Deepcell-spots

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0.4.2

🧰 Maintenance

<details>
<summary>Bump version to 0.4.2 elaubsch (90)</summary>

Bump PyPI release version to 0.4.2.
</details>

0.4.1

🚀 Features

<details>
<summary>Update spot detection model to SpotDetection-8 elaubsch (84)</summary>

This PR updates the default model for the `SpotDetection` application to `SpotDetection-8`. The model hash and model metadata have also been updated accordingly. The training data used to train `SpotDetection-8`(SpotNet v1.1) adds Airlocalize to the set of spot detection methods used to create the consensus spot labels.
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🐛 Bug Fixes

<details>
<summary>Update model name for SpotDetection-8 elaubsch (85)</summary>

This PR updates the model name for SpotDetection-8.
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<details>
<summary>Remove deprecated parameter from training script elaubsch (83)</summary>

This PR removes the deprecated `alpha` parameter from a call of `DotNetLosses` in `training.py`.
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0.4.0

<details>
<summary>Improve print statements for Polaris application elaubsch (82)</summary>

This PR makes the print statements for prediction progress more informative. It add a progress bar for the spot detection prediction and removes a less useful progress bar for gene assignment.
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<details>
<summary>Add skip round functionality to spot prediction in Polaris elaubsch (68)</summary>

This PR adds a check for rounds with no labeling in defined codebook. If detected, these rounds will be skipped during spot detection to prevent hallucination.
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<details>
<summary>Update copyright to 2023 elaubsch (48)</summary>

This PR updates the copyright to 2023.
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<details>
<summary>Tighten CI configuration rossbar (46)</summary>

A couple minor tweaks to the CI configuration to (hopefully) prevent duplication and unnecessary runs on experimental branches. Specifically:
- 0ce6c0c limits CI so that runs are only triggered when either 1) a PR to `master` is opened, or 2) there is a new push to a branch from which a PR already originates. This should prevent CI from running when commits are pushed up to non-master branches.
- 48f4bc7 adds a check which will cancel any in-progress jobs when new changes are pushed up. This can help e.g. when you push two commits up in rapid succession. By default, GH will queue up the 2nd set of jobs and wait for the first set to finish. With this option, the first set of jobs will be cancelled and the 2nd job will start immediately.

The motivation for these changes is to reduce the CI load in private repositories.
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<details>
<summary>Add back deleted function and bug fix elaubsch (35)</summary>

This PR adds back the `ca_to_adjacency_matrix` which was removed in a previous PR. It also fixes a bug in the graph visualization functions.
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🚀 Features

<details>
<summary>Move masking function outside of Polaris application elaubsch (81)</summary>

This PR moves the abstracted `_mask_spots` to be outside of the application. The function is now available in `results_utils.py`. The unit tests and example notebook for Polaris have been updated.
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<details>
<summary>Add authentication to SpotDetection elaubsch (75)</summary>

This PR adds authentication to `SpotDetection` by using `fetch_data` to download the spot detection model upon instantiation. `fetch_data` requires a DeepCell API key.

For this reason, a large number of the tests in `polaris_tests.py` have been temporarily removed, because models cannot be downloaded in the GitHub actions test environment without an API key.
</details>

<details>
<summary>Pixel-wise decoding for Polaris elaubsch (73)</summary>

This PR introduces an algorithmic change to Polaris that increases the number of pixels sent through the `SpotDecoding` application. Previously, we performed peak finding to determine which pixels were decoded. Now with this change, we will threshold the spot probability image and decode all pixels above a certain spot probability. Then, we create a mask for all of the pixels decoded to genes and apply this mask to the spot probability image. We then perform peak finding on this masked image to call the gene locations. This change is like a compromise between Polaris' original method and pixel-wise decoding methods that are common in MERFISH analysis pipelines. We found that this method increases the number of spots decoded to genes by Polaris, while yielding results with a better correlation to bulk sequencing data.

This change redefines of the `threshold` parameter for the `predict` method. Instead of being used in peak finding, this parameter is used to create a mask for tissue area. Therefore, the default value has been changed.

