1 "2021-03-12T13:17:00Z")
Thank you for sharing these awesome tools with the bioinformatics community.
After using totalVI without any issues, I recently started experiencing problems in training my totalVI models and am writing to solicit some advice on how to resolve this. The totalVI example in the tutorial continues to run fine, but when I try something analogous for my data, I see the following:
```
import numpy as np
x = concatenated_adata.copy()
sc.pp.highly_variable_genes(
x,
batch_key="batch",
flavor="seurat_v3",
layer = 'counts',
n_top_genes=4000,
subset=True
)
scvi.data.setup_anndata(x,
batch_key = "batch",
layer = 'counts',
protein_expression_obsm_key = "protein_expression")
x_model = scvi.model.TOTALVI(x, latent_distribution = "normal", n_layers_decoder = 2)
x_model.train(max_epochs = 50,
use_gpu = True)
```
which results in the following error(s):
```
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scanpy/preprocessing/_highly_variable_genes.py:144: FutureWarning: Slicing a positional slice with .loc is not supported, and will raise TypeError in a future version. Use .loc with labels or .iloc with positions instead.
df.loc[: int(n_top_genes), 'highly_variable'] = True
--> added
'highly_variable', boolean vector (adata.var)
'highly_variable_rank', float vector (adata.var)
'means', float vector (adata.var)
'variances', float vector (adata.var)
'variances_norm', float vector (adata.var)
INFO Using batches from adata.obs["batch"]
INFO No label_key inputted, assuming all cells have same label
INFO Using data from adata.layers["counts"]
INFO Computing library size prior per batch
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
/lib/python3.8/site-packages/anndata-0.7.5-py3.8.egg/anndata/_core/anndata.py:1116: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_sub[k] = df_sub[k].cat.remove_unused_categories()
INFO Using protein expression from adata.obsm['protein_expression']
INFO Using protein names from columns of adata.obsm['protein_expression']
INFO Found batches with missing protein expression
INFO Successfully registered anndata object containing 35523 cells, 4000 vars, 6 batches,
1 labels, and 3 proteins. Also registered 0 extra categorical covariates and 0 extra
continuous covariates.
INFO Please do not further modify adata until model is trained.
GPU available: True, used: True
TPU available: None, using: 0 TPU cores
Set SLURM handle signals.
Epoch 1/50: 0%| | 0/50 [00:03 in
21 x_model = scvi.model.TOTALVI(x, latent_distribution = "normal", n_layers_decoder = 2)
22
---> 23 x_model.train(max_epochs = 50,
24 use_gpu = True)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/model/_totalvi.py in train(self, max_epochs, lr, use_gpu, train_size, validation_size, batch_size, early_stopping, check_val_every_n_epoch, reduce_lr_on_plateau, n_steps_kl_warmup, n_epochs_kl_warmup, adversarial_classifier, plan_kwargs, **kwargs)
257 **kwargs,
258 )
--> 259 return runner()
260
261 @torch.no_grad()
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/train/_trainrunner.py in __call__ (self)
73 self.trainer.fit(self.training_plan, train_dl)
74 else:
---> 75 self.trainer.fit(self.training_plan, train_dl, val_dl)
76 try:
77 self.model.history_ = self.trainer.logger.history
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/train/_trainer.py in fit(self, *args, **kwargs)
150 message="you defined a validation_step but have no val_dataloader",
151 )
--> 152 super().fit(*args, **kwargs)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
512
513 # dispath `start_training` or `start_testing` or `start_predicting`
--> 514 self.dispatch()
515
516 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
552
553 else:
--> 554 self.accelerator.start_training(self)
555
556 def train_or_test_or_predict(self):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
72
73 def start_training(self, trainer):
---> 74 self.training_type_plugin.start_training(trainer)
75
76 def start_testing(self, trainer):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
109 def start_training(self, trainer: 'Trainer') -> None:
110 # double dispatch to initiate the training loop
--> 111 self._results = trainer.run_train()
112
113 def start_testing(self, trainer: 'Trainer') -> None:
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
643 with self.profiler.profile("run_training_epoch"):
644 # run train epoch
--> 645 self.train_loop.run_training_epoch()
646
647 if self.max_steps and self.max_steps <= self.global_step:
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
