removed wip features, see future_features branch instead

Author: kaseylove

src/rushd/ddpcr.py

@@ -177,52 +177,3 @@ def load_ddpcr(
 
     return data
 
-
-def calculate_copy_number(
-    df: pd.DataFrame,
-    exp_channel: str,
-    ref_channel: str,
-    gates: Dict[str, float],
-    *,
-    ref_copy_num: float = 2.0,
-) -> pd.DataFrame:
-    """
-    Calculates copy number of an experimental target relative to a
-    reference target.
-
-    Adds a column to the DataFrame with this computed value.
-    Math is based on ... TODO
-
-    Parameters
-    ----------
-    df: pandas DataFrame
-        Data on which to calculate. Must contain columns corresponding 
-        to the experimental and reference channels.
-    exp_channel: str
-        Column in df containing measurements for the experimental target.
-    ref_channel: str
-        Column in df containing measurements for the reference target.
-    gates: dict of (str: float) pairs
-        Gates specifying threshold for positive droplets, one for each
-        experimental and reference channel.
-    ref_copy_num: float, default 2.0
-        Known copy number of the reference gene. If not specified, defaults
-        to 2.0 (diploid).
-
-    Returns
-    -------
-    The original DataFrame with a new column 'copy_num' containing the computed
-    values.
-    """
-    # TODO: throw error if channels not in df
-    # TODO: check if there are no (negative) droplets before calculating
-    data_exp = df[exp_channel]
-    data_ref = df[ref_channel]
-
-    # Compute copies per droplet: -ln(num_negative / num_total)
-    copies_exp = -np.log((data_exp < gates[exp_channel]).sum() / len(data_exp))
-    copies_ref = -np.log((data_ref < gates[ref_channel]).sum() / len(data_ref))
-
-    # Normalize copies to the reference gene
-    df['copy_num'] = copies_exp / copies_ref * ref_copy_num
-    return df

src/rushd/flow.py

@@ -332,13 +332,6 @@ def load_csv(
     return data
 
 
-# draw & optionally assign gates based on quantile (df, channels, quantile(s), multi_gate) -> (df, gates)
-#   assign '[channel]_positive', assign multi-gate '[channel1]_[channel2]_gated'
-# assign gates only (df, gates, multi_gate) -> (df)
-# calculate stats (df, by, channels, stat_list) -> (df_stats)
-
-
-# Rewrite as 'calculate_titer'
 def moi(
     data_frame: pd.DataFrame,
     color_column_name: str,

