Assays - Snailz Docs

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# Assays

Generate snail assays.

## `AssayParams`

Bases: `BaseModel`

Parameters for assay generation.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`baseline` | `float` | Baseline reading value (must be positive) | required
`degrade` | `float` | Rate at which sample responses decrease per day after first day (0-1) | required
`delay` | `int` | Maximum number of days between specimen collection and assay (must be positive) | required
`mutant` | `float` | Mutant reading value (must be positive) | required
`noise` | `float` | Noise level for readings (must be positive) | required
`oops` | `float` | Factor to multiply response values by for one random person (0 means no adjustment) | required
`plate_size` | `int` | Size of assay plate (must be positive) | required
`seed` | `int` | Random seed for reproducibility | required

Source code in `src/snailz/assays.py`

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    class AssayParams(BaseModel):
        """Parameters for assay generation."""

        baseline: float = Field(
            gt=0, description="Baseline reading value (must be positive)"
        )
        degrade: float = Field(
            ge=0,
            le=1,
            description="Rate at which sample responses decrease per day after first day (0-1)",
        )
        delay: int = Field(
            gt=0,
            description="Maximum number of days between specimen collection and assay (must be positive)",
        )
        mutant: float = Field(gt=0, description="Mutant reading value (must be positive)")
        noise: float = Field(
            gt=0, description="Noise level for readings (must be positive)"
        )
        oops: float = Field(
            ge=0,
            description="Factor to multiply response values by for one random person (0 means no adjustment)",
        )
        plate_size: int = Field(gt=0, description="Size of assay plate (must be positive)")
        seed: int = Field(ge=0, description="Random seed for reproducibility")

        model_config = {"extra": "forbid"}

## `Assay`

Bases: `BaseModel`

A single assay.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`performed` | `date` | date assay was performed | required
`ident` | `str` | unique identifier | required
`specimen_id` | `str` | which specimen | required
`person_id` | `str` | who did the assay | required
`readings` | `list[list[float]]` | grid of assay readings | required
`treatments` | `list[list[str]]` | grid of samples or controls | required

Source code in `src/snailz/assays.py`

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    class Assay(BaseModel):
        """A single assay."""

        performed: date = Field(description="date assay was performed")
        ident: str = Field(description="unique identifier")
        specimen_id: str = Field(description="which specimen")
        person_id: str = Field(description="who did the assay")
        readings: list[list[float]] = Field(description="grid of assay readings")
        treatments: list[list[str]] = Field(description="grid of samples or controls")

        def to_csv(self, data_type: str) -> str:
            """Return a CSV string representation of the assay data.

            Parameters:
                data_type: Type of data to output, either "readings" or "treatments"

            Returns:
                A CSV-formatted string with the assay data.

            Raises:
                ValueError: If data_type is not "readings" or "treatments"
            """
            if data_type not in ["readings", "treatments"]:
                raise ValueError("data_type must be 'readings' or 'treatments'")

            # Get the appropriate data based on data_type
            data = self.readings if data_type == "readings" else self.treatments

            # Generate column headers (A, B, C, etc.) and calculate metadata padding
            plate_size = len(data)
            column_headers = [""] + [chr(65 + i) for i in range(plate_size)]
            max_columns = len(column_headers)
            padding = [""] * (max_columns - 2)

            # Write metadata rows with Unix line endings
            output = io.StringIO()
            writer = utils.csv_writer(output)
            writer.writerow(["id", self.ident] + padding)
            writer.writerow(["specimen", self.specimen_id] + padding)
            writer.writerow(["performed", self.performed.isoformat()] + padding)
            writer.writerow(["performed_by", self.person_id] + padding)

            # Write data rows with row numbers
            writer.writerow(column_headers)
            for i, row in enumerate(data, 1):
                writer.writerow([i] + row)
            return output.getvalue()

### `to_csv(data_type)`

Return a CSV string representation of the assay data.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_type` | `str` | Type of data to output, either "readings" or "treatments" | required

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string with the assay data.

