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View Code? Open in Web Editor NEWA tool for semi-automatic cell type classification
Home Page: https://www.celltypist.org/
License: MIT License
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sudolin dhtc jiahuaqu gonv chbeltz cfinstil crsky1023 breezyzhao huaichao2018 mrpander ustcjyh shicheng-guo drsei zhaojk inkuu2998 yqyuhao bio-mingchen sfu-ireceptor lmc0705 jondeen jing-xinxing tangss66 stephenturner ewowiredu schaudge mduan-gt joadams1 hyexployer manveerchauhan tchen-tt canergen harel-coffee milescsmith astorfi aifimmunology intron7celltypist's Issues
Can't run cell typist with a my own model
Hello : )
Im trying to run cell typist with my own model and in the step "Training data using SGD logistic regression" I get the following error:
OMP: Error #13: Assertion failure at kmp_runtime.cpp(3689).
OMP: Hint Please submit a bug report with this message, compile and run commands used, and machine configuration info including native compiler and operating system versions. Faster response will be obtained by including all program sources. For information on submitting this issue, please see https://bugs.llvm.org/.
/Users/amartinezl/opt/anaconda3/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 2 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
zsh: abort python 2.5_RunCellTypist_OwnModel.py
Anyone has faced this error before?
Thanks!
Are the confidence scores of celltype predicted by different reference models comparable？
Dear Celltypist Team,
Are the confidence scores of celltype predicted by different reference models comparable? whether I could choose the most suitable model to annotate new datasets by comparing the average confidence scores of each cluster annotated by different models? in addition, If a cell is annotated as “unassigned”or “heterogeneous”, what does its confidence score mean？
Adult_Mouse_Gut cell types
Hello! Might the team have the publications where the Adult_Mouse_Gut model derived its cell types? It says 'TBD' in the celltypist site.
Binary cell type decisions lead to np.AxisError in predict_labels_and_prob
When trying to make a binary cell type decision using CellTypist, I encountered an AxisError in line 131 of models.py, caused by scores.argmax(axis=1). When making binary decisions, sklearn's LogsticRegression.decision_function yields not an ndarray of shape (n_samples, n_classes) but of (n_samples,), making axis=1 and argmax in general problematic.
My suggested solution is first checking the number of dimensions in the output of decision_function and if it is equal to 1, as with binary decisions, emulating the output format expected by CellTypist, conserving the difference in confidence scores between the two available classes for every cell. This is implemented in #18.
raw training dataset
is it possible to obtain the raw training dataset?
Train Custom Model on scATAC-seq data?
Hello,
Is it at all possible to train a custom celltypist model on scATAC-seq data and then use it for predicting cell types in scATAC-seq datasets? I've been trying accomplish this, but so far it seems incompatible, despite my efforts.
Wording on message
This is a minor point, but the current code logs this:
logger.info("👀 Can not detect a neighborhood graph, construct one before the over-clustering")
when the input data lacks a neighborhood graph. It seems like celltypist automatically creates one in this situation. I would suggest re-phrasing that message more like:
logger.info("👀 Can not detect a neighborhood graph, will construct one before the over-clustering")
to make it clear celltypist is fixing this. the phrasing "construct one before the over-clustering" sounds more like the tool is asking the user to construct one before running celltypist.
predictions = celltypist.annotate "ValueError:"
Hi Teichlab,
This is a really excellent tool and I love to use it. I am able to manage to run the tutorial but when I replace it with my own dataset I am strat to get errors .
adata_2000.X.expm1().sum(axis = 1)
matrix([[1.39864217e+141],
[4.99632738e+074],
[1.12685234e+037],
...,
[4.65627696e+256],
[1.14145687e+070],
[3.34092341e+191]])
adata_2000_raw = adata_2000.copy()
sc.pp.normalize_total(adata_2000_raw)
sc.pp.log1p(adata_2000_raw)
adata_2000.raw = adata_2000_raw
# Not run; predict cell identities using this loaded model.
predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)
# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).
#predictions = celltypist.annotate(adata_2000, model = 'Immune_All_High.pkl', majority_voting = True)
👀 Invalid expression matrix in `.X`, expect log1p normalized expression to 10000 counts per cell; will try the `.raw` attribute
⚠️ Warning: invalid expression matrix, expect all genes and log1p normalized expression to 10000 counts per cell. The prediction result may not be accurate
🔬 Input data has 122530 cells and 24910 genes
🔗 Matching reference genes in the model
🧬 5900 features used for prediction
⚖️ Scaling input data
🖋️ Predicting labels
✅ Prediction done!
👀 Can not detect a neighborhood graph, will construct one before the over-clustering
Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?f160eb2e-abec-4641-aa29-501be9d31a2d)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [35], line 2
1 # Not run; predict cell identities using this loaded model.
----> 2 predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)
File ~/opt/miniconda3/envs/SCVI/lib/python3.8/site-packages/celltypist/annotate.py:89, in annotate(filename, model, transpose_input, gene_file, cell_file, mode, p_thres, majority_voting, over_clustering, min_prop)
87 #over clustering
88 if over_clustering is None:
---> 89 over_clustering = clf.over_cluster()
90 predictions.adata = clf.adata
91 elif isinstance(over_clustering, str):
File ~/opt/miniconda3/envs/SCVI/lib/python3.8/site-packages/celltypist/classifier.py:418, in Classifier.over_cluster(self, resolution)
416 logger.info("👀 Can not detect a neighborhood graph, will construct one before the over-clustering")
417 adata = self.adata.copy()
--> 418 self.adata.obsm['X_pca'], self.adata.obsp['connectivities'], self.adata.obsp['distances'], self.adata.uns['neighbors'] = Classifier._construct_neighbor_graph(adata)
419 else:
420 logger.info("👀 Detected a neighborhood graph in the input object, will run over-clustering on the basis of it")
File ~/opt/miniconda3/envs/SCVI/lib/python3.8/site-packages/celltypist/classifier.py:393, in Classifier._construct_neighbor_graph(adata)
391 if 'highly_variable' not in adata.var:
392 sc.pp.filter_genes(adata, min_cells=5)
--> 393 sc.pp.highly_variable_genes(adata, n_top_genes = min([2500, adata.n_vars]))
394 adata = adata[:, adata.var.highly_variable]
...
265 )
266 elif mn == mx: # adjust end points before binning
267 mn -= 0.001 * abs(mn) if mn != 0 else 0.001
ValueError: cannot specify integer `bins` when input data contains infinity
Thank you
predictions.to_adata() error
Dear developer,
This is really fantastic package for cell annotation, well, when I run the predictions.to_adata(), it returns ValueError: cannot reindex on an axis with duplicate labels
Can you help me with it?
Thanks a lot!!!
Use "python -m" to run celltypist?
Hello,
We're interested in running celltypist from R/reticulate. While I understand that celltypist makes a standalone executable, can something like the following work on the command line? For various reasons this is preferred since code would not need ot know the location of the celltypist executable file itself:
python.exe -m celltypist.command_line --update_models
The above does not error, but it also doesnt seem to do anything either (no console output produced). Thanks.
Guidance for using Nanostring CosMx RNA input
Hello, I appreciate your work in making CellTypist available. I have been able to use the python API to assign predicted_labels and majority_voting types to our data but am getting the warning message below while running celltypist.annotate:
The CosMx data contains counts for 960 genes and 20 negative probes. It is sparse data with an average of 250 unique genes per cell. I have processed these by normalizing each cell to have a target of 10,000 counts then computing their log1p values. The input file is attached. I have also tried using an annData object as input but this throws an error instead of just a warning.
Can you comment on whether this data is a good match for CellTypist and if I have it in the best or correct format?
