Mosaic

https://missionbio.github.io/mosaic/
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Mosaic is a set of tools to analyze DNA and protein data obtained from the Mission Bio Tapestri...
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### Load example dataset

Source: https://missionbio.github.io/mosaic/notebooks/additional-plots-and-customizations.html

Loads the Multisample PBMC example dataset.

```python
sample = ms.load_example_dataset("Multisample PBMC", single=True)
```

--------------------------------

### Execute Linux Installer

Source: https://missionbio.github.io/mosaic/manual/install.html

Run the downloaded shell script to begin the installation process on Linux.

```bash
$ sh mosaic-v3.12.2-Linux-x86_64.sh
```

--------------------------------

### Initialize SampleGroup

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.samplegroup.SampleGroup.html

Load an example dataset to create a SampleGroup instance.

```python
>>> import missionbio.mosaic as ms
>>> group = ms.load_example_dataset('Multisample PBMC')
```

--------------------------------

### Install Mosaic package

Source: https://missionbio.github.io/mosaic/manual/install.html

Create the mosaic environment and install the required missionbio packages.

```bash
$ conda create --name mosaic -c missionbio -c conda-forge "missionbio.tertiary" "python-kaleido=0.1.0"

```

--------------------------------

### Load an example dataset

Source: https://missionbio.github.io/mosaic/pages/functions/missionbio.mosaic.io.load_example_dataset.html

Loads a specific example dataset by name and retrieves its name property.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("3 cell mix")
>>> sample.name
... "3 cell mix"
```

--------------------------------

### Download Linux Installer

Source: https://missionbio.github.io/mosaic/manual/install.html

Use wget to download the Mosaic installer script directly to a Linux server.

```bash
$ wget https://dl.missionbio.io/mosaic/mosaic-v3.12.2-Linux-x86_64.sh
```

--------------------------------

### Load example dataset

Source: https://missionbio.github.io/mosaic/notebooks/filtering.html

Initializes the Mosaic environment and loads a sample dataset for analysis.

```python
# Import mosaic and load the data

import missionbio.mosaic as ms

sample = ms.load_example_dataset("3 cell mix")
```

--------------------------------

### Install Rosetta 2

Source: https://missionbio.github.io/mosaic/manual/install.html

Use this command to install Rosetta 2 if it is not detected on the system.

```bash
$ softwareupdate --install-rosetta
```

--------------------------------

### Example environment list output

Source: https://missionbio.github.io/mosaic/manual/install.html

Sample output showing the active environment marked with an asterisk.

```text
                 /Users/name/miniconda3
base          *  /Users/name/mosaic

```

--------------------------------

### Import Mosaic and Define Path

Source: https://missionbio.github.io/mosaic/manual/getting_started.html

Initial setup to import the library and specify the local file path.

```python
import missionbio.mosaic as ms
h5path = '/path/to/your/h5/file'
```

--------------------------------

### Launch Jupyter Notebook in mosaic environment

Source: https://missionbio.github.io/mosaic/manual/install.html

Activate the environment and start the Jupyter notebook server.

```bash
$ conda activate mosaic
$ jupyter notebook

```

--------------------------------

### YAML Configuration File

Source: https://missionbio.github.io/mosaic/manual/cohort.html

Example configuration file defining pipeline parameters and input file paths.

```yaml
spike_in:
    variants: /path/to/spike_in_variants.csv  # Optional - required for genome-wide CNV
demultiplex:
   variants: /path/to/demultiplexing.csv  # Optional - required for multiplexed samples
tertiary:
   variants: /path/to/somatic_variants.csv  # Optional - if whitelist/blacklist is needed
   report: hem-onc
   input:
      - h5: /path/to/sample1.dna+protein.h5
        name: Sample1
        patient: ["Patient1", "Patient2", "Patient3"]
        timepoint: [1, 1, 1]
      - h5: /path/to/sample2.dna+protein.h5
        name: Sample2
        patient: ["Patient1", "Patient2"]
        timepoint: [2, 2]
```

--------------------------------

### missionbio.mosaic.io.load_example_dataset(path: str, single: bool = False)

Source: https://missionbio.github.io/mosaic/pages/functions/missionbio.mosaic.io.load_example_dataset.html

Loads a sample dataset from a provided link or a predefined example name.

```APIDOC
## missionbio.mosaic.io.load_example_dataset

### Description
Loads a sample dataset. The function accepts either a link to an h5 file or a predefined example name such as '3 cell mix', '2 PBMC mix', or 'Multisample PBMC'.

