process

TemperatureDict

Bases: TypedDict

TypedDict for temperature dictionary.

Source code in hdxms_datasets/process.py

class TemperatureDict(TypedDict):
    """TypedDict for temperature dictionary."""

    value: float
    unit: Literal["C", "K"]

aggregate_columns(df, columns, by=['start', 'end', 'exposure'])

Aggregate the DataFrame the specified columns by intensity-weighted average.

Source code in hdxms_datasets/process.py

def aggregate_columns(
    df: nw.DataFrame, columns: list[str], by: list[str] = ["start", "end", "exposure"]
):
    """
    Aggregate the DataFrame the specified columns by intensity-weighted average.
    """
    groups = df.group_by(by)
    output = {k: [] for k in by}
    for col in columns:
        output[col] = []
        output[f"{col}_sd"] = []

    for (start, end, exposure), df_group in groups:
        output["start"].append(start)
        output["end"].append(end)
        output["exposure"].append(exposure)

        for col in columns:
            val = ufloat_stats(df_group[col], df_group["intensity"])
            output[col].append(val.nominal_value)
            output[f"{col}_sd"].append(val.std_dev)

    agg_df = nw.from_dict(output, backend=BACKEND)
    return agg_df

compute_uptake_metrics(df, exception='raise')

Tries to add derived columns to the DataFrame. Possible columns to add are: uptake, uptake_sd, fd_uptake, fd_uptake_sd, rfu, max_uptake.

Source code in hdxms_datasets/process.py

def compute_uptake_metrics(df: nw.DataFrame, exception="raise") -> nw.DataFrame:
    """
    Tries to add derived columns to the DataFrame.
    Possible columns to add are: uptake, uptake_sd, fd_uptake, fd_uptake_sd, rfu, max_uptake.
    """
    all_columns = {
        "max_uptake": hdx_expr.max_uptake,
        "uptake": hdx_expr.uptake,
        "uptake_sd": hdx_expr.uptake_sd,
        "fd_uptake": hdx_expr.fd_uptake,
        "fd_uptake_sd": hdx_expr.fd_uptake_sd,
        "frac_fd_control": hdx_expr.frac_fd_control,
        "frac_fd_control_sd": hdx_expr.frac_fd_control_sd,
        "frac_max_uptake": hdx_expr.frac_max_uptake,
        "frac_max_uptake_sd": hdx_expr.frac_max_uptake_sd,
    }

    for col, expr in all_columns.items():
        if col not in df.columns:
            try:
                df = df.with_columns(expr)
            except Exception as e:
                if exception == "raise":
                    raise e
                elif exception == "warn":
                    warnings.warn(f"Failed to add column {col}: {e}")
                elif exception == "ignore":
                    pass
                else:
                    raise ValueError("Invalid exception handling option")

    return df

convert_temperature(temperature_dict, target_unit='C')

Convenience function to convert temperature values.

Parameters:

Name	Type	Description	Default
temperature_dict	TemperatureDict	Dictionary with temperature value(s) and unit.	required
target_unit	str	Target unit for temperature. Must be "C, or "K"	'C'

Returns:

Type	Description
float	Converted temperature value(s).

Source code in hdxms_datasets/process.py

def convert_temperature(temperature_dict: TemperatureDict, target_unit: str = "C") -> float:
    """
    Convenience function to convert temperature values.

    Args:
        temperature_dict: Dictionary with temperature value(s) and unit.
        target_unit: Target unit for temperature. Must be "C, or "K"

    Returns:
        Converted temperature value(s).
    """

    src_unit = temperature_dict["unit"]
    temp_offset = TEMPERATURE_OFFSETS[src_unit] - TEMPERATURE_OFFSETS[target_unit]
    return temperature_dict["value"] + temp_offset

convert_time(time_dict, target_unit='s')

Convenience function to convert time values.

