From 4b940f0f7631fa69ddc69d1ea976b1cb43078f10 Mon Sep 17 00:00:00 2001 From: AK Date: Wed, 10 Jul 2024 17:55:46 +0200 Subject: [PATCH] Implemeted Protquantplot with retention time instead of Intensities --- .../data_analysis/prot_quant_plot_peptide.py | 33 +++++++++---------- protzilla/utilities/transform_dfs.py | 25 ++++++++++++++ 2 files changed, 40 insertions(+), 18 deletions(-) diff --git a/protzilla/data_analysis/prot_quant_plot_peptide.py b/protzilla/data_analysis/prot_quant_plot_peptide.py index eeefbcfc..7df80807 100644 --- a/protzilla/data_analysis/prot_quant_plot_peptide.py +++ b/protzilla/data_analysis/prot_quant_plot_peptide.py @@ -23,7 +23,7 @@ def prot_quant_plot_peptide( ) -> dict: """ A function to create a graph visualising protein quantifications across all samples - as a line diagram using retention time and intensity. It's possible to select one proteingroup + as a line diagram using retention time. It's possible to select one proteingroup that will be displayed in orange and choose a similarity measurement with a similarity score to get all proteingroups that are similar displayed in another color in this line diagram. All other proteingroups are displayed in the background as a grey polygon. @@ -37,13 +37,10 @@ def prot_quant_plot_peptide( :return: returns a dictionary containing a list with a plotly figure and/or a list of messages """ - # Ensure the dataframe includes retention time - if 'Retention time' not in input_df.columns: - raise ValueError("The input dataframe must include a 'Retention time' column.") - wide_df = input_df.interpolate(method='linear', axis=0) wide_df = long_to_wide_retention_time(wide_df) if is_long_format(wide_df) else wide_df + if protein_group not in wide_df.columns: raise ValueError("Please select a valid protein group.") elif similarity_measure == "euclidean distance" and similarity < 0: @@ -65,15 +62,15 @@ def prot_quant_plot_peptide( lower_upper_x = [] lower_upper_y = [] - lower_upper_x.append(wide_df['Retention time'].iloc[0]) + lower_upper_x.append(wide_df.index[0]) lower_upper_y.append(wide_df.iloc[0].min()) for index, row in wide_df.iterrows(): - lower_upper_x.append(row['Retention time']) + lower_upper_x.append(index) lower_upper_y.append(row.max()) for index, row in reversed(list(wide_df.iterrows())): - lower_upper_x.append(row['Retention time']) + lower_upper_x.append(index) lower_upper_y.append(row.min()) fig.add_trace( @@ -81,14 +78,14 @@ def prot_quant_plot_peptide( x=lower_upper_x, y=lower_upper_y, fill="toself", - name="Intensity Range", + name="Retention time of all protein groups", line=dict(color="silver"), ) ) similar_groups = [] for group_to_compare in wide_df.columns: - if group_to_compare not in ['Retention time', protein_group]: + if group_to_compare != protein_group: if similarity_measure == "euclidean distance": distance = euclidean_distances( stats.zscore(wide_df[protein_group]).values.reshape(1, -1), @@ -109,7 +106,7 @@ def prot_quant_plot_peptide( for group in similar_groups: fig.add_trace( go.Scatter( - x=wide_df['Retention time'], + x=wide_df.index, y=wide_df[group], mode="lines", name=group[:15] + "..." if len(group) > 15 else group, @@ -134,7 +131,7 @@ def prot_quant_plot_peptide( ) fig.add_trace( go.Scatter( - x=wide_df['Retention time'], + x=wide_df.index, y=wide_df[protein_group], mode="lines", name=formatted_protein_name, @@ -163,22 +160,22 @@ def prot_quant_plot_peptide( ) fig.update_layout( - title=f"Intensity of {formatted_protein_name} across retention time", + title=f"Retention time of {formatted_protein_name} in all samples", plot_bgcolor=colors["plot_bgcolor"], xaxis_gridcolor=colors["gridcolor"], yaxis_gridcolor=colors["gridcolor"], xaxis_linecolor=colors["linecolor"], yaxis_linecolor=colors["linecolor"], - xaxis_title="Retention Time", - yaxis_title="Intensity", + xaxis_title="Sample", + yaxis_title="Retention time", legend_title="Legend", xaxis=dict( tickmode="array", tickangle=0, - tickvals=sorted(wide_df['Retention time']), + tickvals=wide_df.index, ticktext=[ f"•" - for label in wide_df['Retention time'] + for label in wide_df.index ], ), autosize=True, @@ -191,4 +188,4 @@ def prot_quant_plot_peptide( ), ) - return dict(plots=[fig]) + return dict(plots=[fig]) \ No newline at end of file diff --git a/protzilla/utilities/transform_dfs.py b/protzilla/utilities/transform_dfs.py index aa2bc6c0..0b1e7976 100644 --- a/protzilla/utilities/transform_dfs.py +++ b/protzilla/utilities/transform_dfs.py @@ -29,6 +29,31 @@ def long_to_wide(intensity_df: pd.DataFrame, value_name: str = None): intensity_df = intensity_df.fillna(intensity_df.mean()) return intensity_df +def long_to_wide_retention_time(intensity_df: pd.DataFrame, value_name: str = None): + """ + This function transforms the dataframe to a wide format that + can be more easily handled by packages such as sklearn. + Each sample gets one row with all observations as columns. + + :param intensity_df: the dataframe that should be transformed into + long format + :type intensity_df: pd.DataFrame + + :return: returns dataframe in wide format suitable for use by + packages such as sklearn + :rtype: pd.DataFrame + """ + + if intensity_df.duplicated(subset=["Sample", "Protein ID"]).any(): + intensity_df = intensity_df.groupby(["Sample", "Protein ID"]).mean().reset_index() + intensity_df = intensity_df.dropna() + + values_name = 'Retention time' + intensity_df = pd.pivot( + intensity_df, index="Sample", columns="Protein ID", values=values_name + ) + intensity_df = intensity_df.fillna(intensity_df.mean()) + return intensity_df def wide_to_long(wide_df: pd.DataFrame, original_long_df: pd.DataFrame): """