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Mixed distribution + mean plots
======================================


Gating plot
--------------------

.. plot::
    :context: reset

    # Read in data
    data = pd.read_csv('data/data_mGL_WPRE/data_mGL_WPRE.csv')
    
    # Plot mGL-H
    x = 'mGL-H'
    hue = 'cond'
    cond_list = ['mGL', 'mGL-WPRE']
    colormap = {'mGL': 'lightgrey',
                'mGL-WPRE': 'limegreen'}

    # Plot
    fig, ax = plt.subplots(1, 1, figsize=(6, 4))
    sns.kdeplot(ax=ax, data=data, x=x, hue=hue, hue_order=cond_list,
                log_scale=(True, False), common_norm=False,
                shade=True, palette=colormap)

    # Plot neg ctrl
    sns.kdeplot(ax=ax, data=data.loc[data['cond'] == 'Neg'], x=x, 
                log_scale=(True, False), common_norm=False,
                shade=False, color='black', alpha=0.5, linestyle='--')
    ax.annotate('Neg', (0.08, 0.25),
                xycoords='axes fraction', alpha=0.5, ha='center')

    # Add threshold for mGL+ gating
    mGL_H_thresh = 2*10**2
    ax.axvline(mGL_H_thresh, 0, 1, color='black')

    # Title
    plt.suptitle('4 dpi')
    # Adjust limits
    mGL_lim = (10, 10**6)
    ax.set_xlim(mGL_lim)

    # Misc plotting stuff
    fig.tight_layout()  # Helps improve white spacing
    plt.show()


Box plot with well means
------------------------

.. plot::
    :context: close-figs

    # Categorize if mGL+
    mGL_cat = list()
    for mGL_val in data['mGL-H']:
        if mGL_val > mGL_H_thresh:
            mGL_cat.append('mGL+')
        else:
            mGL_cat.append('mGL-')
    data['mGL_cat'] = mGL_cat

    # Get total counts and percent of mGL+ and mGL-
    well_group = ['cond', 'replicate', 'sampleNum'] # specifies we're splitting by cond >> bio rep >> tech rep >> etc...
    count_df = data.groupby([*well_group, 'mGL_cat'])['mGL-H'   # Doesn't have to be mGL-H, any column would work
        ].count().unstack(fill_value=0).stack().rename('count') # unstack()/stack() puts 0 if no mGL-H+ rather than dropping row
    percent_df = (count_df*100/count_df.groupby(well_group).transform('sum')
        ).reset_index(name='percent')

    # Extract just the mGL+ cells
    data_mGL = data.loc[data['mGL_cat'] == 'mGL+']
    percent_df_mGL = percent_df.loc[(percent_df['mGL_cat'] == 'mGL+')]

    # Calculate geom mean of mGL+ cells
    well_mGL_gmean_df = data_mGL.groupby(well_group)[
        'mGL-H'].apply(scipy.stats.gmean).reset_index(name='mGL-H (gmean)')

    # Plotting parameters
    x = 'cond'
    y = 'mGL-H'
    order = ['mGL', 'mGL-WPRE']
    pairs = [('mGL', 'mGL-WPRE')]
    colormap = {'mGL': 'lightgrey',
                'mGL-WPRE': 'limegreen'}
    
    # Plot
    fig, ax = plt.subplots(1, 1, figsize=(3, 3))
    sns.boxplot(
        ax=ax, data=data_mGL, 
        x=x, y=y, order=order, 
        boxprops={'facecolor': 'None'}, showfliers=False) # Gets rid of boxplot colors and outliers
    sns.stripplot(
        ax=ax, data=well_mGL_gmean_df, 
        x=x, y=y+' (gmean)', order=order,
        dodge=True, palette=colormap, size=5)
                
    # Add in stats
    annot = Annotator(ax=ax, data=well_mGL_gmean_df, x=x, y=y+' (gmean)', pairs=pairs, order=order)
    annot.configure(test='t-test_ind', text_format='star', loc='inside', verbose=2)
    annot.apply_and_annotate()

    # Adjust labels
    plt.ticklabel_format(axis='y',style='sci',scilimits=(0,0))
    plt.ylabel(y)
    plt.title('4 dpi, HG')
    fig.tight_layout()  # Helps improve white spacing
    plt.show()
    
Violin plot with well means
-----------------------------

.. plot::
    :context: close-figs

    # For violin plots, you must first log10 transform data
    data_mGL['log({})'.format(y)] = np.log10(data_mGL[y])
    well_mGL_gmean_df['log({})'.format(y+' (gmean)')] = np.log10(well_mGL_gmean_df[y+' (gmean)'])

    # Plot
    fig, ax = plt.subplots(1, 1, figsize=(3, 3))
    # Plot all points as violin
    sns.violinplot(
        ax=ax, data=data_mGL,
        x=x, y='log({})'.format(y), order=order,
        palette=colormap, inner="quartile")
    # Plot log10 transformed -> well geometric means of mGL-A as points
    sns.stripplot(
        ax=ax, data=well_mGL_gmean_df, 
        x=x, y='log({})'.format(y+' (gmean)'), order=order,
        dodge=True, color='white', size=5)

    # Make log axis label:
    ax.yaxis.set_major_formatter(
        mticker.StrMethodFormatter("$10^{{{x:.0f}}}$"))
    ax.yaxis.set_ticks(
        [np.log10(x) for p in range(1, 7) for x in np.linspace(10**p, 10**(p+1), 10)],
        minor=True);

    # Add in stats
    annot = Annotator(ax=ax, data=well_mGL_gmean_df, x=x, y=y+' (gmean)', pairs=pairs, order=order)
    annot.configure(test='t-test_ind', text_format='star', loc='inside', verbose=2)
    annot.apply_test().annotate(line_offset_to_group=0.3) # Offset helps account for height of violin

    # Adjust labels
    plt.ylabel(y)
    plt.title('4 dpi, HG')
    fig.tight_layout()  # Helps improve white spacing
    plt.show()