-Mark Dane
CT_sample_file_values.csv
Training new model gives ValueError
Hi,
I'm trying to train my own model and keep getting this error:
🍳 Preparing data before training
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [37], line 1
----> 1 coarse_model = celltypist.train(GAnn, labels = 'annot_coars', n_jobs = 10, feature_selection = True)
File ~\anaconda3\envs\GDT2\lib\site-packages\celltypist\train.py:293, in train(X, labels, genes, transpose_input, with_mean, check_expression, C, solver, max_iter, n_jobs, use_SGD, alpha, mini_batch, batch_number, batch_size, epochs, balance_cell_type, feature_selection, top_genes, date, details, url, source, version, **kwargs)
291 #check
292 if check_expression and (np.abs(np.expm1(indata[0]).sum()-10000) > 1):
--> 293 raise ValueError(
294 "🛑 Invalid expression matrix, expect log1p normalized expression to 10000 counts per cell")
295 if len(labels) != indata.shape[0]:
296 raise ValueError(
297 f"🛑 Length of training labels ({len(labels)}) does not match the number of input cells ({indata.shape[0]})")
ValueError: 🛑 Invalid expression matrix, expect log1p normalized expression to 10000 counts per cell
my anndata is simple raw counts and obs metadata:
AnnData object with n_obs × n_vars = 185894 × 31053
obs: 'annot_coars', 'annot_fine'
I tried also running sc.pp.log1p() prior to the train function (though that should be done under the hood, no?) but nothing changes.
training on the demo adata_2000 works just fine.
Thanks!
Celltypist showing invalid expression matrix
I tried running celltypist on the anndata object and it wqs giving me this error. The data is from a research paper : https://github.com/scCOVID-19/COVIDPBMC/
Error:
Exception: Invalid expression matrix, expect log1p normalized expression to 10000 counts per cell
Can you please help me out with this?
Support custom .celltypist dir?
Hello,
By default, celltypist makes a folder in the user's home dir to cache data. It would be convenient if celltypist supported either an environment variable or argument to directly specify this path. Our scenario is that we're running in docker on a cluster as a non-root user and the default home is non-writable. I'm fixing this, but having flexibility over the save location would still be a nice feature.
Thanks,
Ben
Predition error
When I ran the prediction porcedure below, there was an error message.
predictions = celltypist.annotate(adata, model = 'Immune_All_Low.pkl', majority_voting = True)
ValueError: 🛑 Invalid expression matrix, expect log1p normalized expression to 10000 counts per cell
I then tried to normalized the adata, it showed:
adata.raw = sc.pp.log1p(adata, copy=True)
WARNING: adata.X seems to be already log-transformed.
Is there any way to fix it?
Bioconda recipe
Would it be possible to add celltypst to bioconda? I'm happy to help create a recipe.
sequencing protocol requirements?
This is more of a question than an issue, but do the trained models only work on samples sequenced on a 10x platform, or can we expect it to also work well for smart-seq2?
If we are not using 10x data, can we simply build a new model, or are there some assumptions in the model that would lead to performance differences?
Using integrated data
Hi,
Thank you for providing the tool!
I integrate the data with harmony which 'corrects' the PCA embeddings as a means of batch correction.
This corrected PCA is then used for downstream clustering.
If I were to use an integrated batch corrected data, would you suggest to save 'harmony-corrected pca' in the 'X_pca' slot of the adata object, since I see that in some modes the classifier uses PCA embeddings?
Kindly advise,
Thanks and Kind regards,
Deterministic results?
Hello,
This is admittedly a picky question. We're experimenting with running celltypist to score cells, from the command line. In our test data we have a rare category with ~4 cells. These are consistently scored as Tcm/Naive cytotoxic T cells in 'predicted labels'. However, the result of majority_voting is not deterministic. Some of the time these 4 cells lump into another category. This by itself it not a huge problem (i.e. in reality they are probably ambiguous cells and it's 4 total). My question is about the inconsistency run-to-run. The input we give celltypist does not have a neighborhood graph, etc., and celltypist creates it for us. Are there any instance where we can or should be setting a random seed or something like this? Thanks
0 features used for prediction
We are running celltypist and ran into a fringe case. I think the error is somewhere upstream in our data preparation, but running celltypist gives this:
⏳ Loading data
🔬 Input data has 2700 cells and 13714 genes
🔗 Matching reference genes in the model
🧬 0 features used for prediction
⚖️ Scaling input data
and then it dies with the stacktrace below. Obviously if no features match, celltypist cant run. I'm reporting this since it might be nice if the celltypist code that finds matching features would die more immediately and in a more informative way if zero features match (or perhaps fewer than some configurable threshold).
I'm confused about this particular case since the failure is from our automated tests, and the input data is the pbmc3k dataset from Seurat, downloaded using the SeuratData R package. This test runs fine on R/release, but not R/develop. In any event, i dont think the failure itself is celltypist's issue.
Traceback (most recent call last):
File "/home/runner/.local/bin/celltypist", line 8, in <module>
sys.exit(main())
File "/home/runner/.local/lib/python3.8/site-packages/click/core.py", line 1128, in __call__
return self.main(*args, **kwargs)
File "/home/runner/.local/lib/python3.8/site-packages/click/core.py", line 1053, in main
rv = self.invoke(ctx)
File "/home/runner/.local/lib/python3.8/site-packages/click/core.py", line 1395, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/runner/.local/lib/python3.8/site-packages/click/core.py", line 754, in invoke
return __callback(*args, **kwargs)
File "/home/runner/.local/lib/python3.8/site-packages/celltypist/command_line.py", line 109, in main
result = annotate(
File "/home/runner/.local/lib/python3.8/site-packages/celltypist/annotate.py", line 81, in annotate
predictions = clf.celltype(mode = mode, p_thres = p_thres)
File "/home/runner/.local/lib/python3.8/site-packages/celltypist/classifier.py", line 351, in celltype
decision_mat, prob_mat, lab = self.model.predict_labels_and_prob(self.indata, mode = mode, p_thres = p_thres)
File "/home/runner/.local/lib/python3.8/site-packages/celltypist/models.py", line 120, in predict_labels_and_prob
scores = self.classifier.decision_function(indata)
File "/home/runner/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py", line 407, in decision_function
X = self._validate_data(X, accept_sparse="csr", reset=False)
File "/home/runner/.local/lib/python3.8/site-packages/sklearn/base.py", line 566, in _validate_data
X = check_array(X, **check_params)
File "/home/runner/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 814, in check_array
raise ValueError(
ValueError: Found array with 0 feature(s) (shape=(2700, 0)) while a minimum of 1 is required.
'Naive' cell type in 'Adult_Mouse_Gut' model
What is the meaning of the 'Naive' cell type in 'Adult_Mouse_Gut' model? seems like 'Naive B-cells' ..
Only 9 features were used for prediction
Hello. Thank you for developing this wonderful tool.
I am trying to apply CellTypist to my dataset composed of mouse heart cells.
Besides the immune cells, I have preliminarily annotated the endothelial cells and fibroblast cells in this dataset too.
I am not familiar with Scanpy so the AnnData file was converted from the Seurat Object following the instructions by SeuratDisk.
https://mojaveazure.github.io/seurat-disk/articles/convert-anndata.html
The raw count matrix was scaled to 10,000 and log normalized by the NormalizeData funtion using Seurat.
cre <- NormalizeData(cre, normalization.method = "LogNormalize", scale.factor = 10000)
However, when I tried predicting the annotation of this converted AnnData file, the result told me that almost all the predicted_labels are "Double-positive thymocytes" in my data, which was not possible at all.
Then I checked for the reason and I found that only 9 features were used for prediction.
Here is what CellTypist output.