src/rushd/qpcr.py

@@ -225,162 +225,3 @@ def load_plates_with_metadata(
     return data
 
 
-def calculate_standard(
-    df: pd.DataFrame,
-    amt_col: str,
-    cp_col: str,
-    ax: Optional[matplotlib.axes] = None
-) -> List[scipy.stats._stats_py.LinregressResult, float]:
-    """
-    Calculate a standard curve for qPCR data.
-
-    For the given data, treats the values in 'amt_col' as 
-    input amounts and values in 'cp_col' as the corresponding
-    cycle counts (Cp, aka Ct) from the qPCR output. Computes a linear 
-    regression on log10(amount) vs Cp, and returns this fit as well
-    as the efficiency.
-
-    If axes are passed, plots the linear fit on the data, annotating 
-    the R^2 value and efficiency.
-
-    Parameters
-    ----------
-    df: pandas DataFrame
-        Data to use to fit.
-    amt_col: str
-        Name of column containing input amounts.
-    cp_col: str
-        Name of the column containing Cp values.
-    ax: matplotlib.axes (optional)
-        Axes on which to plot the data and fit.
-
-    Returns
-    -------
-    A tuple of the fit (output of a call to scipy.stats.linregress)
-    and the calculated efficiency (float).
-    """
-    if amt_col not in df.columns:
-        raise ColumnError(f"Data is missing the 'amt_col' column {amt_col}")
-    if cp_col not in df.columns:
-        raise ColumnError(f"Data is missing the 'cp_col' column {cp_col}")
-    
-    # Remove zero values and log10-transform input amounts
-    df_subset = df[df[amt_col]>0].copy()
-    df_subset['log10_'+amt_col] = df_subset[amt_col].astype(float).apply(np.log10)
-
-    # Fit data
-    x = df_subset['log10_'+amt_col]
-    y = df_subset[cp_col].astype(float)
-    fit = scipy.stats.linregress(x, y)
-    efficiency = (10**(-1/fit.slope) - 1)*100 # percentage
-    
-    # Plot result
-    if ax is not None:
-        ax.scatter(df[amt_col], df[cp_col], label='data', ec='white', lw=0.75)
-        xs = np.logspace(min(df_subset['log10_'+amt_col]), max(df_subset['log10_'+amt_col]), 1000)
-        ys = fit.slope * np.log10(xs) + fit.intercept
-        ax.plot(xs, ys, color='crimson', label='linear\nregression')
-        ax.set_xscale('symlog', linthresh=min(df_subset[amt_col]))
-        pad = 0.01
-        ax.legend(loc='upper right', bbox_to_anchor=(1-pad, 1-pad))
-        ax.annotate(f'$R^2$: {abs(fit.rvalue):0.3f}', (0+pad*2, 0.1), xycoords='axes fraction',
-                    ha='left', va='bottom', size='medium')
-        ax.annotate(f'Efficiency: {efficiency:0.1f}%', (0+pad*2, 0+pad*2), xycoords='axes fraction',
-                    ha='left', va='bottom', size='medium')
-    
-    return fit, efficiency
-
-
-def calculate_input_amount(
-    y: float, # TODO: list of float
-    fit: Union[scipy.stats._stats_py.LinregressResult, List[float]],
-) -> float:
-    """
-    Given a cycle count (Cp, aka Ct value) and a linear regression fit, 
-    compute the amount of input.
-
-    Note that the linear regression fit is expected to have been performed
-    on the log10-transform of the input amounts. Units of the returned value
-    match those of the non-transformed input amount data.
-
-    Parameters
-    ----------
-    y: float
-        Cycle count (Cp, aka Ct value).
-    fit: scipy LinregressResult object or list of two floats
-        Linear fit to use. Accepts either the output of a call
-        to scipy.stats.linregress or a list of the fit values
-        [slope, intercept].
-
-    Returns
-    -------
-    A float of the calculated amount.
-    """
-    if type(fit) is scipy.stats._stats_py.LinregressResult:
-        return 10**((float(y)-fit.intercept)/fit.slope)
-    if len(fit) < 2:
-        raise InputError("'fit' is expected to be a list containing [slope, intercept]. Alternatively, pass a scipy LinregressResult object.")
-    return 10**((float(y)-fit[1])/fit[0])
-
-# TODO: add 'type' arg for dsDNA, ssDNA, ssRNA
-def convert_moles_to_mass(
-    moles: Union[float, List[float]],
-    length: Union[float, List[float]]
-) -> Union[float, List[float]]:
-    """
-    For a given amount of DNA in moles, use its length
-    to calculate its mass.
-
-    Formula from NEB: 
-    g = mol x (bp x 615.94 g/mol/bp + 36.04 g/mol)
-     - mass of dsDNA (g) = moles dsDNA x (molecular weight of dsDNA (g/mol))
-     - molecular weight of dsDNA = (number of base pairs of dsDNA x average molecular weight of a base pair) + 36.04 g/mol
-     - average molecular weight of a base pair = 615.94 g/mol, excluding the water molecule removed during polymerization 
-       and assuming deprotonated phosphate hydroxyls
-     - the additional 36.04 g/mol accounts for the 2 -OH and 2 -H added back to the ends
-     - bases are assumed to be unmodified
-
-    Parameters
-    ----------
-    moles: float or list of float
-        Amount of dsDNA in moles.
-    length: float or list of float
-        Number of base pairs of the dsDNA (or average length of a heterogeneous sample).
-    
-    Returns
-    -------
-    A float or list of floats of the calculated mass in grams.
-    """
-    return np.array(moles) * (np.array(length) * 615.96 + 36.04)
-
-
-# TODO: add 'type' arg for dsDNA, ssDNA, ssRNA
-def convert_mass_to_moles(
-    mass: Union[float, List[float]],
-    length: Union[float, List[float]]
-) -> Union[float, List[float]]:
-    """
-    For a given amount of DNA in moles, use its length
-    to calculate its mass.
-
-    Formula from NEB: 
-    mol = g / (bp x 615.94 g/mol/bp + 36.04 g/mol)
-     - moles dsDNA = mass of dsDNA (g) / (molecular weight of dsDNA (g/mol))
-     - molecular weight of dsDNA = (number of base pairs of dsDNA x average molecular weight of a base pair) + 36.04 g/mol
-     - average molecular weight of a base pair = 615.94 g/mol, excluding the water molecule removed during polymerization 
-       and assuming deprotonated phosphate hydroxyls
-     - the additional 36.04 g/mol accounts for the 2 -OH and 2 -H added back to the ends
-     - bases are assumed to be unmodified
-
-    Parameters
-    ----------
-    mass: float or list of float
-        Mass of dsDNA in grams.
-    length: float or list of float
-        Number of base pairs of the dsDNA (or average length of a heterogeneous sample).
-    
-    Returns
-    -------
-    A float or list of floats of the calculated amount in moles.
-    """
-    return np.array(mass) / (np.array(length) * 615.96 + 36.04)