Parameters:

Name	Type	Description	Default
time_dict	dict	Dictionary with time value(s) and unit.	required
target_unit	Literal['s', 'min', 'h']	Target unit for time.	's'

Returns:

Type	Description
Union[float, list[float]]	Converted time value(s).

Source code in hdxms_datasets/process.py

def convert_time(
    time_dict: dict, target_unit: Literal["s", "min", "h"] = "s"
) -> Union[float, list[float]]:
    """
    Convenience function to convert time values.

    Args:
        time_dict: Dictionary with time value(s) and unit.
        target_unit: Target unit for time.

    Returns:
        Converted time value(s).
    """
    raise DeprecationWarning()
    src_unit = time_dict["unit"]

    time_factor = TIME_FACTORS[src_unit] / TIME_FACTORS[target_unit]
    if values := time_dict.get("values"):
        return [v * time_factor for v in values]
    elif value := time_dict.get("value"):
        return value * time_factor
    else:
        raise ValueError("Invalid time dictionary")

drop_null_columns(df)

Drop columns that are all null from the DataFrame.

Source code in hdxms_datasets/process.py

def drop_null_columns(df: nw.DataFrame) -> nw.DataFrame:
    """Drop columns that are all null from the DataFrame."""
    all_null_columns = [col for col in df.columns if df[col].is_null().all()]
    return df.drop(all_null_columns)

dynamx_cluster_to_state(cluster_data, nd_exposure=0.0)

convert dynamx cluster data to state data must contain only a single state

Source code in hdxms_datasets/process.py

def dynamx_cluster_to_state(cluster_data: nw.DataFrame, nd_exposure: float = 0.0) -> nw.DataFrame:
    """
    convert dynamx cluster data to state data
    must contain only a single state
    """

    assert len(cluster_data["state"].unique()) == 1, "Multiple states found in data"

    # determine undeuterated masses per peptide
    nd_data = cluster_data.filter(nw.col("exposure") == nd_exposure)
    nd_peptides: list[tuple[int, int]] = sorted(
        {(start, end) for start, end in zip(nd_data["start"], nd_data["end"])}
    )

    peptides_nd_mass = {}
    for p in nd_peptides:
        start, end = p
        df_nd_peptide = nd_data.filter((nw.col("start") == start) & (nw.col("end") == end))

        masses = df_nd_peptide["z"] * (df_nd_peptide["center"] - PROTON_MASS)
        nd_mass = ufloat_stats(masses, df_nd_peptide["inten"])

        peptides_nd_mass[p] = nd_mass

    groups = cluster_data.group_by(["start", "end", "exposure"])
    unique_columns = [
        "end",
        "exposure",
        "fragment",
        "maxuptake",
        "mhp",
        "modification",
        "protein",
        "sequence",
        "start",
        "state",
        "stop",
    ]
    records = []
    for (start, end, exposure), df_group in groups:
        record = {col: df_group[col][0] for col in unique_columns}

        rt = ufloat_stats(df_group["rt"], df_group["inten"])
        record["rt"] = rt.nominal_value
        record["rt_sd"] = rt.std_dev

        # state data 'center' is mass as if |charge| would be 1
        center = ufloat_stats(
            df_group["z"] * (df_group["center"] - PROTON_MASS) + PROTON_MASS, df_group["inten"]
        )
        record["center"] = center.nominal_value
        record["center_sd"] = center.std_dev

        masses = df_group["z"] * (df_group["center"] - PROTON_MASS)
        exp_mass = ufloat_stats(masses, df_group["inten"])

        if (start, end) in peptides_nd_mass:
            uptake = exp_mass - peptides_nd_mass[(start, end)]
            record["uptake"] = uptake.nominal_value
            record["uptake_sd"] = uptake.std_dev
        else:
            record["uptake"] = None
            record["uptake_sd"] = None

        records.append(record)

    d = records_to_dict(records)
    df = nw.from_dict(d, backend=BACKEND)

    if set(df.columns) == set(STATE_DATA_COLUMN_ORDER):
        df = df[STATE_DATA_COLUMN_ORDER]

    return df

filter_peptides(df, state=None, exposure=None)

Convenience function to filter a peptides DataFrame. .