# Predict the identity of each input cell.
cre_predictions = celltypist.annotate( cre, model = 'Immune_All_Low.pkl', majority_voting = True)
🔬 Input data has 9267 cells and 20011 genes
🔗 Matching reference genes in the model
🧬 9 features used for prediction
⚖️ Scaling input data
🖋️ Predicting labels
✅ Prediction done!
👀 Can not detect a neighborhood graph, will construct one before the over-clustering
No error was reported.
Was it caused by the conversion from Seurat to AnnData? Would it be better if I turned to use Scanpy from the beginning?
Can we extract the performance metrics from a customized model?
Thanks for this awesome tool! We are using it with our own data to create a customized model. My question is how we can obtain the model performance info from the customized model? Is there any utility function in celltypist we can use or we have to write our own code to evaluate how well our model performs? Thanks!
How to update the model
Dear Celltypist Team,
If I have a new annotated dataset, how can I train and update it into the existing model ？
Looking forward to your reply！
T compartment without exhausted T cells?
Congratulations on this outstanding job! There is a little doubt in my mind that in your dataset (https://www.science.org/doi/10.1126/science.abl5197) your team detected 101 immune populations without any exhausted T cells. I wonder whether it is needed?
Add a ``celltypist.download_model(force_update=True)`` API
celltypist has an API download_models(force_update: bool=False) to download all models with the latest version. We don't want this for all models, only a handful. Could you provide an API for a single model? Something like celltypist.download_model(force_update=True) If there is already a way to do this with the current API, please let me know.
Thanks!
undetermined cells
Hi
Im trying to run celltypist on several different datasets (PBMCs, Spleenocytes etc) in almost all cases im getting more than 30% of cells being called as undetermined. I tried using some high quality public datasets as well but end up with same situation. Im using the low resolution Immune cell model. Can you help me understand what I can do to trouble shoot ?
Genes driving the prediction
Hello, as usual thank you so much for all your help with my questions. I am wondering if there is a way to know which genes are driving a specific similarity with a model? Can I get that information from the results?
Many thanks,
Carmen
`celltypist.annotate` to specify the required scale for count normalization (currently 10'000)
It appears that counts needs to be normalized with a scale of 10'000 when calling celltypist.annotate. This is not clear from the documentation of that function. However one can figure it out by trial and error or by code inspection (for instance from the following code in classifier.py:
if np.abs(np.expm1(self.indata[0]).sum()-10000) > 1: raise ValueError("🛑 Invalid expression matrix, expect log1p normalized expression to 10000 counts per cell")).
This issue is a suggestion to explicitly call that out in the documentation of the annotate method.
Thanks!
Getting error when running predictions
predictions = celltypist.annotate(adata, model = 'Immune_All_Low.pkl', majority_voting = True)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
I check adata.X and I don't have NaN values, also I shorten the values. Not sure what else to try.
CellTypist with batch correction
Hi together,
great tool you have created with CellTypist!
Since I work with mixed scRNA-Seq data from multiple patients, I need to do a batch effects correction before the cell type annotation.
Unfortunately, I don't know of any method that corrects batch effects while leaving the data in a count matrix.
There will always be negative data that does not correspond to a count matrix anymore.
Is it still possible to apply CellTypist after a batch effects correction, although the data is not in 1e4 and log1p format?
Any other ideas to find a workaround for that problem?
Thanks a lot!
The tutorial notebook didn't quite work ModuleNotFoundError: No module named 'celltypist'
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/tmp/ipykernel_7855/813241117.py in <module>
----> 1 import celltypist
2 from celltypist import models
ModuleNotFoundError: No module named 'celltypist'
Cell Encyclopedia Link Broken
I was looking for the encyclopedia table and the link (https://github.com/Teichlab/celltypist_wiki/tree/main/atlases/Immune/v2/encyclopedia) from the CellTypist website was broken. Is that no longer posted?
Thank you!
Failed to install with conda
I've tried installing the package with conda on both Ubuntu and macOS, but it fails.
What I've done:
conda create -n celltypist python=3.8
conda install -c bioconda celltypist
Ubuntu
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: \
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versionsThe following specifications were found to be incompatible with your system:
- feature:/linux-64::__glibc==2.31=0
- feature:|@/linux-64::__glibc==2.31=0
Your installed version is: 2.31
macOS
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: |
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versions
I've tried with Python 3.9 and 3.7 as well with the same outcome.
Would appreciate any hints!
Best
error while plotting predictions: cannot find keys
Hi,
I am trying to plot the predictions as a dotplot. It works for one dataset but does not seem to work for another. I have checked to make sure the respective columns exist in predictions.predicted_labels.
celltypist.dotplot(predictions, use_as_reference='seurat_clusters', use_as_prediction='majority_voting')
Error:
KeyError Traceback (most recent call last)
----> 7 celltypist.dotplot(predictions, use_as_reference='seurat_clusters', use_as_prediction="majority_voting")
.../scanpy_py37/lib/python3.7/site-packages/celltypist/plot.py in dotplot(predictions, use_as_reference, use_as_prediction, prediction_order, reference_order, filter_prediction, cmap, vmin, vmax, colorbar_title, dot_min, dot_max, smallest_dot, size_title, swap_axes, title, figsize, show, save, ax, return_fig, **kwds)
156 _adata.obs['_pred'] = dot_size_df.index
157 #DotPlot
--> 158 dp = sc.pl.DotPlot(_adata, dot_size_df.columns, '_pred', title = title, figsize = figsize, dot_color_df = dot_color_df, dot_size_df = dot_size_df, ax = ax, vmin = vmin, vmax = vmax, **kwds)
159 if swap_axes:
160 dp.swap_axes()
.../scanpy_py37/lib/python3.7/site-packages/scanpy/plotting/_dotplot.py in __init__(self, adata, var_names, groupby, use_raw, log, num_categories, categories_order, title, figsize, gene_symbols, var_group_positions, var_group_labels, var_group_rotation, layer, expression_cutoff, mean_only_expressed, standard_scale, dot_color_df, dot_size_df, ax, vmin, vmax, vcenter, norm, **kwds)
151 vcenter=vcenter,
152 norm=norm,
--> 153 **kwds,
154 )
155
.../scanpy_py37/lib/python3.7/site-packages/scanpy/plotting/_baseplot_class.py in __init__(self, adata, var_names, groupby, use_raw, log, num_categories, categories_order, title, figsize, gene_symbols, var_group_positions, var_group_labels, var_group_rotation, layer, ax, vmin, vmax, vcenter, norm, **kwds)
117 num_categories,
118 layer=layer,
--> 119 gene_symbols=gene_symbols,
120 )
121 if len(self.categories) > self.MAX_NUM_CATEGORIES:
.../scanpy_py37/lib/python3.7/site-packages/scanpy/plotting/_anndata.py in _prepare_dataframe(adata, var_names, groupby, use_raw, log, num_categories, layer, gene_symbols)
1918 keys = list(groupby) + list(np.unique(var_names))
1919 obs_tidy = get.obs_df(
-> 1920 adata, keys=keys, layer=layer, use_raw=use_raw, gene_symbols=gene_symbols
1921 )
1922 assert np.all(np.array(keys) == np.array(obs_tidy.columns))
.../scanpy_py37/lib/python3.7/site-packages/scanpy/get/get.py in obs_df(adata, keys, obsm_keys, layer, gene_symbols, use_raw)
276 keys,
277 alias_index=alias_index,
--> 278 use_raw=use_raw,
279 )
280
.../scanpy_py37/lib/python3.7/site-packages/scanpy/get/get.py in _check_indices(dim_df, alt_index, dim, keys, alias_index, use_raw)
166 if len(not_found) > 0:
167 raise KeyError(
--> 168 f"Could not find keys '{not_found}' in columns of `adata.{dim}` or in"
169 f" {alt_repr}.{alt_search_repr}."