### Signature
`load_example_dataset(path: str, single: bool = False) -> Union[Sample, SampleGroup]`

### Parameters
- **path** (str) - Required. Either a link to an h5 file or a predefined dataset name ('3 cell mix', '2 PBMC mix', 'Multisample PBMC').
- **single** (bool) - Optional. Whether to load as a single sample despite being a multi-sample h5 file. Defaults to False. Only recommended for debugging.

### Returns
- **Sample/SampleGroup** - An example sample object.

### Example
```python
import missionbio.mosaic as ms
sample = ms.load_example_dataset("3 cell mix")
print(sample.name)
# Output: "3 cell mix"
```
```

--------------------------------

### Get Help for Clone vs Analyte

Source: https://missionbio.github.io/mosaic/notebooks/curated_notebooks/dna.html

Displays documentation for the clone_vs_analyte method.

```python
help(sample.clone_vs_analyte)
```

--------------------------------

### CSV Format Example

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.demultiplex.dna.truth.Truth.read_csv.html

Example of the expected CSV structure for the Truth.read_csv method.

```csv
variant_id,genotype,type,sample_id
chr1:115256669:G/A,0,germline,SampleA
chr1:115256669:G/A,2,germline,SampleA
```

--------------------------------

### Example of prompt change during activation

Source: https://missionbio.github.io/mosaic/manual/install.html

Visual representation of the command prompt changing from base to mosaic.

```bash
(base) C:\WINDOWS\system32> conda activate mosaic
(mosaic) C:\WINDOWS\system32> jupyter notebook

```

--------------------------------

### Get Help for Signature Map

Source: https://missionbio.github.io/mosaic/notebooks/curated_notebooks/dna.html

Displays documentation for the signaturemap method.

```python
help(sample.signaturemap)
```

--------------------------------

### VariantSubcloneTable.run()

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.workflows.variant_subclone_table.VariantSubcloneTable.run.html

Starts the VariantSubcloneTable workflow with configurable display modes and Dash parameters.

```APIDOC
## VariantSubcloneTable.run()

### Description
Starts the workflow for the VariantSubcloneTable.

### Signature
`VariantSubcloneTable.run(mode: str = 'inline', **kwargs: Any) -> None`

### Parameters
- **mode** (str) - Optional - One of 'inline', 'external', or 'jupyterlab'. Determines where the workflow is displayed.
- **kwargs** (dict) - Optional - Additional arguments passed to Dash.run (e.g., height, width, debug).

### Raises
- **ValueError** - Raised if the provided port value is already in use by another workflow or application.
```

--------------------------------

### Install compatible python-kaleido version

Source: https://missionbio.github.io/mosaic/manual/install.html

Use this command to resolve image saving failures on Windows 11 by installing a specific version of the kaleido package.

```bash
$ conda install -c conda-forge "python-kaleido=0.1.0"
```

--------------------------------

### Get help for DNA clustering methods

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna.ipynb

Access documentation for the DNA clustering functionality.

```python
# Review other clustering methods
help(sample.dna.cluster)
```

--------------------------------

### Conda Initialization Prompt

Source: https://missionbio.github.io/mosaic/manual/install.html

The installer will prompt to initialize the environment via conda; choose yes or no based on your preference for setting the default environment.

```text
Do you wish the installer to initialize mosaic by running conda init? [yes|no]
```

--------------------------------

### Verify Mosaic environment installation

Source: https://missionbio.github.io/mosaic/manual/install.html

List all available conda environments to confirm the mosaic directory exists.

```bash
$ conda env list

```

--------------------------------

### Get help for variant filtering

Source: https://missionbio.github.io/mosaic/v3.12.2/_sources/notebooks/curated_notebooks/dna.ipynb

Displays the help documentation for the filter_variants method.

```python
help(sample.dna.filter_variants)
```

--------------------------------

### Get DNA Annotation Help

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna.ipynb

Displays the help documentation for the get_annotations method.

```python
help(sample.dna.get_annotations)
```

--------------------------------

### CopyNumber.run()

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.workflows.copy_number.CopyNumber.run.html

Starts the copy number workflow with specified display mode and configuration options.

```APIDOC
## missionbio.mosaic.workflows.copy_number.CopyNumber.run

### Description
Starts the copy number workflow.

### Signature
`CopyNumber.run(mode: str = 'inline', **kwargs: Any) -> None`

### Parameters
- **mode** (str) - Optional - One of 'inline', 'external', or 'jupyterlab'. Determines where the workflow is displayed.
- **kwargs** (dict) - Optional - Additional arguments passed to Dash.run (e.g., height, width, debug).