This PR also includes changes the output of Polaris that are unrelated to the algorithmic change. Polaris previously returned `df_spots` and `df_intensities`, but now these two outputs have been concatenated column-wise to yield a single `DataFrame`.
</details>

<details>
<summary>Add function for barcode assignment to cells for optical pooled screens elaubsch (70)</summary>

This PR adds a function that processes Polaris predictions for barcode assignment to cells for optical pooled screens. Unit tests have been added for this function.
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<details>
<summary>Add get\_cell\_counts to results utils elaubsch (67)</summary>

This PR adds the function `get_cell_counts` to `results_utils`. This utility function converts the Polaris output format to a gene expression per cell table for compatibility with downstream analysis packages like scanpy and Seurat. Unit tests for this function have been added.

This PR also adds an example notebook to demonstrate how to use `get_cell_counts` to generate an input for scanpy.
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<details>
<summary>Add gene scatter plot to results utils elaubsch (65)</summary>

This PR adds an additional function `gene_scatter` to `results_utils`. This function creates a scatter plot with Plotly to visualize the location of decoded genes. It also adds some additional arguments to `expression_correlation` to offer more control over the plot appearance and input.
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<details>
<summary>Add arguments to expression\_correlation elaubsch (64)</summary>

This PR adds arguments to `expression_correlation`:

1. `log`: Boolean that determines whether to create the scatter plot in log space.
2. `exclude_genes`: List of outlier genes excluded from the plot.
3. `exclude_zeros`: Boolean that determines whether zero counts from control and experimental sets are excluded.
4. `eps`: A small epsilon value added to the counts to avoid errors taking the log of zero counts.

This function yields a figure and is not tested, so no additional testing has been added to cover the code added for these arguments.

This PR also includes minor changes to docstrings in `results_utils.py`.
</details>

<details>
<summary>Add results\_utils functions elaubsch (63)</summary>

This PR adds a few utility functions for processing and visualizing the results output by Polaris. Many of these functions require `plotly` which has been added to the requirements file. Unit tests have been added for the functions added in this PR that don't output a figure. This PR include also includes a minor change to a Polaris application docstring.
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<details>
<summary>Add masking of bright background objects elaubsch (57)</summary>

This PR adds a function, `_mask_spots`, to the Polaris application, which creates a mask for bright fluorescent objects in the background image of a FISH sample. It then marks all detected spots inside this mask as 'masked', so they can be exclude from downstream analysis. This information is added to the `decoding_result` and appears as an additional column in the main Polaris output.

Because of the increasing complexity of the Polaris prediction inputs, a function , `_validate_prediction_input` was added to Polaris. This method checks the shapes of the inputs `spots_image`, `segmentation_image`, and `background_image`. It also checks the values of `threshold` and `mask_threshold`.

Test cases have been added to cover these two new functions.
</details>

<details>
<summary>Add mixed barcode rescue to SpotDecoding elaubsch (56)</summary>

This PR adds a function `_rescue_mixed_spots` to the `SpotDecoding` application. This function addresses the case of mixed barcodes caused by spatial crowding of spots. An argument `rescue_mixed` has been added to the `predict` method of `SpotDecoding` to toggle this function. A test case has been added to cover this function. Print statements have been added to make the prediction more verbose to make the amount of error correction more obvious.

The function `_rescue_spots` has been refactored to `_rescue_errors`, because there are now two methods for rescuing spots. The exposed argument `rescue_spots` has also been changed to `rescue_errors`.

Regardless of error correction, two items have been added to the dictionary returned by `SpotDetection.predict`.
1. `spot_index` indexes the spots, because `rescue_mixed_spots` introduces the case that two gene assignments can be made for the same spot. In that case, a new entry is added to the output with the same index as the original spot.
2. `source` details the origin of a prediction. Its values can be:

- `'prediction'` from `SpotDetection.predict`
- `'error rescue'` from `rescue_errors`
- `'mixed rescue'` from `_rescue_mixed_spots`
</details>

<details>
<summary>Add Bernoulli to decoding distributions elaubsch (54)</summary>

This PR adds Bernoulli as an option for decoding distributions. Bernoulli has the same options for numbers of parameters as Relaxed Bernoulli, so the arguments for defining the model distribution have changed. There is a new argument `distribution` which has valid values `['Gaussian', 'Bernoulli', 'Relaxed Bernoulli']`. This is a departure from the previous logic where Relaxed Bernoulli was implied unless `params_mode` was set to `'Gaussian'`. The argument `params_mode` has the same valid values, except `'Gaussian'`.