Raises:

Type | Description
--- | ---
`ValueError` | If data_type is not "readings" or "treatments"

Source code in `src/snailz/assays.py`

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    def to_csv(self, data_type: str) -> str:
        """Return a CSV string representation of the assay data.

        Parameters:
            data_type: Type of data to output, either "readings" or "treatments"

        Returns:
            A CSV-formatted string with the assay data.

        Raises:
            ValueError: If data_type is not "readings" or "treatments"
        """
        if data_type not in ["readings", "treatments"]:
            raise ValueError("data_type must be 'readings' or 'treatments'")

        # Get the appropriate data based on data_type
        data = self.readings if data_type == "readings" else self.treatments

        # Generate column headers (A, B, C, etc.) and calculate metadata padding
        plate_size = len(data)
        column_headers = [""] + [chr(65 + i) for i in range(plate_size)]
        max_columns = len(column_headers)
        padding = [""] * (max_columns - 2)

        # Write metadata rows with Unix line endings
        output = io.StringIO()
        writer = utils.csv_writer(output)
        writer.writerow(["id", self.ident] + padding)
        writer.writerow(["specimen", self.specimen_id] + padding)
        writer.writerow(["performed", self.performed.isoformat()] + padding)
        writer.writerow(["performed_by", self.person_id] + padding)

        # Write data rows with row numbers
        writer.writerow(column_headers)
        for i, row in enumerate(data, 1):
            writer.writerow([i] + row)
        return output.getvalue()

## `AllAssays`

Bases: `BaseModel`

Keep track of generated assays.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`items` | `list[Assay]` | actual assays | required
`params` | `AssayParams` | parameters used in generation | required

Source code in `src/snailz/assays.py`

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    class AllAssays(BaseModel):
        """Keep track of generated assays."""

        items: list[Assay] = Field(description="actual assays")
        params: AssayParams = Field(description="parameters used in generation")

        def to_csv(self) -> str:
            """Return a CSV string representation of the assay summary data.

            Returns:
                A CSV-formatted string containing a summary of all assays
            """

            output = io.StringIO()
            writer = utils.csv_writer(output)
            writer.writerow(["ident", "specimen_id", "performed", "performed_by"])
            for assay in self.items:
                writer.writerow(
                    [
                        assay.ident,
                        assay.specimen_id,
                        assay.performed.isoformat(),
                        assay.person_id,
                    ]
                )
            return output.getvalue()

### `to_csv()`

Return a CSV string representation of the assay summary data.

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string containing a summary of all assays

Source code in `src/snailz/assays.py`

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    def to_csv(self) -> str:
        """Return a CSV string representation of the assay summary data.

        Returns:
            A CSV-formatted string containing a summary of all assays
        """

        output = io.StringIO()
        writer = utils.csv_writer(output)
        writer.writerow(["ident", "specimen_id", "performed", "performed_by"])
        for assay in self.items:
            writer.writerow(
                [
                    assay.ident,
                    assay.specimen_id,
                    assay.performed.isoformat(),
                    assay.person_id,
                ]
            )
        return output.getvalue()

## `assays_generate(params, people, specimens)`

Generate an assay for each specimen.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `AssayParams` | AssayParams object containing assay generation parameters | required
`people` | `AllPersons` | People object with staff members | required
`specimens` | `AllSpecimens` | Specimens object with individual specimens to generate assays for | required

Returns:

Type | Description
--- | ---
`AllAssays` | Assays object containing generated assays and parameters

Source code in `src/snailz/assays.py`

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    def assays_generate(
        params: AssayParams, people: AllPersons, specimens: AllSpecimens
    ) -> AllAssays:
        """Generate an assay for each specimen.