### Raises
- **ValueError** - If the provided port value is already in use by another workflow or application.
```

--------------------------------

### Running SPARC Phylogeny Prediction

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.sample.Sample.sparc.html

Example demonstrating how to load a sample, execute the SPARC algorithm with specified variants, label the clones, and visualize the resulting phylogenetic tree.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load("/path/to/file.h5")
>>> result = sample.sparc(variants=['var1', 'var2', 'var3'])
>>> sample.dna.set_labels(result.labels.values)  # Adds the clones to the assay
>>> result.draw()  # Draws the phylogenetic tree
```

--------------------------------

### Initialize and Run COMPASS

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.algorithms.compass.COMPASS.html

Demonstrates how to instantiate the COMPASS class with a sample and somatic variants, then execute the analysis.

```python
>>> import missionbio.mosaic as ms
>>> from missionbio.mosaic.algorithms.compass import COMPASS
>>> sample = ms.load_example_dataset("2 PBMC mix")
>>> compass = COMPASS(sample, somatic_variants=["chr4:55599436:T/C", "chr5:170837457:A/G"])
>>> compass.run()
```

--------------------------------

### Initialize and Run CopyNumber Workflow

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.workflows.copy_number.CopyNumber.html

Demonstrates how to load a sample and execute the CopyNumber workflow.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("3 cell mix")
>>> wfc = ms.workflows.CopyNumber(sample)
>>> wfc.run()
```

--------------------------------

### Check for Rosetta 2 installation

Source: https://missionbio.github.io/mosaic/manual/install.html

Run this command to verify if Rosetta 2 is currently installed on an Apple device.

```bash
$ /usr/bin/pgrep -q oahd && echo Yes || echo No
```

--------------------------------

### Install specific Mosaic version via conda

Source: https://missionbio.github.io/mosaic/manual/install.html

Use this command to install a specific version of Mosaic from the missionbio conda channel.

```bash
$ conda create --name mosaic -c missionbio -c plotly -c conda-forge missionbio.mosaic=1.8.1 notebook
```

--------------------------------

### Install labeled Mosaic version via conda

Source: https://missionbio.github.io/mosaic/manual/install.html

Use this command to install versions tagged with specific labels by modifying the channel path.

```bash
$ conda create --name mosaic -c missionbio/label/unsupported -c plotly -c conda-forge missionbio.mosaic=1.7.1 notebook
```

--------------------------------

### VariantSubcloneTable.run()

Source: https://missionbio.github.io/mosaic/v3.12.2/pages/methods/missionbio.mosaic.workflows.variant_subclone_table.VariantSubcloneTable.run.html

Starts the variant subclone table workflow.

```APIDOC
## VariantSubcloneTable.run()

### Description
Starts the workflow for the VariantSubcloneTable.

### Signature
`VariantSubcloneTable.run(mode: str = 'inline', **kwargs: Any) -> None`

### Parameters
- **mode** (str) - Optional - One of ‘inline’, ‘external’, or ‘jupyterlab’. If it’s ‘inline’ then the workflow is displayed in the notebook itself otherwise it’s displayed in a new tab in the browser or a new jupyterlab window.
- **kwargs** (dict) - Optional - Passed to Dash.run. E.g. height, width, debug, etc.

### Raises
- **ValueError** - If the provided port value is already in use by another workflow or application.
```

--------------------------------

### Prepare Compass Input

Source: https://missionbio.github.io/mosaic/v3.12.2/_sources/notebooks/curated_notebooks/dna-protein-multisample.ipynb

Initializes the input directory for the Compass analysis tool.

```python
compass.prepare_input(prefix="./compass_input")
```

--------------------------------

### Rename Protein Labels

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/ge-protein.ipynb

Example of how to rename protein labels in the sample.

```python
# sample.protein.rename_labels({
#     "1": "Cell-type1",
#     "2": "Cell-type2",
#     "3": "Cell-type3",
#     "4": "Cell-type4",
#     "5": "Cell-type5",
# })
```

--------------------------------

### Rna.get_markers()

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.rna.Rna.get_markers.html

Get the markers for each RNA cluster using DE analysis.