170 )
KeyError: "Could not find keys '['0', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '5', '6', '7', '8', '9']' in columns of `adata.obs` or in adata.var_names."
Any help would be great.
Thanks
Saurabh
conda version is out of date.
Celltypist in conda is out of date (2023-2-9), methods like extract_top_markers is not in the conda version. Thanks for the nice work.
Subsetting celltypist model by cell type
Hi, thanks a lot for developing this tool!
May I ask if there's a way to subset the model by cell types? The scenario is the model contains some cell types that I know is absent in the tissue I'm working on, so I want to prevent it from assigning these cell types and focus only on a subset of cell types.
(I'm working on bone marrow samples, while the model like 'Immune_All_Low.pkl' is very close but also include some cell types which are usually not seen in bone marrow).
Would it be feasible and reasonable to do this? Thanks a lot for your help!
Best regards,
Marcus
Can I use log2(TPM) normalized data as input instead log1p w/ 10K scaling factor?
Hi,
Thank you for developing and maintaining CellTypist.
It is a great tool and makes my life much easier.
I'm analyzing log2(TPM) normalized data from a publicly available data set and I was wondering if I could provide this to CellTypist or is there any assumption violated by this?
I know that the software requires log1p (w/ 10K scaling factor) normalized data as input, but for this particularly data set, I don't have access to the count data to normalize it myself and I still would like to run CellTypist.
I know that CellTypist gives an error and exits when such data is not provided, but if I comment that line of code, will the CellTypist assumptions still be valid?
Thanks in advance for any help or advice.
Best regards,
António
Filtering labels in celltypist.dotplot
Hello CellTypist team,
It would be great to have an option to specify a subset of labels to plot in celltypist.dotplot. E.g. when predicting from a model with > 100 cell types and using majority_voting=False, often certain labels are assigned to just a few cells, so I get a huge dotplot, but I am really interested in just a few of those labels.
In scanpy.pl.dotplot I would do this by simply filtering the anndata object, but I don't seem to be able to manipulate predictions.adata in this case.
RAM usage by converting from sparse to dense
Is there a reason why the input data is converted to an np.array rather than accepting sparse matrices when running .train? Skimming the remainder of the code, I cannot seem to find anything that would not also work with sparse matrices. The reason I am asking is that this conversion to an array seems to be the reasons I find myself running out of RAM quite frequently when working with larger datasets.
Thanks
TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray?
Hello,
We've been running cell typist pretty regularly without issues, but recently saw this. I dont know yet whether this is a quirk in the input data or not, but I thought I'd report. We are running a basic celltypist command with a built-in model. The input is an AnnData file created by writing an R SeuratObject to disk.
This stack makes me wonder if some dependency updated and changed validation, like sklearn, but I havent debugged it yet.
Have you seen anything like this before? Thanks in advance.
09 Dec 2022 08:15:11,830 DEBUG: Traceback (most recent call last):
09 Dec 2022 08:15:11,834 DEBUG: File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
09 Dec 2022 08:15:11,838 DEBUG: return _run_code(code, main_globals, None,
09 Dec 2022 08:15:11,843 DEBUG: File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
09 Dec 2022 08:15:11,848 DEBUG: exec(code, run_globals)
09 Dec 2022 08:15:11,852 DEBUG: File "/usr/local/lib/python3.8/dist-packages/celltypist/command_line.py", line 129, in <module>
09 Dec 2022 08:15:11,857 DEBUG: main()
09 Dec 2022 08:15:11,862 DEBUG: File "/usr/local/lib/python3.8/dist-packages/click/core.py", line 1130, in __call__
09 Dec 2022 08:15:11,866 DEBUG: return self.main(*args, **kwargs)
09 Dec 2022 08:15:11,872 DEBUG: File "/usr/local/lib/python3.8/dist-packages/click/core.py", line 1055, in main
09 Dec 2022 08:15:11,877 DEBUG: rv = self.invoke(ctx)
09 Dec 2022 08:15:11,881 DEBUG: File "/usr/local/lib/python3.8/dist-packages/click/core.py", line 1404, in invoke
09 Dec 2022 08:15:11,885 DEBUG: return ctx.invoke(self.callback, **ctx.params)
09 Dec 2022 08:15:11,889 DEBUG: File "/usr/local/lib/python3.8/dist-packages/click/core.py", line 760, in invoke
09 Dec 2022 08:15:11,895 DEBUG: return __callback(*args, **kwargs)
09 Dec 2022 08:15:11,900 DEBUG: File "/usr/local/lib/python3.8/dist-packages/celltypist/command_line.py", line 109, in main
09 Dec 2022 08:15:11,904 DEBUG: result = annotate(
09 Dec 2022 08:15:11,910 DEBUG: File "/usr/local/lib/python3.8/dist-packages/celltypist/annotate.py", line 81, in annotate
09 Dec 2022 08:15:11,915 DEBUG: predictions = clf.celltype(mode = mode, p_thres = p_thres)
09 Dec 2022 08:15:11,919 DEBUG: File "/usr/local/lib/python3.8/dist-packages/celltypist/classifier.py", line 376, in celltype
09 Dec 2022 08:15:11,924 DEBUG: decision_mat, prob_mat, lab = self.model.predict_labels_and_prob(self.indata, mode = mode, p_thres = p_thres)
09 Dec 2022 08:15:11,937 DEBUG: File "/usr/local/lib/python3.8/dist-packages/celltypist/models.py", line 145, in predict_labels_and_prob
09 Dec 2022 08:15:11,945 DEBUG: scores = self.classifier.decision_function(indata)
09 Dec 2022 08:15:11,951 DEBUG: File "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_base.py", line 401, in decision_function
09 Dec 2022 08:15:11,957 DEBUG: X = self._validate_data(X, accept_sparse="csr", reset=False)
09 Dec 2022 08:15:11,965 DEBUG: File "/usr/local/lib/python3.8/dist-packages/sklearn/base.py", line 535, in _validate_data
09 Dec 2022 08:15:11,971 DEBUG: X = check_array(X, input_name="X", **check_params)
09 Dec 2022 08:15:11,977 DEBUG: File "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py", line 737, in check_array
09 Dec 2022 08:15:11,983 DEBUG: raise TypeError(
09 Dec 2022 08:15:11,993 DEBUG: TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
09 Dec 2022 08:15:12,094 DEBUG: Quitting from lines 182-193 (16-2-GEX.df.appendHashing.frc.cite.norm.pca.dr.RunCelltypist.rmd)
Adding a version argument to download_models
It would be great to have a version argument to download_models. This will allow writing and sharing code for reproducible analysis. (Currently, a newer model version will likely change the results of my analysis. While force_update is sufficient locally, it is not sufficient if I wish to share my code).
Gene names VS gene IDs in precomputed models
Hello celltypers,
While using a trained celltypist model on my data, I got this error. It took me a little while to realise it was coming from having mismatched feature names: my adata.var_names are EnsemblIDs while the model uses gene names.
predictions = celltypist.annotate(adata, model = 'Immune_All_Low.pkl', majority_voting = True)
🔬 Input data has 634000 cells and 5000 genes
🔗 Matching reference genes in the model
🧬 0 features used for prediction
⚖️ Scaling input data
🖋️ Predicting labels
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-248-0b4cb11719f9> in <module>
----> 1 predictions = celltypist.annotate(adata, model = 'Immune_All_Low.pkl', majority_voting = True)
~/my-conda-envs/emma_env/lib/python3.7/site-packages/celltypist/annotate.py in annotate(filename, model, transpose_input, gene_file, cell_file, mode, p_thres, majority_voting, over_clustering, min_prop)
79 clf = classifier.Classifier(filename = filename, model = lr_classifier, transpose = transpose_input, gene_file = gene_file, cell_file = cell_file)
80 #predict
---> 81 predictions = clf.celltype(mode = mode, p_thres = p_thres)
82 if not majority_voting:
83 return predictions
~/my-conda-envs/emma_env/lib/python3.7/site-packages/celltypist/classifier.py in celltype(self, mode, p_thres)
349
350 logger.info("🖋️ Predicting labels")
--> 351 decision_mat, prob_mat, lab = self.model.predict_labels_and_prob(self.indata, mode = mode, p_thres = p_thres)
352 logger.info("✅ Prediction done!")
