Introduce string constants

alphastats/DataSet.py

@@ -10,7 +10,7 @@
 
 
 from alphastats.DataSet_Plot import Plot
-from alphastats.DataSet_Preprocess import Preprocess
+from alphastats.DataSet_Preprocess import Preprocess, PreprocessingStateKeys
 from alphastats.DataSet_Pathway import Enrichment
 from alphastats.DataSet_Statistics import Statistics
 from alphastats.utils import LoaderError
@@ -188,7 +188,7 @@ def _remove_misc_samples_in_metadata(self):
     def _subset(self):
         # filter matrix so only samples that are described in metadata are also found in matrix
         self.preprocessing_info.update(
-            {"Matrix: Number of samples": self.metadata.shape[0]}
+            {PreprocessingStateKeys.NUM_SAMPLES: self.metadata.shape[0]}
         )
         return self.mat[self.mat.index.isin(self.metadata[self.sample].tolist())]
 
@@ -265,17 +265,19 @@ def load_metadata(self, file_path: Union[pd.DataFrame, str]) -> pd.DataFrame:
     def _save_dataset_info(self):
         n_proteingroups = self.mat.shape[1]
         preprocessing_dict = {
-            "Raw data number of Protein Groups": n_proteingroups,
-            "Matrix: Number of ProteinIDs/ProteinGroups": self.mat.shape[1],
-            "Matrix: Number of samples": self.mat.shape[0],
-            "Intensity used for analysis": self.intensity_column,
-            "Log2-transformed": False,
-            "Normalization": None,
-            "Imputation": None,
-            "Contaminations have been removed": False,
-            "Contamination columns": self.filter_columns,
-            "Number of removed ProteinGroups due to contaminaton": 0,
-            "Data completeness cut-off": 0,
+            PreprocessingStateKeys.RAW_DATA_NUM_PG: n_proteingroups,
+            PreprocessingStateKeys.NUM_PG: self.mat.shape[1],
+            PreprocessingStateKeys.NUM_SAMPLES: self.mat.shape[0],
+            PreprocessingStateKeys.INTENSITY_COLUMN: self.intensity_column,
+            PreprocessingStateKeys.LOG2_TRANSFORMED: False,
+            PreprocessingStateKeys.NORMALIZATION: None,
+            PreprocessingStateKeys.IMPUTATION: None,
+            PreprocessingStateKeys.CONTAMINATIONS_REMOVED: False,
+            PreprocessingStateKeys.CONTAMINATION_COLUMNS: self.filter_columns,
+            PreprocessingStateKeys.NUM_REMOVED_PG_DUE_TO_CONTAMINATION: 0,
+            PreprocessingStateKeys.DATA_COMPLETENESS_CUTOFF: 0,
+            PreprocessingStateKeys.NUM_PG_REMOVED_DUE_TO_DATA_COMPLETENESS_CUTOFF: 0,
+            PreprocessingStateKeys.MISSING_VALUES_REMOVED: False,
         }
         return preprocessing_dict
 