491 # ------------------------------------
492 with self.trainer.profiler.profile("run_training_batch"):
--> 493 batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
494
495 # when returning -1 from train_step, we end epoch early
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_batch(self, batch, batch_idx, dataloader_idx)
653
654 # optimizer step
--> 655 self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
656
657 else:
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
424
425 # model hook
--> 426 model_ref.optimizer_step(
427 self.trainer.current_epoch,
428 batch_idx,
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py in optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)
1382 # wraps into LightingOptimizer only for running step
1383 optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, optimizer_idx)
-> 1384 optimizer.step(closure=optimizer_closure)
1385
1386 def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py in step(self, closure, *args, **kwargs)
212 profiler_name = f"optimizer_step_and_closure_{self._optimizer_idx}"
213
--> 214 self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
215 self._total_optimizer_step_calls += 1
216
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py in __optimizer_step(self, closure, profiler_name, **kwargs)
132
133 with trainer.profiler.profile(profiler_name):
--> 134 trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
135
136 def step(self, *args, closure: Optional[Callable] = None, **kwargs):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in optimizer_step(self, optimizer, opt_idx, lambda_closure, **kwargs)
276 )
277 if make_optimizer_step:
--> 278 self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
279 self.precision_plugin.post_optimizer_step(optimizer, opt_idx)
280 self.training_type_plugin.post_optimizer_step(optimizer, opt_idx, **kwargs)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in run_optimizer_step(self, optimizer, optimizer_idx, lambda_closure, **kwargs)
281
282 def run_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs):
--> 283 self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
284
285 def optimizer_zero_grad(self, current_epoch: int, batch_idx: int, optimizer: Optimizer, opt_idx: int) -> None:
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in optimizer_step(self, optimizer, lambda_closure, **kwargs)
158
159 def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
--> 160 optimizer.step(closure=lambda_closure, **kwargs)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/optim/optimizer.py in wrapper(*args, **kwargs)
87 profile_name = "Optimizer.step#{}.step".format(obj. __class__. __name__ )
88 with torch.autograd.profiler.record_function(profile_name):
---> 89 return func(*args, **kwargs)
90 return wrapper
91
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
25 def decorate_context(*args, **kwargs):
26 with self. __class__ ():
---> 27 return func(*args, **kwargs)
28 return cast(F, decorate_context)
29
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/optim/adam.py in step(self, closure)
64 if closure is not None:
65 with torch.enable_grad():
---> 66 loss = closure()
67
68 for group in self.param_groups:
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in train_step_and_backward_closure()
647
648 def train_step_and_backward_closure():
--> 649 result = self.training_step_and_backward(
650 split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
651 )
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
741 with self.trainer.profiler.profile("training_step_and_backward"):
742 # lightning module hook
--> 743 result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
744 self._curr_step_result = result
745
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in training_step(self, split_batch, batch_idx, opt_idx, hiddens)
291 model_ref._results = Result()
292 with self.trainer.profiler.profile("training_step"):
--> 293 training_step_output = self.trainer.accelerator.training_step(args)
294 self.trainer.accelerator.post_training_step()
295
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in training_step(self, args)
155
156 with self.precision_plugin.train_step_context(), self.training_type_plugin.train_step_context():
--> 157 return self.training_type_plugin.training_step(*args)
158
159 def post_training_step(self):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in training_step(self, *args, **kwargs)
120
121 def training_step(self, *args, **kwargs):
