### 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]
```

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

### 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
```

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

### 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
```

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

### 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

```

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

### 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.
```

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

### 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
```

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

### 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')
```

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

### 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
```

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

### 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 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()
```

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

### 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
```

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

### 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()
```

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

### 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())
```

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

### 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
```

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

### 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"})
```

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

### Sample.clone_vs_analyte(analyte='cnv', plot_width=0, subclone_order=None, protein_order=None, cbar=True)

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

Generates a set of summary plots to provide a run overview. The visualization includes a VAF vs subclone/variant table, cell population distribution, and either a read count distribution (for CNV) or a violin plot (for protein).

```APIDOC
## Sample.clone_vs_analyte

### Description
Generates a set of summary plots to provide a run overview. The plots consist of a VAF vs subclone/variant table, a cell population count/fraction plot, and an analyte-specific plot (read count distribution for CNV or violin plot for protein).

### Signature
`Sample.clone_vs_analyte(analyte='cnv', plot_width=0, subclone_order=None, protein_order=None, cbar=True)`

### Parameters
- **analyte** (str) - Optional - 'cnv' or 'protein' to be used in the third plot.
- **plot_width** (float) - Optional - The width of the plot in points.
- **subclone_order** (list) - Optional - A list containing the order in which subclones should be displayed in the resulting figure.
- **protein_order** (list) - Optional - A list containing the order in which proteins should be displayed in the resulting figure.
- **cbar** (bool) - Optional - Toggle to display or hide the colorbar for the heatmap.
```

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

### Access Function Documentation

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

Uses the built-in help function to retrieve parameter information and default values for specific Mosaic functions.

```python
# Any function's parameters and default values can be looked up via the 'help' function
# Here, the function is 'ms.load'
help(ms.load)
```

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

### Load dependencies

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

Imports necessary libraries for data manipulation, visualization, and display.

```python
# Import mosaic libraries
import missionbio.mosaic as ms

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

# Import numpy for statistics
import numpy as np

# Import additional packages for specific visuals
import matplotlib.pyplot as plt

# Import the colors
import seaborn as sns

# Import the Heatmap class to create heatmaps of custom data
from missionbio.plotting.heatmap import Heatmap
```

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

### Save Sample

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

Persist the analyzed sample object to a specified file path.

```python
ms.save(sample,'/path/to/save/h5')
```

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

### Inspect PCA Help

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

Displays documentation for the PCA function.

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

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

### Modeling amplicons and applying results

Source: https://missionbio.github.io/mosaic/pages/methods/missionbio.mosaic.cnv.Cnv.model_amplicons.html

Demonstrates fitting a CNV model using a spike-in assay, saving it to a sample assay, and adding the resulting labels and ploidy layers to the sample.

```python
>>> import missionbio.mosaic as ms
>>> spikein = ms.load("/path/to/spikein.h5")
>>> sample = ms.load("/path/to/sample.h5")
>>>
>>> lm = spikein.cnv.model_amplicons()
>>> lm.save_model(sample.cnv)
>>> result = sample.sparc([], cnamps=sample.cnv.ids(), min_loh_group_size=2)
>>>
>>> # Add the results to the assay
>>> sample.cnv.add_labels(result.labels.values)  # Add the cluster labels to the assay
>>> sample.cnv.add_layer("flat-ploidy", result.profile.flat_ploidy.values)  # Integer CNV
>>> sample.cnv.add_layer("ploidy", result.profile.relative_ploidy.values)
>>> sample.cnv.signaturemap("ploidy", kind="mean")
```

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

### missionbio.mosaic.io.to_zip(sample, path)

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

Exports a Sample or SampleGroup to a zip file with a specific directory structure for assays.

```APIDOC
## missionbio.mosaic.io.to_zip

### Description
Export the sample to a zip file. Within the zip file, there is a folder for each assay (Dna, Cnv, Protein). The folder structure mirrors the assay, containing layers, row attributes, column attributes, and a metadata.json file.

### Signature
`to_zip(sample: Union[Sample, SampleGroup], path: Union[str, Path]) -> None`

### Parameters
- **sample** (Union[Sample, SampleGroup]) - Required - The sample to be exported. SampleGroup objects are merged using `merge()` before exporting.
- **path** (Union[str, pathlib.Path]) - Required - The path to the zip file.
```