353
~/my-conda-envs/emma_env/lib/python3.7/site-packages/celltypist/models.py in predict_labels_and_prob(self, indata, mode, p_thres)
118 A tuple of decision score matrix, raw probability matrix, and predicted cell type labels.
119 """
--> 120 scores = self.classifier.decision_function(indata)
121 probs = expit(scores)
122 if mode == 'best match':
~/my-conda-envs/emma_env/lib/python3.7/site-packages/sklearn/linear_model/_base.py in decision_function(self, X)
280 check_is_fitted(self)
281
--> 282 X = check_array(X, accept_sparse='csr')
283
284 n_features = self.coef_.shape[1]
~/my-conda-envs/emma_env/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72 return f(**kwargs)
73 return inner_f
74
~/my-conda-envs/emma_env/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
659 " a minimum of %d is required%s."
660 % (n_features, array.shape, ensure_min_features,
--> 661 context))
662
663 if copy and np.may_share_memory(array, array_orig):
ValueError: Found array with 0 feature(s) (shape=(634000, 0)) while a minimum of 1 is required.This made me think of two suggestions:
- Could the error message for this case become a bit more informative? If the feature overlap is 0, then print a message saying "Are you using gene names is adata.var_names?" With the current message I first thought it was triggered by having all zeros in some row or column
- Using gene names while matching info between datasets can be problematic, because of name duplication or mismatches in different gene annotation databases. Would it be possible to also store unique geneIDs in the model objects (e.g. ensembl IDs) and give an option to select the type of feature names to use in
celltypist.annotate?
Details to be added about the available model
Dear,
In the description of the model it is not clear whether the model involves cells from both healthy and CD or just healthy? I am especially referring to 'Cells_Intestinal_Tract ' from intestinal cells from fetal, pediatric and adult human gut (134 cell types). The original study associated also has CD data. Hence the confusion.
Kindly clarify.
Getting error when running predictions follow tutorial
Thank you for providing such a useful tool, but when I run your the tutorial of Best practice in large-scale cross-dataset label transfer using CellTypist, why can't I read the locally saved model_from_Elmentaite_2021.pkl.
predictions = celltypist.annotate(adata_James, model = 'model_from_Elmentaite_2021.pkl' , majority_voting = True,mode = 'best match')
🔎 No available models. Downloading...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/annotate.py", line 77, in annotate
lr_classifier = model if isinstance(model, Model) else Model.load(model)
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/models.py", line 90, in load
if model in get_all_models():
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/models.py", line 359, in get_all_models
download_if_required()
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/models.py", line 372, in download_if_required
download_models()
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/models.py", line 432, in download_models
models_json = get_models_index(force_update)
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/site-packages/celltypist/models.py", line 394, in get_models_index
return json.load(f)
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/jdfssz1/USER/hw/2.software/mn3/envs/celltypist/lib/python3.9/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
Can celltypist handle doublets and low quality cells?
Should cells with low counts/ high-Mitochondria and doublets be discarded prior to entering the data to cell typist? or can one remove them thereafter?
Thanks!
model download - requests not working
Dear Celltypist Team,
great job. One small problem I encountered when using Celltypist in a Jupyter Notebook on our compute cluster (CentOS Linux Linux 7 (Core)) is that the model download is not working. I have tested this on google colab and it works perfectly fine. Specifically I am referring to this function:
models.download_models()
I noticed that this function as well as the download_model_index function it calls use requests. But this gets stalled but doesnt give an error/timeout when using it on our compute cluster. I find the same problem when trying to download the model manually through curl using os.system in the Jupyter notebook. I think the issue is that the worker node of the compute cluster doesnt run on the home directory where celltypist is stored.
def download_model_index(only_model: bool = True) -> None:
"""
Download the `models.json` file from the remote server.
Parameters
----------
only_model
If set to `False`, will also download the models in addition to the json file.
(Default: `True`)
"""
url = 'https://celltypist.cog.sanger.ac.uk/models/models.json'
logger.info(f"📜 Retrieving model list from server {url}")
with open(get_model_path("models.json"), "wb") as f:
f.write(requests.get(url).content)
model_count = len(requests.get(url).json()["models"])
logger.info(f"📚 Total models in list: {model_count}")
if not only_model:
download_models()
A workaround is getting the Celltypist directory with:
models.models_path
then downloading the model files manually using the urls listed here https://celltypist.cog.sanger.ac.uk/models/models.json .
Perhaps you have an idea how to solve this more elegantly/ you include an error message/timeout in the?:
f.write(requests.get(url).content)](url)
Maybe defining the model directory manually in models.download_models() would be an option?
This is my environment:
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 1_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
alabaster 0.7.12 py_0 conda-forge
anndata 0.8.0 py37h89c1867_0 conda-forge
anyio 3.5.0 py37h89c1867_0 conda-forge
argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py37h540881e_2 conda-forge
arpack 3.7.0 hdefa2d7_2 conda-forge
astor 0.8.1 pyh9f0ad1d_0 conda-forge
attrs 21.4.0 pyhd8ed1ab_0 conda-forge
autograd 1.3 py_0 conda-forge
autograd-gamma 0.5.0 pyh9f0ad1d_0 conda-forge
babel 2.9.1 pyh44b312d_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
backports.zoneinfo 0.2.1 py37h5e8e339_4 conda-forge
beautifulsoup4 4.10.0 pypi_0 pypi
binutils_impl_linux-64 2.36.1 h193b22a_2 conda-forge