This PR also adds a `_validate_spots_intensities` function which will verify the `spots_intensities_vec` input into the `SpotDecoding` application, because the different distribution options have different requirements for input values. This function aims to return an error message that will be more interpretable to the user than the PyTorch message. Tests have been added for invalid examples of `spots_intensities_vec`. Logic has been added to the Polaris application to input the correctly pre-processed spot intensities into the `SpotDecoding` application. The singleplex version of the app now returns the original pixel values at the spot locations, which resolves 16.
</details>

<details>
<summary>Rescue spots during spot decoding elaubsch (52)</summary>

This PR adds a function to the `SpotDecoding` application that rescues the spots whose probability values have a Hamming distance of 1 from a barcode in the codebook. A parameter, `rescue_spots`, has been added to the `predict` method of the `SpotDecoding` application, which determines if the rescuing function is applied to the predictions.
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<details>
<summary>Validate codebook in SpotDecoding elaubsch (53)</summary>

This PR adds a function to the `SpotDecoding` application that checks the format of the codebook (`df_barcodes`) during instantiation of the application. This function requires the following criteria:

1. The codebook is a Pandas DataFrame
2. The first column contains the gene names and has the column name 'Gene'
3. The length of the barcodes is equal to the product of the arguments `rounds` and `channels`
4. The barcodes only contain 0s and 1s
5. The codebook does not already contain 'Background' and 'Unknown' entries, because they are added automatically

Unit tests have been added to cover these criteria. Before this PR, the `SpotDecoding` application expected a column 'code_name', containing the gene names. This name has been refactored to 'Gene' which is more accurate/specific.
</details>

<details>
<summary>Add mixture of Gaussians to decoding distributions elaubsch (49)</summary>

This PR adds mixture of Gaussians as an option in `params_mode`. These changes include:

- Addition of decoding functions adapted from PoSTcode (https://github.com/gerstung-lab/postcode).
- Exposing `params_mode` as an argument for the Polaris application
- Addition of logic to use raw pixel values when `params_mode=='Gaussian'` and probability values otherwise
- More extensive testing of applications and decoding functions
</details>

<details>
<summary>Add decoding functionality to Polaris xuefei-wang (36)</summary>

Add decoding part to the repo. Spot decoding has its own application, and is also wrapped into Polaris.
Tests are also provided.
</details>


🐛 Bug Fixes

<details>
<summary>Fix bug for multi-batch predictions with pixel-wise decoding elaubsch (74)</summary>

This PR addresses a bug that affected multi-batch predictions for Polaris' new pixel-wise decoding method. Now, Polaris iterates through the batch index to find the local maxima in the masked spot probability image.
</details>

<details>
<summary>Remove one data validation check from Polaris elaubsch (66)</summary>

This PR removes a data validation check that contains an error from Polaris. The check enforces that the segmentation image has one channel when Mesmer predictions usually have two.
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<details>
<summary>Bug fix for output\_to\_df for mixed rescue elaubsch (62)</summary>

This PR addresses a bug in the input for `output_to_df` caused by the additional spots added during mixed rescue.
</details>

<details>
<summary>Update application docstrings elaubsch (59)</summary>

This PR updates the docstrings for the `SpotDetection`, `SpotDecoding`, and `Polaris` applications. It also contains a bug fix for `_validate_spots_intensities `.
</details>

<details>
<summary>Fix bug in Polaris Bernoulli predictions elaubsch (55)</summary>

This PR fixes a bug in the Polaris `predict` function that arises when decoding with a Bernoulli distribution. It adds a test case to catch this condition.