```APIDOC
## Rna.get_markers(layer='normalized_counts', split_by='label', method='welch', top_n_markers=5, min_cluster_size=10)

### Description
Get the markers for each RNA cluster using DE analysis.

### Parameters
- **layer** (str) - Optional - The RNA data layer to use. Default is 'normalized_counts'.
- **split_by** (str) - Optional - RNA row attribute to call markers on. Default is 'label'.
- **method** (str) - Optional - The DE method to use. 'welch' or 'wilcoxon' currently implemented. Default is 'welch'.
- **top_n_markers** (int) - Optional - How many top markers from each grouping (RNA label) to include in markers table. Default is 5.
- **min_cluster_size** (int) - Optional - Minimum # of cells in smallest group. Will raise ValueError if the smallest group has fewer than min_cells cells. Default is 10.

### Returns
- **missionbio.mosaic.algorithms.rna.cluster_markers.RNAClusterMarkers** - The resulting markers object.
```

--------------------------------

### Initialize and run VariantSubcloneTable

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.workflows.variant_subclone_table.VariantSubcloneTable.html

Instantiate the workflow with a sample object and execute the interactive table interface.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("3 cell mix")
>>> wfv = ms.workflows.VariantSubcloneTable(sample)
>>> wfv.run()
```

--------------------------------

### Save sample

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.sample.Sample.html

Save the current state of the sample to disk.

```python
>>> mio.save(sample)
```

--------------------------------

### run()

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.algorithms.compass.COMPASS.html

Executes the COMPASS algorithm on the provided sample.

```APIDOC
## run(path, prefix, cnv, kwargs)

### Description
Processes the sample using the COMPASS algorithm.
```

--------------------------------

### load_example_dataset

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.io.html

Loads a sample dataset for testing or demonstration purposes.

```APIDOC
## load_example_dataset(path, single=False)

### Description
Load a sample dataset.

### Parameters
- **path** (str) - Required - Path to the sample dataset.
- **single** (bool) - Optional - Whether to load a single dataset.
```

--------------------------------

### Importing Mosaic and IPython utilities

Source: https://missionbio.github.io/mosaic/notebooks/multi-sample-analysis.html

Initializes the Mosaic library and IPython display tools required for the analysis.

```python
import missionbio.mosaic as ms
from IPython.display import display, HTML
```

--------------------------------

### Load dependencies and configure environment

Source: https://missionbio.github.io/mosaic/notebooks/curated_notebooks/dna-multisample.html

Imports necessary libraries for data analysis and visualization, and configures notebook display settings.

```python
import warnings
warnings.filterwarnings('ignore')

# Import mosaic libraries
import missionbio.mosaic as ms

# Import these to display entire dataframes
from IPython.display import display, HTML

# Import graph_objects from the plotly package to display figures when saving the notebook as an HTML
import plotly as px
import plotly.graph_objects as go

# Import additional packages for specific visuals
import matplotlib.pyplot as plt
import plotly.offline as pyo
pyo.init_notebook_mode()
import numpy as np
import seaborn as sns
from missionbio.plotting.multimap import MultiMap
from missionbio.plotting.heatmap import Heatmap

# Note: when exporting the notebook as an HTML, plots that use the "go.Figure(fig)" command are saved
```

```python
# This code is optional, but will make the notebook cells/figures display across the entire width of the browser
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```

--------------------------------

### Initialize and Visualize Built-in Truth

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna-protein.ipynb

Load the built-in truth structure and visualize its configuration.

```python
# Import for protein assignment
from missionbio.demultiplex.protein.truth import Truth

# Visualize the structure of the built-in truth
# The names that are underlines are links to the source of truth
truth = Truth.builtin()
truth.plot()
```

--------------------------------

### Check Mosaic Version

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna.ipynb

Verifies the installed version of the Mosaic library, which should be v3.17 or higher for this notebook.

```python
# Check version; this notebook is designed for Mosaic v3.17 or higher
print(ms.__version__)
```

--------------------------------

### Initialize SampleGroup and inspect assays

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.samplegroup.SampleGroup.merge_assay.html

Load a multisample dataset and verify the titles of the DNA assays present in the group.

```python
>>> import missionbio.mosaic as ms
>>> grp = ms.load_example_dataset("Multisample PBMC")
>>> [s.dna.title for s in grp]  # Group of assays
['Sample 1', 'Sample 2']
```

--------------------------------

### Sample list output

Source: https://missionbio.github.io/mosaic/v3.12.2/notebooks/multi-sample-analysis.html

The resulting list of sample names from the loaded dataset.