alphastats/DataSet_Preprocess.py

@@ -13,6 +13,28 @@
 from alphastats.utils import ignore_warning
 
 
+class PreprocessingStateKeys:
+    """Keys for accessing the dictionary holding the information about preprocessing."""
+
+    RAW_DATA_NUM_PG = "Raw data number of Protein Groups"
+    NUM_PG = "Matrix= Number of ProteinIDs/ProteinGroups"
+    NUM_SAMPLES = "Matrix= Number of samples"
+    INTENSITY_COLUMN = "Intensity used for analysis"
+    LOG2_TRANSFORMED = "Log2-transformed"
+    NORMALIZATION = "Normalization"
+    IMPUTATION = "Imputation"
+    CONTAMINATIONS_REMOVED = "Contaminations have been removed"
+    CONTAMINATION_COLUMNS = "Contamination columns"
+    NUM_REMOVED_PG_DUE_TO_CONTAMINATION = (
+        "Number of removed ProteinGroups due to contaminaton"
+    )
+    DATA_COMPLETENESS_CUTOFF = "Data completeness cut-off"
+    NUM_PG_REMOVED_DUE_TO_DATA_COMPLETENESS_CUTOFF = (
+        "Number of removed ProteinGroups due to data completeness cutoff"
+    )
+    MISSING_VALUES_REMOVED = "Missing values were removed"
+
+
 class Preprocess:
     imputation_methods = ["mean", "median", "knn", "randomforest"]
     normalization_methods = ["vst", "zscore", "quantile"]
@@ -45,14 +67,17 @@ def _remove_samples(self, sample_list: list):
     def _subset(self):
         # filter matrix so only samples that are described in metadata are also found in matrix
         self.preprocessing_info.update(
-            {"Matrix: Number of samples": self.metadata.shape[0]}
+            {PreprocessingStateKeys.NUM_SAMPLES: self.metadata.shape[0]}
         )
         return self.mat[self.mat.index.isin(self.metadata[self.sample].tolist())]
 
     def _remove_na_values(self, cut_off):
         if (
-            self.preprocessing_info.get("Missing values were removed")
-            and self.preprocessing_info.get("Data completeness cut-off") == cut_off
+            self.preprocessing_info.get(PreprocessingStateKeys.MISSING_VALUES_REMOVED)
+            and self.preprocessing_info.get(
+                PreprocessingStateKeys.DATA_COMPLETENESS_CUTOFF
+            )
+            == cut_off
         ):
             logging.info("Missing values have already been filtered.")
             st.warning(
@@ -83,10 +108,10 @@ def _remove_na_values(self, cut_off):
 
         self.preprocessing_info.update(
             {
-                "Number of removed ProteinGroups due to data completeness cutoff": num_proteins
+                PreprocessingStateKeys.NUM_PG_REMOVED_DUE_TO_DATA_COMPLETENESS_CUTOFF: num_proteins
                 - self.mat.shape[1],
-                "Missing values were removed": True,
-                "Data completeness cut-off": cut_off,
+                PreprocessingStateKeys.MISSING_VALUES_REMOVED: True,
+                PreprocessingStateKeys.DATA_COMPLETENESS_CUTOFF: cut_off,
             }
         )
 
@@ -95,7 +120,7 @@ def _filter(self):
             logging.info("No columns to filter.")
             return
 
-        if self.preprocessing_info.get("Contaminations have been removed"):
+        if self.preprocessing_info.get(PreprocessingStateKeys.CONTAMINATIONS_REMOVED):
             logging.info("Contaminatons have already been filtered.")
             return
 
@@ -113,11 +138,11 @@ def _filter(self):
 
         self.preprocessing_info.update(
             {
-                "Number of removed ProteinGroups due to contaminaton": len(
+                PreprocessingStateKeys.NUM_REMOVED_PG_DUE_TO_CONTAMINATION: len(
                     protein_groups_to_remove
                 ),
-                "Contaminations have been removed": True,
-                "Matrix: Number of ProteinIDs/ProteinGroups": self.mat.shape[1],
+                PreprocessingStateKeys.CONTAMINATIONS_REMOVED: True,
+                PreprocessingStateKeys.NUM_PG: self.mat.shape[1],
             }
         )
 
@@ -177,7 +202,7 @@ def _imputation(self, method: str):
         self.mat = pd.DataFrame(
             imputation_array, index=self.mat.index, columns=self.mat.columns
         )
-        self.preprocessing_info.update({"Imputation": method})
+        self.preprocessing_info.update({PreprocessingStateKeys.IMPUTATION: method})
 
     def _linear_normalization(self, dataframe: pd.DataFrame):
         """Normalize data using l2 norm without breaking when encoutering nones
@@ -227,7 +252,7 @@ def _normalization(self, method: str):
             normalized_array, index=self.mat.index, columns=self.mat.columns
         )
 