binutils_linux-64 2.36 hf3e587d_10 conda-forge
biothings_client 0.2.6 pyh5e36f6f_0 bioconda
blas 1.1 openblas conda-forge
bleach 4.1.0 pypi_0 pypi
blosc 1.21.0 h9c3ff4c_0 conda-forge
brewer2mpl 1.4.1 pypi_0 pypi
brotli 1.0.9 h7f98852_6 conda-forge
brotli-bin 1.0.9 h7f98852_6 conda-forge
brotlipy 0.7.0 py37h540881e_1004 conda-forge
bwidget 1.9.14 ha770c72_1 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.5.18.1 ha878542_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cairo 1.16.0 ha12eb4b_1010 conda-forge
cellrank 1.0.0rc4 py_0 bioconda
celltypist 0.1.9 pyhdfd78af_0 bioconda
certifi 2021.10.8 pypi_0 pypi
cffi 1.15.0 py37h036bc23_0 conda-forge
charset-normalizer 2.0.12 pyhd8ed1ab_0 conda-forge
click 8.1.3 py37h89c1867_0 conda-forge
cloudpickle 2.1.0 pyhd8ed1ab_0 conda-forge
cmake 3.22.4 pypi_0 pypi
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
cryptography 36.0.0 py37h9ce1e76_0
cudatoolkit 10.1.243 h036e899_10 conda-forge
curl 7.82.0 h2283fc2_0 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cython 0.29.28 pypi_0 pypi
cytoolz 0.11.2 py37h540881e_2 conda-forge
dask-core 1.1.4 py37_1
debugpy 1.5.1 pypi_0 pypi
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
docrep 0.3.2 pyh44b312d_0 conda-forge
docutils 0.17.1 py37h89c1867_2 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
enum34 1.1.10 py37hc8dfbb8_2 conda-forge
et_xmlfile 1.0.1 py_1001 conda-forge
expat 2.4.8 h27087fc_0 conda-forge
fa2 0.3.5 pypi_0 pypi
fcsparser 0.2.4 pypi_0 pypi
flit-core 3.7.1 pyhd8ed1ab_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.0 h8e229c2_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.30.0 py37h540881e_0 conda-forge
formulaic 0.3.4 pyhd8ed1ab_0 conda-forge
freetype 2.10.4 h0708190_1 conda-forge
fribidi 1.0.10 h36c2ea0_0 conda-forge
future 0.18.2 py37h89c1867_5 conda-forge
future_fstrings 1.2.0 py37h89c1867_3 conda-forge
gcc_impl_linux-64 9.4.0 h03d3576_16 conda-forge
gcc_linux-64 9.4.0 h391b98a_10 conda-forge
gettext 0.19.8.1 h73d1719_1008 conda-forge
gfortran_impl_linux-64 9.4.0 h0003116_16 conda-forge
gfortran_linux-64 9.4.0 hf0ab688_10 conda-forge
giflib 5.2.1 h36c2ea0_2 conda-forge
glpk 4.65 h9202a9a_1004 conda-forge
gmp 6.2.1 h58526e2_0 conda-forge
graphite2 1.3.13 h58526e2_1001 conda-forge
gsl 2.7 he838d99_0 conda-forge
gxx_impl_linux-64 9.4.0 h03d3576_16 conda-forge
gxx_linux-64 9.4.0 h0316aca_10 conda-forge
h5py 3.6.0 nompi_py37hd308b1e_100 conda-forge
harfbuzz 3.4.0 hb4a5f5f_0 conda-forge
harmonyts 0.1.4 pypi_0 pypi
hdf5 1.12.1 nompi_h4df4325_104 conda-forge
icu 69.1 h9c3ff4c_0 conda-forge
idna 3.3 pyhd8ed1ab_0 conda-forge
igraph 0.9.7 hf5496dd_0 conda-forge
imagecodecs-lite 2019.12.3 py37hda87dfa_5 conda-forge
imageio 2.19.2 pyhcf75d05_0 conda-forge
imagesize 1.3.0 pyhd8ed1ab_0 conda-forge
importlib-metadata 4.11.3 py37h89c1867_0 conda-forge
importlib-resources 5.4.0 pypi_0 pypi
importlib_metadata 4.11.3 hd8ed1ab_0 conda-forge
importlib_resources 5.7.1 pyhd8ed1ab_0 conda-forge
iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge
intel-openmp 2022.0.1 h06a4308_3633
interface_meta 1.3.0 pyhd8ed1ab_0 conda-forge
ipykernel 6.9.2 pypi_0 pypi
ipython 7.32.0 py37h89c1867_0 conda-forge
ipython-genutils 0.2.0 pypi_0 pypi
ipython_genutils 0.2.0 py_1 conda-forge
jbig 2.1 h7f98852_2003 conda-forge
jedi 0.18.1 py37h89c1867_1 conda-forge
jinja2 3.0.3 pypi_0 pypi
joblib 1.1.0 pyhd8ed1ab_0 conda-forge
joypy 0.2.6 pypi_0 pypi
jpeg 9e h7f98852_0 conda-forge
json5 0.9.6 pypi_0 pypi
jsonpickle 2.1.0 pypi_0 pypi
jsonschema 4.4.0 pyhd8ed1ab_0 conda-forge
jupyter-client 7.1.2 pypi_0 pypi
jupyter-server 1.15.6 pypi_0 pypi
jupyter_client 7.3.0 pyhd8ed1ab_0 conda-forge
jupyter_core 4.9.2 py37h89c1867_0 conda-forge
jupyter_server 1.16.0 pyhd8ed1ab_1 conda-forge
jupyterlab 3.3.2 pypi_0 pypi
jupyterlab-pygments 0.1.2 pypi_0 pypi
jupyterlab-server 2.11.2 pypi_0 pypi
jupyterlab_pygments 0.2.2 pyhd8ed1ab_0 conda-forge
jupyterlab_server 2.13.0 pyhd8ed1ab_1 conda-forge
kaleido 0.2.1 pypi_0 pypi
kernel-headers_linux-64 2.6.32 he073ed8_15 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
kiwisolver 1.4.0 py37h7cecad7_0 conda-forge
kneed 0.7.0 pypi_0 pypi
krb5 1.19.3 h08a2579_0 conda-forge
lcms2 2.12 hddcbb42_0 conda-forge
ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge
leidenalg 0.8.10 pypi_0 pypi
lerc 3.0 h9c3ff4c_0 conda-forge
libblas 3.9.0 13_linux64_openblas conda-forge
libbrotlicommon 1.0.9 h7f98852_6 conda-forge
libbrotlidec 1.0.9 h7f98852_6 conda-forge
libbrotlienc 1.0.9 h7f98852_6 conda-forge
libcblas 3.9.0 13_linux64_openblas conda-forge
libcurl 7.82.0 h2283fc2_0 conda-forge
libdeflate 1.10 h7f98852_0 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 h516909a_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc 7.2.0 h69d50b8_2 conda-forge
libgcc-devel_linux-64 9.4.0 hd854feb_16 conda-forge
libgcc-ng 11.2.0 h1d223b6_14 conda-forge
libgfortran-ng 11.2.0 h69a702a_14 conda-forge
libgfortran5 11.2.0 h5c6108e_14 conda-forge
libglib 2.70.2 h174f98d_4 conda-forge
libgomp 11.2.0 h1d223b6_14 conda-forge
libiconv 1.16 h516909a_0 conda-forge
liblapack 3.9.0 13_linux64_openblas conda-forge
libllvm9 9.0.1 default_hc23dcda_7 conda-forge
libnghttp2 1.47.0 he49606f_0 conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libopenblas 0.3.18 pthreads_h8fe5266_0 conda-forge
libpng 1.6.37 h21135ba_2 conda-forge
libsanitizer 9.4.0 h79bfe98_16 conda-forge
libsodium 1.0.18 h36c2ea0_1 conda-forge
libssh2 1.10.0 ha35d2d1_2 conda-forge
libstdcxx-devel_linux-64 9.4.0 hd854feb_16 conda-forge
libstdcxx-ng 11.2.0 he4da1e4_14 conda-forge
libtiff 4.3.0 h542a066_3 conda-forge
libuuid 2.32.1 h7f98852_1000 conda-forge
libwebp 1.2.2 h3452ae3_0 conda-forge
libwebp-base 1.2.2 h7f98852_1 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libxml2 2.9.12 h885dcf4_1 conda-forge
libzlib 1.2.11 h36c2ea0_1013 conda-forge
lifelines 0.27.1 pyhd8ed1ab_0 conda-forge
llvmlite 0.33.0 py37h5202443_1 conda-forge
loompy 3.0.6 py_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
lzo 2.10 h516909a_1000 conda-forge
make 4.3 hd18ef5c_1 conda-forge
markupsafe 2.1.1 py37h540881e_1 conda-forge
matplotlib-base 3.5.1 py37h1058ff1_0 conda-forge
matplotlib-inline 0.1.3 pyhd8ed1ab_0 conda-forge
matplotlib-venn 0.11.6 pypi_0 pypi
metis 5.1.0 h58526e2_1006 conda-forge
mistune 0.8.4 py37h5e8e339_1005 conda-forge