```text
['Sample 1', 'Sample 2']
```

--------------------------------

### Generate a crosstab heatmap

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.assay._Assay.crosstabmap.html

Displays a heatmap of the cross-tabulation between DNA and protein labels from a loaded example dataset.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("3 cell mix")
>>> sample.dna.crosstabmap(sample.protein.get_labels())
```

--------------------------------

### Display Gene Name Array

Source: https://missionbio.github.io/mosaic/notebooks/additional-plots-and-customizations.html

Example output showing the array of gene names mapped to amplicons.

```python
array(['ASXL1', 'ASXL1', 'ASXL1', 'ASXL1', 'ASXL1', 'ASXL1', 'ASXL1',
       'ASXL1', 'ASXL1', 'ASXL1', 'ATM', 'ATM', 'BCOR', 'BCOR', 'BCOR',
       'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR',
       'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR', 'BCOR',
       'BCOR', 'BCOR', 'BRAF', 'BRAF', 'BRAF', 'BRAF', 'BRAF', 'BRAF',
       'BRAF', 'BRAF', 'BRAF', 'BRAF', 'BRAF', 'BRAF', 'CALR', 'CBL',
       'CBL', 'CBL', 'CHEK2', 'CHEK2', 'CSF3R', 'CSF3R', 'CSF3R', 'CSF3R',
       'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A',
       'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A',
       'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A', 'DNMT3A',
       'DNMT3A', 'ERG', 'ERG', 'ETV6', 'ETV6', 'ETV6', 'ETV6', 'ETV6',
       'ETV6', 'ETV6', 'ETV6', 'ETV6', 'ETV6', 'EZH2', 'EZH2', 'EZH2',
       'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2',
       'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2', 'EZH2',
       'FLT3', 'FLT3', 'FLT3', 'FLT3', 'FLT3', 'GATA2', 'GATA2', 'GNAS',
       'IDH1', 'IDH2', 'JAK2', 'JAK2', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A',
       'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A',
       'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A', 'KDM6A',
       'KDM6A', 'KIT', 'KIT', 'KIT', 'KIT', 'KIT', 'KIT', 'KMT2A',
       'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A',
       'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A', 'KMT2A',
       'KMT2A', 'KMT2A', 'KRAS', 'KRAS', 'KRAS', 'MPL', 'MYC', 'MYC',
       'MYC', 'MYC', 'MYC', 'MYC', 'MYD88', 'MYD88', 'NF1', 'NF1', 'NF1',
       'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1',
       'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1',
       'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NF1', 'NPM1', 'NRAS',
       'NRAS', 'PHF6', 'PHF6', 'PHF6', 'PHF6', 'PHF6', 'PHF6', 'PHF6',
       'PHF6', 'PPM1D', 'PPM1D', 'PTEN', 'PTEN', 'PTPN11', 'PTPN11',
       'PTPN11', 'PTPN11', 'RAD21', 'RAD21', 'RAD21', 'RAD21', 'RUNX1',
       'RUNX1', 'RUNX1', 'RUNX1', 'RUNX1', 'RUNX1', 'RUNX1', 'RUNX1',
       'SETBP1', 'SETBP1', 'SETBP1', 'SETBP1', 'SETBP1', 'SETBP1',
       'SF3B1', 'SF3B1', 'SF3B1', 'SF3B1', 'SF3B1', 'SMC1A', 'SMC1A',
       'SMC1A', 'SMC3', 'SMC3', 'SMC3', 'SMC3', 'STAG2', 'STAG2', 'STAG2',
       'STAG2', 'STAG2', 'STAG2', 'STAG2', 'STAG2', 'STAG2', 'STAG2',
       'STAG2', 'STAT3', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2',
       'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2',
       'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2', 'TET2',
       'TET2', 'TET2', 'TET2', 'TP53', 'TP53', 'TP53', 'TP53', 'TP53',
       'TP53', 'TP53', 'TP53', 'TP53', 'TP53', 'TP53', 'U2AF1', 'U2AF1',
       'WT1', 'WT1', 'WT1', 'ZRSR2', 'ZRSR2', 'ZRSR2', 'ZRSR2', 'ZRSR2',
       'ZRSR2', 'ZRSR2', 'ZRSR2', 'ZRSR2', 'ZRSR2', 'ZRSR2'], dtype='<U6')
```

--------------------------------

### Load Mosaic Dependencies

Source: https://missionbio.github.io/mosaic/notebooks/curated_notebooks/dna-rna.html