        Parameters:
            params: AssayParams object containing assay generation parameters
            people: People object with staff members
            specimens: Specimens object with individual specimens to generate assays for

        Returns:
            Assays object containing generated assays and parameters
        """
        individuals = specimens.individuals
        susc_locus = specimens.susceptible_locus
        susc_base = specimens.susceptible_base
        items = []

        gen = utils.UniqueIdGenerator("assays", lambda: f"{random.randint(0, 999999):06d}")

        # If oops factor is greater than 0, select one person randomly to have their values adjusted
        oops_person_id = None
        if params.oops > 0:
            oops_person = random.choice(people.individuals)
            oops_person_id = oops_person.ident

        for individual in individuals:
            # Set assay date to specimen collection date plus a random number of days (0 to delay)
            assay_date = individual.collected_on + timedelta(
                days=random.randint(0, params.delay)
            )
            assay_id = gen.next()

            # Generate treatments randomly with equal probability
            treatments = []
            for row in range(params.plate_size):
                treatment_row = []
                for col in range(params.plate_size):
                    treatment_row.append(random.choice(["S", "C"]))
                treatments.append(treatment_row)

            # Calculate degradation factor based on days since collection
            days_since_collection = (assay_date - individual.collected_on).days
            degradation_days = max(
                0, days_since_collection - 1
            )  # No degradation on first day
            degradation_factor = max(0.0, 1.0 - (params.degrade * degradation_days))

            # Randomly select a person to perform the assay
            person = random.choice(people.individuals)
            person_id = person.ident

            # Generate readings based on treatments and susceptibility
            readings = []
            is_susceptible = individual.genome[susc_locus] == susc_base
            for row in range(params.plate_size):
                reading_row = []
                for col in range(params.plate_size):
                    if treatments[row][col] == "C":
                        # Control cells have values uniformly distributed between 0 and noise
                        # Controls are not affected by degradation or oops factor
                        value = random.uniform(0, params.noise)
                    else:
                        if is_susceptible:
                            # Susceptible specimens
                            noise = params.noise * params.mutant / params.baseline
                            base_value = params.mutant * degradation_factor
                        else:
                            # Non-susceptible specimens
                            noise = params.noise
                            base_value = params.baseline * degradation_factor

                        # Calculate value and adjust for oops
                        value = base_value + random.uniform(0, noise)
                        if params.oops > 0 and person_id == oops_person_id:
                            value = value * (1 + params.oops)

                    reading_row.append(round(value, utils.PRECISION))

                readings.append(reading_row)

            # Create the assay record
            items.append(
                Assay(
                    performed=assay_date,
                    ident=assay_id,
                    specimen_id=individual.ident,
                    person_id=person_id,
                    readings=readings,
                    treatments=treatments,
                )
            )

        return AllAssays(items=items, params=params)

## `assays_to_csv(input, output)`

Write assays to standard output or files.

Source code in `src/snailz/assays.py`

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    def assays_to_csv(input: str | Path, output: str | Path | None) -> None:
        """Write assays to standard output or files."""
        data = utils.load_data("assays", input, AllAssays)

        # Type casting for the type checker - this tells the type checker
        # that data is an AllAssays instance, but doesn't perform any runtime checks
        data = cast(AllAssays, data)

        # For stdout, only output the summary
        if output is None:
            content = data.to_csv()
            print(content, end="")
            return

        output_path = Path(output)
        with open(output_path / "assays.csv", "w") as writer:
            writer.write(data.to_csv())

        # Create assays subdirectory
        assays_dir = output_path / ASSAYS_SUBDIR
        assays_dir.mkdir(exist_ok=True)

        # Write individual assay files
        for assay in data.items:
            # Design file
            design_file = assays_dir / f"{assay.ident}_design.csv"
            with open(design_file, "w") as writer:
                writer.write(assay.to_csv(data_type="treatments"))

            # Readings file
            assay_file = assays_dir / f"{assay.ident}_assay.csv"
            with open(assay_file, "w") as writer:
                writer.write(assay.to_csv(data_type="readings"))