This PR also applies some of the suggested set syntax changes from 54
</details>

<details>
<summary>Fix bug in multi-batch E-step for spot decoding elaubsch (51)</summary>

This PR contains a few changes in the Relaxed Bernoulli and Gaussian E-step functions for multi-batch predictions. The primary bug was in the RB E-step function, which didn't correctly index the data before prediction when the number of spots exceeded the batch size. The other key bug arose when the number of spots was exactly divisible by the batch size. Other small changes include renaming variables for clarity. Tests have been added for multi-batch predictions and the case where the number of spots are divisible by the batch size.
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<details>
<summary>Allow blank images in training data set elaubsch (42)</summary>

This PR addresses a known bug in `subpixel_distance_transform` that prevented images without spots from being included in the training data set for the spot detection model. `subpixel_distance_transform` now checks the length of input `point_list` and if its length is zero, it returns a null result. This PR also adds a test for the no spots case for `subpixel_distance_transform`.
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<details>
<summary>Add command to install deepcell spots in docker container msschwartz21 (41)</summary>

Currently `deepcell-spots` does not get installed in the docker container unless you mount the spots folder into the correct folder in the running container. This PR adds the installation to the Dockerfile so that the package is always available.
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<details>
<summary>Update pytest and rm pytest-pep8 dependency. rossbar (40)</summary>

pytest-pep8 is no longer supported, and pytest<6 is likely the source of the issues with coveralls.

I will re-add some form of linting to CI to replace pytest-pep8 in the near future (this applies to all the `deepcell-*` libraries). See also vanvalenlab/deepcell-toolbox128.
</details>


🧰 Maintenance

<details>
<summary>Update DEEPCELL\_VERSION in README elaubsch (77)</summary>

This PR updates the specified DeepCell version in the Docker build command in the README.
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<details>
<summary>Update example data download in nbs elaubsch (76)</summary>

This PR updates the data download to use `SpotNetExampleData` and `SpotNet` in the example notebooks for this repo.
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<details>
<summary>Update example notebooks elaubsch (72)</summary>

This PR updates the example notebooks, including a notebook demonstrating training a spot detection model, using the applications, and exporting the results from the Polaris application.
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<details>
<summary>Add tests for results visualization functions elaubsch (71)</summary>

This PR adds tests for results visualization functions. It also adds the `plotly` requirement to `setup.py` and `statsmodels` to `requirements.txt` and `setup.py`. It also removes `hamming_dist_hist` because the input `df_spots` requires manipulation beyond the output of Polaris.
</details>

<details>
<summary>Update decoding probability threshold elaubsch (69)</summary>

This PR updates the threshold probability for barcode assignment during decoding. The new value (0.95) has been determined to yield more consistent/accurate results than the previous value (0.5) in experiments across multiple datasets.
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<details>
<summary>Replace thres\_prob with pred\_prob\_thresh elaubsch (61)</summary>

This PR replaces `thres_prob` with `pred_prob_thresh` for clarity. This variable name is more distinguishable from other thresholds in this code base.
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<details>
<summary>Create utils module in deepcell\_spots elaubsch (60)</summary>

This PR reorganizes the utility functions in `deepcell_spots`. It creates a module, `deepcell_spots.utils`, to which `data_utils`, `preprocessing_utils`, `postprocessing_utils`, and `utils` (refactored to `augmentation_utils`) have been added.
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<details>
<summary>Update application docstrings elaubsch (59)</summary>

This PR updates the docstrings for the `SpotDetection`, `SpotDecoding`, and `Polaris` applications. It also contains a bug fix for `_validate_spots_intensities `.
</details>

<details>
<summary>Update README and example notebooks elaubsch (50)</summary>

This PR updates the README and example notebooks to reflect recent changes to the `SpotDecoding` and `Polaris` applications.
</details>

<details>
<summary>Standardize application classes elaubsch (58)</summary>

This PR makes changes to the applications class to make them more similar to the pattern established in [deepcell.applications](https://github.com/vanvalenlab/deepcell-tf/tree/master/deepcell/applications). This update involves two major changes:

1. It moves the functionality of the `predict` method of `Polaris` to a `_predict` method. Therefore, `predict` is now a wrapper for `_predict`.
2. It removes `Application` from `deepcell_applications` as the base class for `SpotDecoding`, because this application does not need any of the same methods as a normal segmentation application.
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<details>
<summary>Bump default action versions. rossbar (47)</summary>

Bump action versions to keep CI current, c.f. vanvalenlab/deepcell-tf653
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<details>
<summary>Maintenance to clip and threshold application arguments elaubsch (44)</summary>

This PR addresses a naming discrepancy between the `SpotDetection` and `Polaris` applications for the `clip` and `threshold` parameters. The default value for `clip` in `SpotDetection` and `Polaris` have now been set to `True`, because this setting gives better spot detection results on a wider range of images.

It also removes a line passing a `threshold` argument into the `SpotDetection` application inside the `Polaris` application. There are two reasons for this: (1) the `SpotDetection` application in `Polaris` is instantiated with `postprocessing_fn=None`, so the `threshold` argument would not be used, and (2) the default value for `threshold` in the `SpotDetection` application prevents an error from being raised about its value.




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<details>
<summary>Update docstring for decoding functions elaubsch (45)</summary>

This PR addresses some discrepancies between the default values for arguments and default values states in the docstring in `decoding_functions.py`.
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<details>
<summary>Update pinned deepcell version to 0.12.4 in Dockerfile elaubsch (43)</summary>

This PR updates the pinned deepcell version in the Dockerfile. This version determines the base image used to build the deepcell-spots image.
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<details>
<summary>Maintenance of DotNetLosses scripts elaubsch (37)</summary>

This PR removes unused variables in DotNetLosses and makes the style more consistent through the script.
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<details>
<summary>Update Python version in README elaubsch (34)</summary>

This PR updates the Python version in the Docker run command in the README.
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📚️ Documentation

<details>
<summary>Update DEEPCELL\_VERSION in README elaubsch (77)</summary>

This PR updates the specified DeepCell version in the Docker build command in the README.
</details>

0.3.2

🐛 Bug Fixes

<details>
<summary>Bug fix for model output format elaubsch (29)</summary>

This PR fixes a bug in `assign_gene_identities` introduced by changes to the output of the `SpotDetection` application.
</details>

<details>
<summary>Update image alignment functions and tests elaubsch (28)</summary>

This PR fixes a bug in `align_images` and adds tests for this function.
</details>


🧰 Maintenance

<details>
<summary>Add unit tests for model and loss scripts elaubsch (32)</summary>

This PR adds unit tests for `dotnet.py`, `dotnet_losses.py`, and `utils.py`. It also adds the "docs" badge to the README.
</details>

<details>
<summary>Update model version elaubsch (30)</summary>

This PR updates the version of the model used by the SpotDetection application.
</details>


📚️ Documentation

<details>
<summary>Add Read the Docs documentation elaubsch (31)</summary>

This PR adds Read the Docs [documentation](https://deepcell-spots.readthedocs.io/). This includes big changes to the docstrings for the majority of the functions in the package.
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0.3.1

🐛 Bug Fixes

<details>
<summary>Update supported Python versions elaubsch (27)</summary>

This PR changes the supported Python versions as defined in `setup.py`.
</details>

0.3.0

🚀 Features

<details>
<summary>Refactor EM functions to use DataFrames elaubsch (24)</summary>

This PR introduces large changes to the functions used to handle coordinates data during EM for training data creation. The new functions use Pandas DataFrames and dictionaries instead of nested lists of lists, which makes them much less fragile and opaque. No changes were made to the functions used to perform EM, so no changes to the EM output are expected.
</details>

<details>
<summary>Upgrade to TensorFlow 2.8 elaubsch (25)</summary>

This PR upgrades the Tensorflow version from 2.5.1 to 2.8.0 and drops support for Python 3.6. All `tensorflow.python.keras` imports were changed to `tensorflow.keras`. The base Docker image and required DeepCell version have been upgraded to 0.12.0, which also requires Tensorflow 2.8.0.

A new version of the spots model has been trained with Tensorflow 2.8 and results are comparable to previous models. During this process, changes were made to the functions used to assemble the training data set without changing underlying functionality (see 24).
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