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

### Iterate and visualize per-sample heatmaps

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

Loop through a SampleGroup to generate and display a heatmap for each sample.

```python
# Heatmap per sample
for sample in group:
    fig = sample.dna.heatmap(attribute='NGT_FILTERED',splitby="label")
    fig.update_layout(title = sample.name)
    go.Figure(fig)
    fig.show()
```

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

### List sample order

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

Print the names of samples in the group object to verify their processing order.

```python
# Establish the order of the samples in the group object
# Note: timepoint one will not automatically be the first sample
for sample in group:
    print(sample.name)
```

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

### Sample.raw_heatmaps()

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

Generates three heatmaps showing the distribution of barcodes, protein reads, and DNA reads. Requires cnv_raw and protein_raw to be loaded.

```APIDOC
## Sample.raw_heatmaps()

### Description
Generates three heatmaps with barcodes mapped by total DNA reads (x-axis) and total protein reads (y-axis). The first heatmap shows the number of barcodes in each bin, the second shows the number of protein reads, and the third shows the number of DNA reads. This method is useful for understanding the distribution of barcodes, protein reads, and DNA reads within specific clusters.

### Requirements
- cnv_raw must be loaded
- protein_raw must be loaded
```

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

### to_zip

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

Exports a sample to a zip file.

```APIDOC
## to_zip(sample, path)

### Description
Export the sample to a zip file.

### Parameters
- **sample** (object) - Required - The sample object to export.
- **path** (str) - Required - Destination path.
```

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

### Load H5 File

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

Load data into a sample object with configurable filtering parameters.

```python
sample = ms.load(
    h5path,
    raw=False,
    filter_variants=True,
    filter_cells=False,
    single=True
)
```

```python
sample = ms.load(
   h5path,
   raw=False,
   filter_variants=True,
   single=True,
   whitelist=["chr1:115258747:C/T", "chr2:198266834:T/C", "chrX:133547940:C/T"],
)
```

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

### Sample.heatmap()

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

Plots heatmaps (cluster vs target) for each available assay next to each other.

```APIDOC
## Sample.heatmap(clusterby=None, attributes=None, features=None, x_groups=None, bars_order=None, widths=None, order=('dna', 'protein', 'cnv', 'rna', 'ge'))

### Description
Plots heatmaps (cluster vs target) for each available assay next to each other.

### Parameters
- **clusterby** (list, str) - Optional - The assays to plot. If None, all assays are plotted.
- **attributes** (tuple) - Optional - The layers or row attributes to plot for each assay.
- **features** (tuple) - Optional - The features to plot for each assay. If None, all features are plotted.
- **x_groups** (tuple) - Optional - The groups to plot on the x-axis for each assay. If None, the x-axis is not grouped.
- **bars_order** (Sequence) - Optional - The order of the barcodes to plot. If None, the barcodes are sorted by the sortby assay.
- **widths** (list) - Optional - The width of each heatmap. If None, the width is automatically determined.
- **order** (tuple) - Optional - The order of the assays to plot.

### Returns
- **fig** (plotly.graph_objects.Figure) - The generated multi-assay heatmap figure.
```

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

### Configure Notebook Display

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

Optional configuration to force notebook cells and figures to span the full width of the browser window.

```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>"))
```

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

### Load Data

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

Loads the h5 file into the Mosaic sample object.

```python
# Specify the h5 file to be used in this analysis: h5path = '/path/to/h5/file/test.h5'
# If working with Windows, an 'r' may need to be added before the path: h5path = r'/path/to/h5/file/test.h5'
h5path = './Example.h5'
sample = ms.load(h5path)
```

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

### Inspect Normalization Function

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

Displays help documentation for the protein normalization method.

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

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

### Merge samples in a SampleGroup

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

Demonstrates loading a multisample dataset and merging the samples into a single object.

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

```python
>>> sample = grp.merge()
>>> sample.name  # A single sample with all the cells
'Sample 1, Sample 2'
```

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

### Initialize Combined Heatmap

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

Placeholder for generating a combined DNA, CNV, and protein heatmap.

```python
# DNA + CNV + Protein heatmap
for sample in group:
```