--> 122 return self.lightning_module.training_step(*args, **kwargs)
123
124 def post_training_step(self):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/train/_trainingplans.py in training_step(self, batch, batch_idx, optimizer_idx)
346 if optimizer_idx == 1:
347 inference_inputs = self.module._get_inference_input(batch)
--> 348 outputs = self.module.inference(**inference_inputs)
349 z = outputs["z"]
350 loss = self.loss_adversarial_classifier(z.detach(), batch_tensor, True)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/module/base/_decorators.py in auto_transfer_args(self, *args, **kwargs)
30 # decorator only necessary after training
31 if self.training:
---> 32 return fn(self, *args, **kwargs)
33
34 device = list(set(p.device for p in self.parameters()))
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/module/_totalvae.py in inference(self, x, y, batch_index, label, n_samples, transform_batch, cont_covs, cat_covs)
436 else:
437 categorical_input = tuple()
--> 438 qz_m, qz_v, ql_m, ql_v, latent, untran_latent = self.encoder(
439 encoder_input, batch_index, *categorical_input
440 )
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/nn/_base_components.py in forward(self, data, *cat_list)
984 qz_m = self.z_mean_encoder(q)
985 qz_v = torch.exp(self.z_var_encoder(q)) + 1e-4
--> 986 z, untran_z = self.reparameterize_transformation(qz_m, qz_v)
987
988 ql_gene = self.l_gene_encoder(data, *cat_list)
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/scvi/nn/_base_components.py in reparameterize_transformation(self, mu, var)
950
951 def reparameterize_transformation(self, mu, var):
--> 952 untran_z = Normal(mu, var.sqrt()).rsample()
953 z = self.z_transformation(untran_z)
954 return z, untran_z
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/distributions/normal.py in __init__ (self, loc, scale, validate_args)
48 else:
49 batch_shape = self.loc.size()
---> 50 super(Normal, self). __init__ (batch_shape, validate_args=validate_args)
51
52 def expand(self, batch_shape, _instance=None):
anaconda3/envs/scvi_tools_env/lib/python3.8/site-packages/torch/distributions/distribution.py in __init__ (self, batch_shape, event_shape, validate_args)
51 continue # skip checking lazily-constructed args
52 if not constraint.check(getattr(self, param)).all():
---> 53 raise ValueError("The parameter {} has invalid values".format(param))
54 super(Distribution, self). __init__ ()
55
ValueError: The parameter loc has invalid values
```
This is the AnnData setup summary:
```
Anndata setup with scvi-tools version 0.9.0.
Data Summary
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ Data ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ Cells │ 35523 │
│ Vars │ 10620 │
│ Labels │ 1 │
│ Batches │ 6 │
│ Proteins │ 3 │
│ Extra Categorical Covariates │ 0 │
│ Extra Continuous Covariates │ 0 │
└──────────────────────────────┴───────┘
SCVI Data Registry
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Data ┃ scvi-tools Location ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ X │ adata.layers['counts'] │
│ batch_indices │ adata.obs['_scvi_batch'] │
│ local_l_mean │ adata.obs['_scvi_local_l_mean'] │
│ local_l_var │ adata.obs['_scvi_local_l_var'] │
│ labels │ adata.obs['_scvi_labels'] │
│ protein_expression │ adata.obsm['protein_expression'] │
└────────────────────┴──────────────────────────────────┘
Label Categories
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
│ adata.obs['_scvi_labels'] │ 0 │ 0 │
└───────────────────────────┴────────────┴─────────────────────┘
Batch Categories
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
│ adata.obs['batch'] │ PB_baseline │ 0 │
│ │ PB_primary_CMV │ 1 │
│ │ PB_steady_state │ 2 │
│ │ LN_steady_state │ 3 │
│ │ PB_CMV_rechallenge │ 4 │
│ │ LN_CMV_rechallenge │ 5 │
└────────────────────┴────────────────────┴─────────────────────┘
```
Any recommendations on what I might try? Thank in advance!
---
### Author:  [@adamgayoso](https://discourse.scverse.org/u/adamgayoso)
#### Post date: [March 12, 2021, 8:51pm UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/2 "2021-03-12T20:51:03Z")
It’s hard to tell what might be the problem just from this traceback. But you might try two things:
1. See if you can run your model with just scVI (ignore protein data for now) `scvi.model.SCVI`
2. Turn down the learning rate of totalVI in the [train method](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.TOTALVI.train.html#scvi.model.TOTALVI.train) to e.g., 2e-3.
And just to double check, the protein data is count data yes?