Imports essential libraries for data manipulation, visualization, and reporting. Note that initialization may take several minutes.

```python
import warnings
warnings.filterwarnings('ignore')

# Import mosaic libraries
import missionbio.mosaic as ms
from missionbio.mosaic.assay import _Assay
from missionbio.mosaic.constants import READS, LABEL, NORMALIZED_READS

# Import graph_objects from the plotly package to display figures when saving the notebook as an HTML
import plotly as px
import plotly.graph_objects as go

# Import additional packages for specific visuals
import matplotlib.pyplot as plt
import plotly.offline as pyo
pyo.init_notebook_mode()
import pandas as pd
import numpy as np
import seaborn as sns
from missionbio.plotting.multimap import MultiMap
from missionbio.plotting.heatmap import Heatmap
import plotly.graph_objects as go
from IPython.display import display, HTML
from sklearn.decomposition import PCA

# Import dependencies to create a new report
from pathlib import Path
from missionbio.config.config import Config
from missionbio.tertiary.config.prepare import DEFAULTS
from missionbio.tertiary.step.report import create_single_patient_report

from typing import Sequence, Optional, Tuple

pd.set_option("display.max_rows", 400)

# Note: when exporting the notebook as an HTML, plots that use the "go.Figure(fig)" command are saved
```

--------------------------------

### Access assay info

Source: https://missionbio.github.io/mosaic/manual/data_structure.html

Examples for accessing palette information stored within the assay info dictionary.

```python
>>> sample.dna[sample.dna.samples().pop()]["palette"]  # Accessing the palette dictionary
>>> sample.dna.sampleinfo["palette"]  # Shorthand to access the info for single sample assays
```

--------------------------------

### Initialize and Run SPARC Inference

Source: https://missionbio.github.io/mosaic/pages/missionbio.demultiplex.phylogeny.sparc.SPARC.html

Demonstrates how to load a dataset, initialize the SPARC model, and predict clonal structure using specific SNV variants.

```python
>>> import missionbio.mosaic as ms
>>> from missionbio.demultiplex.phylogeny.sparc import SPARC
>>> sample = ms.load_example_dataset("2 PBMC mix")
>>> sparc = SPARC()
>>> result = sparc.predict(sample.dna, variants=["chr4:55599436:T/C", "chr5:170837457:A/G"])
```

--------------------------------

### Initialize and Run COMPASS

Source: https://missionbio.github.io/mosaic/v3.12.2/_sources/notebooks/curated_notebooks/dna-multisample.ipynb

Initializes the COMPASS object using the full_combined dataset and somatic variants, then executes the analysis.

```python
# Use compass to assume the subclone architecture and phylogeny
# Depending on the size and complexity of the data, this step can take ~1-10 minutes
# Use the full_combined object to utilize all cells and unannotated variants
compass = COMPASS(full_combined, somatic_variants=variants)
compass.run()
```

--------------------------------

### Access Help Documentation

Source: https://missionbio.github.io/mosaic/notebooks/curated_notebooks/dna-protein.html

Retrieve help information for specific protein analysis methods.

```python
help(sample.protein.run_pca)
```

```python
help(sample.protein.scatterplot)
```

--------------------------------

### Configure libmamba solver

Source: https://missionbio.github.io/mosaic/manual/install.html

Set up the faster libmamba solver and strict channel priority for conda.

```bash
$ conda install -n base conda-libmamba-solver
$ conda config --set channel_priority strict
$ conda config --set solver libmamba

```

--------------------------------

### Handle attribute shape mismatch

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.assay._Assay.get_attribute.html

Shows an example where an attribute cannot be reshaped to satisfy the provided constraint, resulting in a ValueError.

```python
>>> assay = assay[:, :9]
>>> print(assay.shape)
(100, 9)
>>> attr = assay.get_attribute('barcode', constraint='col')
ValueError - 'The given attribute does not have the expected
shape nor could be reshaped appropriately.'
```

--------------------------------

### Splitting a Sample by Attribute

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.sample.Sample.split.html

Demonstrates splitting a sample into multiple samples based on the 'label' attribute and creating a SampleGroup.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("3 cell mix")
>>> samples = sample.split("label")  # List of 4 samples for each `label`
>>> ms.SampleGroup(samples)
Group of 4 samples
```

--------------------------------

### Accessing individual samples

Source: https://missionbio.github.io/mosaic/notebooks/multi-sample-analysis.html

Demonstrates how to access specific samples within a SampleGroup and list their names.

```python
# To analyze one sample at a time access, them using the samples attribute
sample = group.samples[1]

# These are the samples in the h5 file
[s.name for s in group.samples]
```

```python
['Sample 1', 'Sample 2']
```

--------------------------------

### Compute cross-tabulation of DNA clones and protein clusters

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.assay._Assay.crosstab.html

Computes the number of cells in each DNA clone and protein cluster using the example dataset.