mkl 2022.0.1 h06a4308_117
mpfr 4.1.0 h9202a9a_1 conda-forge
multicoretsne 0.1 pypi_0 pypi
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
mygene 3.2.2 pyh5e36f6f_0 bioconda
natsort 8.1.0 pyhd8ed1ab_0 conda-forge
nbclassic 0.3.7 pyhd8ed1ab_0 conda-forge
nbclient 0.5.13 pypi_0 pypi
nbconvert 6.4.4 pypi_0 pypi
nbconvert-core 6.5.0 pyhd8ed1ab_0 conda-forge
nbconvert-pandoc 6.5.0 pyhd8ed1ab_0 conda-forge
nbformat 5.2.0 pypi_0 pypi
ncurses 6.3 h9c3ff4c_0 conda-forge
nest-asyncio 1.5.4 pypi_0 pypi
networkx 2.6.3 pypi_0 pypi
ninja 1.10.2 h4bd325d_1 conda-forge
notebook 6.4.10 pypi_0 pypi
notebook-shim 0.1.0 pyhd8ed1ab_0 conda-forge
numba 0.51.0rc1 np1.11py3.7h04863e7_g833c5907c_0 numba
numexpr 2.8.1 py37hecfb737_0
numpy 1.21.5 pypi_0 pypi
numpy_groupies 0.9.16 pyhd8ed1ab_0 conda-forge
openblas 0.3.18 pthreads_h4748800_0 conda-forge
openjpeg 2.4.0 hb52868f_1 conda-forge
openpyxl 3.0.9 pyhd8ed1ab_0 conda-forge
openssl 3.0.3 h166bdaf_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
oslom-runner 1.5 pypi_0 pypi
packaging 21.3 pyhd8ed1ab_0 conda-forge
palantir 1.0.0 pypi_0 pypi
pandas 1.3.5 py37he8f5f7f_0 conda-forge
pandoc 2.18 ha770c72_0 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
pango 1.48.10 h4dcc4a0_3 conda-forge
parso 0.8.3 pyhd8ed1ab_0 conda-forge
pathlib 1.0.1 py37h89c1867_6 conda-forge
patsy 0.5.2 pyhd8ed1ab_0 conda-forge
pcre 8.45 h9c3ff4c_0 conda-forge
pcre2 10.37 h032f7d1_0 conda-forge
pexpect 4.8.0 pyh9f0ad1d_2 conda-forge
phenograph 1.5.7 pypi_0 pypi
pickleshare 0.7.5 py_1003 conda-forge
pillow 9.0.1 py37h44f0d7a_2 conda-forge
pip 22.0.4 pyhd8ed1ab_0 conda-forge
pixman 0.40.0 h36c2ea0_0 conda-forge
plotly 5.6.0 py_0 plotly
pluggy 1.0.0 py37h89c1867_2 conda-forge
progressbar2 4.0.0 pyhd8ed1ab_0 conda-forge
prometheus-client 0.13.1 pypi_0 pypi
prometheus_client 0.14.1 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.28 pypi_0 pypi
psutil 5.9.0 py37h540881e_1 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
py 1.11.0 pyh6c4a22f_0 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pydiffmap 0.2.0.1 pypi_0 pypi
pygam 0.8.0 py_0 conda-forge
pygments 2.11.2 pypi_0 pypi
pyopenssl 22.0.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.7 pyhd8ed1ab_0 conda-forge
pyrsistent 0.18.1 py37h540881e_1 conda-forge
pysocks 1.7.1 py37h89c1867_5 conda-forge
pytables 3.7.0 py37h5dea08b_0 conda-forge
pytest 7.1.0 py37h89c1867_0 conda-forge
python 3.7.12 hf930737_100_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.15.3 pyhd8ed1ab_0 conda-forge
python-igraph 0.9.9 py37h6c76e3a_0 conda-forge
python-tzdata 2022.1 pyhd8ed1ab_0 conda-forge
python-utils 3.2.2 pyhd8ed1ab_0 conda-forge
python_abi 3.7 2_cp37m conda-forge
pytorch 1.4.0 py3.7_cuda10.1.243_cudnn7.6.3_0 pytorch
pytz 2021.3 pyhd8ed1ab_0 conda-forge
pytz-deprecation-shim 0.1.0.post0 py37h89c1867_1 conda-forge
pyvis 0.1.9 pypi_0 pypi
pywavelets 1.3.0 py37hda87dfa_1 conda-forge
pyzmq 22.3.0 py37h0c0c2a8_2 conda-forge
r-base 4.1.2 hde4fec0_0 conda-forge
readline 8.1 h46c0cb4_0 conda-forge
requests 2.27.1 pyhd8ed1ab_0 conda-forge
rpy2 3.5.1 py37r41hda87dfa_0 conda-forge
scanpy 1.8.2 pypi_0 pypi
schpf 0.5.0 pypi_0 pypi
scikit-image 0.19.2 py37he8f5f7f_0 conda-forge
scikit-learn 1.0.2 py37hf9e9bfc_0 conda-forge
scikit-misc 0.1.4 pypi_0 pypi
scipy 1.7.3 py37hf2a6cf1_0 conda-forge
scvelo 0.2.4 pyhdfd78af_0 bioconda
seaborn 0.11.2 hd8ed1ab_0 conda-forge
seaborn-base 0.11.2 pyhd8ed1ab_0 conda-forge
sed 4.8 he412f7d_0 conda-forge
send2trash 1.8.0 pyhd8ed1ab_0 conda-forge
session-info 1.0.0 pyhd8ed1ab_0 conda-forge
setuptools 59.8.0 py37h89c1867_0 conda-forge
simplegeneric 0.8.1 py_1 conda-forge
simplejson 3.17.6 pypi_0 pypi
sinfo 0.3.4 pypi_0 pypi
six 1.16.0 pyh6c4a22f_0 conda-forge
sklearn 0.0 pypi_0 pypi
slalom 1.0.0.dev11 pypi_0 pypi
sniffio 1.2.0 py37h89c1867_3 conda-forge
snowballstemmer 2.2.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.3.1 pyhd8ed1ab_0 conda-forge
sphinx 4.5.0 pyh6c4a22f_0 conda-forge
sphinxcontrib-applehelp 1.0.2 py_0 conda-forge
sphinxcontrib-devhelp 1.0.2 py_0 conda-forge
sphinxcontrib-htmlhelp 2.0.0 pyhd8ed1ab_0 conda-forge
sphinxcontrib-jsmath 1.0.1 py_0 conda-forge
sphinxcontrib-qthelp 1.0.3 py_0 conda-forge
sphinxcontrib-serializinghtml 1.1.5 pyhd8ed1ab_2 conda-forge
sqlite 3.37.1 h4ff8645_0 conda-forge
statsmodels 0.13.2 py37hb1e94ed_0 conda-forge
stdlib-list 0.8.0 pypi_0 pypi
suitesparse 5.10.1 h9e50725_1 conda-forge
sysroot_linux-64 2.12 he073ed8_15 conda-forge
tbb 2021.5.0 h4bd325d_0 conda-forge
tenacity 8.0.1 pyhd8ed1ab_0 conda-forge
terminado 0.13.3 py37h89c1867_1 conda-forge
testpath 0.6.0 pypi_0 pypi
texttable 1.6.4 pyhd8ed1ab_0 conda-forge
threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge
tifffile 2019.7.26.2 py37_0 conda-forge
tinycss2 1.1.1 pyhd8ed1ab_0 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
tktable 2.10 hb7b940f_3 conda-forge
tokenize-rt 4.2.1 pyhd8ed1ab_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
toolz 0.11.2 pyhd8ed1ab_0 conda-forge
tornado 6.1 py37h540881e_3 conda-forge
tqdm 4.63.0 pypi_0 pypi
traitlets 5.1.1 pyhd8ed1ab_0 conda-forge
typing-extensions 4.1.1 hd8ed1ab_0 conda-forge
typing_extensions 4.1.1 pyha770c72_0 conda-forge
tzdata 2022a h191b570_0 conda-forge
tzlocal 4.2 py37h89c1867_0 conda-forge
umap-learn 0.4.6 pypi_0 pypi
unicodedata2 14.0.0 py37h5e8e339_0 conda-forge
urllib3 1.26.9 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 pypi_0 pypi
websocket-client 1.3.1 pypi_0 pypi
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
wrapt 1.14.1 py37h540881e_0 conda-forge
xlrd 1.2.0 pypi_0 pypi
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h7f98852_0 conda-forge
xorg-libsm 1.2.3 hd9c2040_1000 conda-forge
xorg-libx11 1.7.2 h7f98852_0 conda-forge
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h7f98852_1 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-libxt 1.2.1 h7f98852_2 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.5 h516909a_1 conda-forge
zeromq 4.3.4 h9c3ff4c_1 conda-forge
zipp 3.7.0 pyhd8ed1ab_1 conda-forge
zlib 1.2.11 h36c2ea0_1013 conda-forge
zstd 1.5.2 ha95c52a_0 conda-forge
Error : sparse matrix length is ambiguous; use getnnz() or shape[0]
The anndata is GSE158055. I passed an anndata into the celltypist and got the above error. What is this error and how do I solve it ?