---
### Author:  [@Stefan](https://discourse.scverse.org/u/Stefan)
#### Post date: [March 12, 2021, 11:06pm UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/3 "2021-03-12T23:06:10Z")
I should have stated this: scVI and scANVI continue to work just fine as does the totalVI example in the scvi-tools tutorial. All attempts to run totalVI on my data, including turning down the learning rate, have been unsuccessful. Interestingly it completes a variable number of epochs - even without changing the learning rate - before raising this exception.
I confirm I am indeed trying to run totalVI on count data for both mRNA and protein.
I realise how incredibly annoying it is to share and develop nice code and then get asked support-type questions. I’m really just looking for your gut reaction: is it something with my data and should I focus my attention on finding some kind of problem there … or is it more worthwhile for me to try to step through your very nice code with ipdb or spyder?
Thanks again for the benefit of your advice.
---
### Author:  [@adamgayoso](https://discourse.scverse.org/u/adamgayoso)
#### Post date: [March 12, 2021, 11:28pm UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/4 "2021-03-12T23:28:50Z")
It’s either the data or some hyperparam; though I’ve never seen this sort of error before on the mean of the latent space.
Does scVI run successfully on your data? If it does, I could quickly try to run totalVI if you are open to providing the data.
If scVI does not run successfully – are there any cells with all 0 counts?
---
### Author:  [@adamgayoso](https://discourse.scverse.org/u/adamgayoso)
#### Post date: [March 13, 2021, 4:16am UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/5 "2021-03-13T04:16:05Z")
Thanks for sending the data, upon some inspection you should do the following:
```python
x_model = scvi.model.TOTALVI(adata, empirical_protein_background_prior=False, n_layers_decoder=2)
```
Basically I think since you have only 3 proteins, the new [empirically learned prior initialization](https://docs.scvi-tools.org/en/stable/release_notes/v0.8.0.html#empirical-initialization-of-protein-background-parameters-with-totalvi) is getting thrown off and having bad values. This was something we added after the fact to try to better initialize the parameters that represent the protein background.
I can put a warning message if there are fewer than 10 proteins to alert users of this potential issue.
---
### Author:  [@Stefan](https://discourse.scverse.org/u/Stefan)
#### Post date: [March 13, 2021, 11:36am UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/6 "2021-03-13T11:36:42Z")
Thank so much!! Everything appears to work fine now. The data come from a species (and a cell type within that species) for which there aren’t many cross-reactive CITE-seq antibodies. Even those three antibodies help us quite a bit and thanks to totalVI we are able to overcome some of those limitations by mapping into references of other species with more CITE-seq antibodies.
Do you think it might make sense to have the _default_ value of empirical\_protein\_background\_prior to be (the boolean value of) n\_proteins \< 10?
Thanks again for sharing your awesome tool with this community.
---
### Author:  [@adamgayoso](https://discourse.scverse.org/u/adamgayoso)
#### Post date: [March 25, 2021, 1:17am UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/7 "2021-03-25T01:17:25Z")
Yes we can definitely do this. Would you be willing to make an issue on GitHub and link to this discussion?
---
### Author:  [@bradmacer](https://discourse.scverse.org/u/bradmacer)
#### Post date: [August 3, 2022, 8:32pm UTC](https://discourse.scverse.org/t/totalvi-nan-loss-with-few-proteins/45/9 "2022-08-03T20:32:36Z")
> [@Stefan](#):
>
> ```auto
> SettingWithCopyWarning:
> A value is trying to be set on a copy of a slice from a DataFrame.
>
> ```
The first thing you should understand is that SettingWithCopyWarning is a warning, and not an error. The real problem behind the warning is that it is generally difficult to predict whether a view or a copy is returned. In most cases, the warning was raised because you have chained two indexing operations together. The [SettingWithCopyWarning](http://net-informations.com/ds/err/copy.htm) was created to flag “chained assignment” operations. This is made easier to spot because you might be used `[] (square brackets)` twice, but the same would be true if you used other access methods such as `.loc[] , .iloc[]` and so on.
Moreover, you can change the behaviour of SettingWithCopyWarning warning using pd.options.mode.chained\_assignment with three option “None/raise”/“warn”.