```python
>>> import missionbio.mosaic as ms
>>> sample = ms.load_example_dataset("2 PBMC mix")
>>> sample.dna.crosstab(sample.protein.get_labels())
```

--------------------------------

### Export variants to CSV

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/ge-protein.ipynb

Provides commented-out examples for exporting the variants table to a single CSV or multiple CSV files grouped by target.

```python
# Write the all variants table to a file

# Uncomment the following line to write out the all_vairants_table (containing information for all targets) to a CSV file

# all_vars_table.to_csv(f"{sample.name}_all_variants.csv")

# Uncomment the following for-loop to write out 1 all_variants CSV file per target to the current working directory

# for target, mini_df in all_vars_table.groupby('target'):
#     mini_df.to_csv(f"{sample.name}_{target}_all_variants.csv")
```

--------------------------------

### SPARCResult.serialize_clones()

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.demultiplex.phylogeny.state.SPARCResult.serialize_clones.html

Renames the clones within the SPARCResult object to a sequential naming convention starting with Wildtype followed by Clone 1, Clone 2, etc.

```APIDOC
## SPARCResult.serialize_clones()

### Description
Renames the clones as Wildtype, Clone 1, Clone 2, Clone 3…

### Signature
`SPARCResult.serialize_clones() -> SPARCResult`

### Returns
- **SPARCResult** - The updated SPARCResult object with renamed clones.
```

--------------------------------

### NSP.__init__

Source: https://missionbio.github.io/mosaic/pages/missionbio.demultiplex.protein.nsp.NSP.html

Initializes the NSP normalization method with parameters for jitter, random state, sample size, and zero-read cell thresholds.

```APIDOC
## class NSP(jitter=0.5, random_state=None, sample_size=inf, max_zero_read_cells=0.05)

### Description
Initializes the Noise corrected and Scaled Protein counts (NSP) normalization object.

### Parameters
- **jitter** (float) - Optional - The standard deviation of the jitter added to read counts.
- **random_state** (int) - Optional - The random state for reproducibility.
- **sample_size** (float) - Optional - Number of cells used to estimate linear models; defaults to all cells (inf).
- **max_zero_read_cells** (float) - Optional - Maximum percentage of cells allowed to have 0 reads after scaling.
```

--------------------------------

### COMPASS Class Initialization

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.algorithms.compass.COMPASS.html

Initializes the COMPASS wrapper with a sample and a list of somatic variants.

```APIDOC
## COMPASS(sample, somatic_variants, min_prob_diff=0.9)

### Description
Initializes the COMPASS wrapper to prepare for phylogenetic analysis on a given sample.

### Parameters
- **sample** (Sample) - Required - The sample object on which COMPASS is to be processed.
- **somatic_variants** (Sequence) - Required - The list of variants used to find the clones, formatted as 'chrom:pos:ref/alt'.
- **min_prob_diff** (float) - Optional - The minimum difference in probability of assignment of nodes for the cell to be assigned a label.
```

--------------------------------

### Access Scatterplot Help

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna-protein.ipynb

Displays documentation for the scatterplot method.

```python
help(sample.protein.scatterplot)
```

--------------------------------

### Load Dependencies

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/clonal-insights.ipynb

Imports necessary libraries for data analysis, visualization, and report generation. Note that this process may take several minutes.

```python
import warnings
warnings.filterwarnings('ignore')

# Import mosaic libraries
import missionbio.mosaic as ms
from missionbio.config.config import Config     
from missionbio.mosaic.assay import _Assay

# Import graph_objects from the plotly package to display figures when saving the notebook as an HTML
import plotly as px
import plotly.graph_objects as go

# Import additional packages for specific visuals
import matplotlib.pyplot as plt
import plotly.offline as pyo
pyo.init_notebook_mode()
import pandas as pd
import numpy as np
import seaborn as sns
from missionbio.plotting.multimap import MultiMap
from missionbio.plotting.heatmap import Heatmap
import plotly.graph_objects as go
from IPython.display import display, HTML
from sklearn.decomposition import PCA

# Import dependencies to create a new report
from pathlib import Path 
from missionbio.tertiary.config.prepare import DEFAULTS
from missionbio.tertiary.step.report import create_single_patient_report, create_cohort_report

# Read in the configuration file
config = Config.read({}, DEFAULTS)

# Note: when exporting the notebook as an HTML, plots that use the "go.Figure(fig)" command are saved
```

--------------------------------

### CopyNumber.__init__

Source: https://missionbio.github.io/mosaic/pages/missionbio.mosaic.workflows.copy_number.CopyNumber.html

Initializes the CopyNumber workflow with a sample object and optional layout configuration.