Parameters:

Name	Type	Description	Default
df	DataFrame	Input dataframe.	required
state	Optional[str]	Name of protein state to select.	None
exposure	Optional[dict]	Exposure value(s) to select. Exposure is given as a :obj:dict, with keys "value" or "values" for exposure value, and "unit" for the time unit.	None
time_unit		Time unit for exposure column of supplied dataframe.	required

Examples:

Filter peptides for a specific protein state and exposure time:

>>> d = {"state", "SecB WT apo", "exposure": {"value": 0.167, "unit": "min"}
>>> filtered_df = filter_peptides(df, **d)

Returns:

Type	Description
DataFrame	Filtered dataframe.

Source code in hdxms_datasets/process.py

def filter_peptides(
    df: nw.DataFrame,
    state: Optional[str] = None,
    exposure: Optional[dict] = None,
) -> nw.DataFrame:
    """
    Convenience function to filter a peptides DataFrame. .

    Args:
        df: Input dataframe.
        state: Name of protein state to select.
        exposure: Exposure value(s) to select. Exposure is given as a :obj:`dict`, with keys "value" or "values" for
            exposure value, and "unit" for the time unit.
        time_unit: Time unit for exposure column of supplied dataframe.

    Examples:
        Filter peptides for a specific protein state and exposure time:

        >>> d = {"state", "SecB WT apo", "exposure": {"value": 0.167, "unit": "min"}
        >>> filtered_df = filter_peptides(df, **d)

    Returns:
        Filtered dataframe.
    """
    raise DeprecationWarning()
    if state is not None:
        df = df.filter(nw.col("state") == state)

    if exposure is not None:
        # NA unit is used when exposure is given as string, in case of HD examiner this can be 'FD'
        if exposure["unit"] == "NA":
            t_val = exposure["value"]
        else:
            t_val = convert_time(exposure, "s")
        if isinstance(t_val, list):
            if all(isinstance(v, float) for v in t_val):
                col = nw.col("exposure")
            elif all(isinstance(v, str) for v in t_val):
                col = nw.col("exposure").cast(nw.Float64)
            else:
                raise ValueError("Invalid exposure values")
            df = df.filter(col.is_in(t_val))
        else:
            df = df.filter(nw.col("exposure") == t_val)

    return df

records_to_dict(records)

Convert a list of records to a dictionary of lists.

Parameters:

Name	Type	Description	Default
records	list[dict]	List of dictionaries.	required

Returns:

Type	Description
dict[str, list]	Dictionary with keys as column names and values as lists.

Source code in hdxms_datasets/process.py

def records_to_dict(records: list[dict]) -> dict[str, list]:
    """
    Convert a list of records to a dictionary of lists.

    Args:
        records: List of dictionaries.

    Returns:
        Dictionary with keys as column names and values as lists.
    """

    df_dict = defaultdict(list)
    for record in records:
        for key, value in record.items():
            df_dict[key].append(value)

    return dict(df_dict)

sort_rows(df)

Sorts the DataFrame by state, exposure, start, end, file.

Source code in hdxms_datasets/process.py

def sort_rows(df: nw.DataFrame) -> nw.DataFrame:
    """Sorts the DataFrame by state, exposure, start, end, file."""
    all_by = ["state", "exposure", "start", "end", "replicate"]
    by = [col for col in all_by if col in df.columns]
    return df.sort(by=by)

ufloat_stats(array, weights)

Calculate the weighted mean and standard deviation.

Source code in hdxms_datasets/process.py

def ufloat_stats(array, weights) -> Variable:
    """Calculate the weighted mean and standard deviation."""
    weighted_stats = DescrStatsW(array, weights=weights, ddof=0)
    return ufloat(weighted_stats.mean, weighted_stats.std)