Plot a celltypist.dotplot to visualise celltypist's classification using a probability threshold
Hi all,
I am trying to visualise the results of the classification using a probability threshold and majority of voting on a cell typist.dotplot. I get an error:
Traceback (most recent call last):
File "celltypist-scRNA-test.py", line 66, in <module>
celltypist.dotplot(predictions, use_as_reference = 'predicted.celltype.l2', use_as_prediction = 'majority_voting', save ='scRNA-test-celltypist-probabilistic-majority_voting.png')
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/celltypist/plot.py", line 140, in dotplot
dot_size_df, dot_color_df = _get_fraction_prob_df(predictions, use_as_reference, use_as_prediction, None, None)
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/celltypist/plot.py", line 33, in _get_fraction_prob_df
score = [row[pred[index]] for index, row in predictions.probability_matrix.iterrows()]
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/celltypist/plot.py", line 33, in <listcomp>
score = [row[pred[index]] for index, row in predictions.probability_matrix.iterrows()]
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/pandas/core/series.py", line 851, in __getitem__
return self._get_value(key)
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/pandas/core/series.py", line 959, in _get_value
loc = self.index.get_loc(label)
File "/Users/ysanchez/opt/anaconda3/envs/transcriptomicsconda/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3082, in get_loc
raise KeyError(key) from err
KeyError: 'Unassigned'
Could you please let me know if there is a way to get around this?
Many thanks for your help!
Error with model download
📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json
timeout Traceback (most recent call last)
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
381 try:
--> 382 self._validate_conn(conn)
383 except (SocketTimeout, BaseSSLError) as e:
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1009 if not getattr(conn, "sock", None): # AppEngine might not have .sock
-> 1010 conn.connect()
1011
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
420 ssl_context=context,
--> 421 tls_in_tls=tls_in_tls,
422 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/util/ssl_.py in ssl_wrap_socket(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)
449 ssl_sock = _ssl_wrap_socket_impl(
--> 450 sock, context, tls_in_tls, server_hostname=server_hostname
451 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/util/ssl_.py in _ssl_wrap_socket_impl(sock, ssl_context, tls_in_tls, server_hostname)
492 if server_hostname:
--> 493 return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
494 else:
~/.conda/envs/single_cell_v0.1/lib/python3.7/ssl.py in wrap_socket(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)
422 context=self,
--> 423 session=session
424 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/ssl.py in _create(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)
869 raise ValueError("do_handshake_on_connect should not be specified for non-blocking sockets")
--> 870 self.do_handshake()
871 except (OSError, ValueError):
~/.conda/envs/single_cell_v0.1/lib/python3.7/ssl.py in do_handshake(self, block)
1138 self.settimeout(None)
-> 1139 self._sslobj.do_handshake()
1140 finally:
timeout: _ssl.c:1074: The handshake operation timed out
During handling of the above exception, another exception occurred:
ReadTimeoutError Traceback (most recent call last)
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 retries=self.max_retries,
--> 449 timeout=timeout
450 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
755 retries = retries.increment(
--> 756 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
757 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
531 if read is False or not self._is_method_retryable(method):
--> 532 raise six.reraise(type(error), error, _stacktrace)
533 elif read is not None:
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/packages/six.py in reraise(tp, value, tb)
769 raise value.with_traceback(tb)
--> 770 raise value
771 finally:
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
705 headers=headers,
--> 706 chunked=chunked,
707 )
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
--> 385 self._raise_timeout(err=e, url=url, timeout_value=conn.timeout)
386 raise
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/urllib3/connectionpool.py in _raise_timeout(self, err, url, timeout_value)
336 raise ReadTimeoutError(
--> 337 self, url, "Read timed out. (read timeout=%s)" % timeout_value
338 )
ReadTimeoutError: HTTPSConnectionPool(host='celltypist.cog.sanger.ac.uk', port=443): Read timed out. (read timeout=30)
During handling of the above exception, another exception occurred:
ReadTimeout Traceback (most recent call last)
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/celltypist/models.py in _requests_get(url, timeout)
36 try:
---> 37 r = requests.get(url, timeout = timeout)
38 r.raise_for_status()
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/api.py in get(url, params, **kwargs)
74
---> 75 return request('get', url, params=params, **kwargs)
76
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
541 send_kwargs.update(settings)
--> 542 resp = self.send(prep, **send_kwargs)
543
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
654 # Send the request
--> 655 r = adapter.send(request, **kwargs)
656
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
528 elif isinstance(e, ReadTimeoutError):
--> 529 raise ReadTimeout(e, request=request)
530 else:
ReadTimeout: HTTPSConnectionPool(host='celltypist.cog.sanger.ac.uk', port=443): Read timed out. (read timeout=30)
During handling of the above exception, another exception occurred:
Exception Traceback (most recent call last)
in
1 # Enabling force_update = True will overwrite existing (old) models.
----> 2 models.download_models(force_update = True)
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/celltypist/models.py in download_models(force_update, model)
424 To check all available models, use :func:~celltypist.models.models_description.
425 """
--> 426 models_json = get_models_index(force_update)
427 logger.info(f"📂 Storing models in {models_path}")
428 if model is not None:
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/celltypist/models.py in get_models_index(force_update)
384 models_json_path = get_model_path("models.json")
385 if not os.path.exists(models_json_path) or force_update:
--> 386 download_model_index()
387 with open(models_json_path) as f:
388 return json.load(f)
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/celltypist/models.py in download_model_index(only_model)
402 logger.info(f"📜 Retrieving model list from server {url}")
403 with open(get_model_path("models.json"), "wb") as f:
--> 404 f.write(_requests_get(url).content)
405 model_count = len(_requests_get(url).json()["models"])
406 logger.info(f"📚 Total models in list: {model_count}")
~/.conda/envs/single_cell_v0.1/lib/python3.7/site-packages/celltypist/models.py in _requests_get(url, timeout)
39 except requests.exceptions.RequestException as e:
40 raise Exception(
---> 41 f"🛑 Cannot fetch '{url}', the error is: {e}")
42 return r
43
Exception: 🛑 Cannot fetch 'https://celltypist.cog.sanger.ac.uk/models/models.json', the error is: HTTPSConnectionPool(host='celltypist.cog.sanger.ac.uk', port=443): Read timed out. (read timeout=30)
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