```APIDOC
## missionbio.mosaic.workflows.copy_number.CopyNumber(sample, layout_kwargs=None)

### Description
Initializes the Copy Number estimation workflow. This workflow filters cells and amplicons, computes ploidy, and provides visualization tools.

### Parameters
- **sample** (Sample) - Required - The Sample object used in the workflow. Clone creation actions modify labels in the Dna object.
- **layout_kwargs** (Dict) - Optional - Arguments passed to fig.update_layout for generated visualizations.
```

--------------------------------

### Fishplot.__init__

Source: https://missionbio.github.io/mosaic/v3.12.2/pages/missionbio.plotting.fishplot.Fishplot.html

Initializes a new Fishplot instance with labels, parent relationships, sample ordering, and barcode data.

```APIDOC
## class missionbio.plotting.fishplot.Fishplot

### Description
Initializes the Fishplot container for visualizing clonal evolution.

### Parameters
- **labels** (Sequence[str]) - Required - The list of unique labels (subclones) to include in the plot.
- **parents** (Sequence[Optional[str]]) - Required - The list of parents for each clone. Use None for the root clone.
- **sample_order** (Sequence[str]) - Required - The names and order of samples to include in the plot.
- **times** (Optional[List[float]]) - Optional - Relative time points for the samples, normalized to between 0 and 1.
- **barcode_labels** (Sequence[str]) - Required - Labels for all barcodes used to determine clone proportions.
- **barcode_samples** (Sequence[str]) - Required - Sample identifiers for all barcodes used to determine clone proportions.
```

--------------------------------

### missionbio.mosaic.io.load()

Source: https://missionbio.github.io/mosaic/pages/functions/missionbio.mosaic.io.load.html

Loads data from an h5 file. Returns a Sample or SampleGroup object, or raises an Exception if the file format is unsupported.

```APIDOC
## missionbio.mosaic.io.load()

### Description
Loads data from an h5 file into a `missionbio.mosaic.sample.Sample` or `missionbio.mosaic.samplegroup.SampleGroup` object.

### Parameters
- **assays** (Optional[Sequence[str]]) - The assays to load. If None, all assays are loaded. Examples include "dna_variants", "dna_read_counts", "protein_read_counts", and "rna_read_counts".
- **raw_counts** (Optional[Sequence[str]]) - The raw counts to load. If None, all raw counts are loaded. Examples include "dna_read_counts" and "protein_read_counts".

### Returns
- **missionbio.mosaic.sample.Sample / missionbio.mosaic.samplegroup.SampleGroup** - The loaded sample or sample group object.

### Errors
- **Exception** - Raised when the h5 file format is not supported.
```

--------------------------------

### Access RNA Normalization Help

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/dna-rna.ipynb

Retrieve documentation for the rna.normalize_reads method.

```python
# To get more information on the available parameters in rna.normalize_reads
help(sample.rna.normalize_reads)
```

--------------------------------

### Load sample H5 file

Source: https://missionbio.github.io/mosaic/_sources/notebooks/curated_notebooks/ge-protein.ipynb

Loads the sample data from a specified H5 file path.

```python
sample = mio.load('/Users/user_name/Documents/edited-t-cells.h5')
```

--------------------------------

### Initialize and draw a BarGraph

Source: https://missionbio.github.io/mosaic/pages/missionbio.plotting.bargraph.BarGraph.html

Demonstrates the basic instantiation of a BarGraph using a pandas Series and rendering it with the draw method.

```python
>>> from missionbio.plotting.bargraph import BarGraph
>>> import pandas as pd
>>> import numpy as np
>>> data = pd.Series([75, 25], index=["a", "b"])
>>> bg = BarGraph(data, text=data.values / 100)
>>> bg.draw()
```

--------------------------------

### Create and draw a phylogenetic tree

Source: https://missionbio.github.io/mosaic/pages/missionbio.plotting.phylotree.PhyloTree.html

Initializes a PhyloTree with 4 nodes and renders it with a custom color palette.

```python
>>> from missionbio.plotting.phylotree import PhyloTree
>>> labels = ["1", "2", "3", "4"]
>>> parents = [None, "1", "1", "2"]
>>> descriptions = ["WT", "KIT HET", "KIT HET<br>TP53 HET", "KIT LOH"]
>>> graph = PhyloTree(labels, parents, descriptions)
>>> graph.draw(palette={"1": "#000000", "2": "crimson", "3": "skyblue", "4": "red"})
```
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