-        self.preprocessing_info.update({"Normalization": method})
+        self.preprocessing_info.update({PreprocessingStateKeys.NORMALIZATION: method})
 
     # TODO this needs to be reimplemented
     # @ignore_warning(RuntimeWarning)
@@ -267,7 +292,7 @@ def _normalization(self, method: str):
 
     def _log2_transform(self):
         self.mat = np.log2(self.mat + 0.1)
-        self.preprocessing_info.update({"Log2-transformed": True})
+        self.preprocessing_info.update({PreprocessingStateKeys.LOG2_TRANSFORMED: True})
         print("Data has been log2-transformed.")
 
     def batch_correction(self, batch: str) -> pd.DataFrame:
@@ -364,7 +389,11 @@ def preprocess(
         if data_completeness > 0:
             self._remove_na_values(cut_off=data_completeness)
 
-        if log2_transform and self.preprocessing_info.get("Log2-transformed") is False:
+        if (
+            log2_transform
+            and self.preprocessing_info.get(PreprocessingStateKeys.LOG2_TRANSFORMED)
+            is False
+        ):
             self._log2_transform()
 
         if normalization is not None:
@@ -380,7 +409,7 @@ def preprocess(
 
         self.preprocessing_info.update(
             {
-                "Matrix: Number of ProteinIDs/ProteinGroups": self.mat.shape[1],
+                PreprocessingStateKeys.NUM_PG: self.mat.shape[1],
             }
         )
 

alphastats/DataSet_Statistics.py

@@ -1,11 +1,9 @@
-from codecs import ignore_errors
-from itertools import permutations
 import pandas as pd
-import scipy.stats
 import numpy as np
 import pingouin
+
+from alphastats.DataSet_Preprocess import PreprocessingStateKeys
 from alphastats.utils import ignore_warning
-from tqdm import tqdm
 from functools import lru_cache
 from typing import Union
 
@@ -22,7 +20,7 @@ def _calculate_foldchange(
     ) -> pd.DataFrame:
         mat_transpose += 0.00001
 
-        if self.preprocessing_info["Log2-transformed"]:
+        if self.preprocessing_info[PreprocessingStateKeys.LOG2_TRANSFORMED]:
             fc = (
                 mat_transpose[group1_samples].T.mean().values
                 - mat_transpose[group2_samples].T.mean().values

alphastats/__init__.py

@@ -39,10 +39,11 @@
     "development": "requirements_development.txt",
 }
 
+# TODO get rid of these imports
 from .loader.AlphaPeptLoader import *
 from .loader.DIANNLoader import *
 from .loader.FragPipeLoader import *
 from .loader.MaxQuantLoader import *
-from .DataSet import *
+from .loader.SpectronautLoader import *
 from .cli import *
 import alphastats.gui

alphastats/gui/pages/02_Import Data.py

@@ -2,7 +2,8 @@
 
 import streamlit as st
 
-from alphastats import DataSet, BaseLoader
+from alphastats.DataSet import DataSet
+from alphastats import BaseLoader
 from alphastats.gui.utils.options import SOFTWARE_OPTIONS
 
 from alphastats.gui.utils.import_helper import (

alphastats/gui/utils/overview_helper.py

@@ -1,7 +1,8 @@
 import streamlit as st
 import pandas as pd
 
-from alphastats import DataSet
+from alphastats.DataSet_Preprocess import PreprocessingStateKeys
+from alphastats.DataSet import DataSet
 from alphastats.gui.utils.ui_helper import convert_df
 
 
@@ -33,11 +34,23 @@ def get_display_matrix():
 def display_matrix():
     text = (
         "Normalization: "
-        + str(st.session_state.dataset.preprocessing_info["Normalization"])
+        + str(
+            st.session_state.dataset.preprocessing_info[
+                PreprocessingStateKeys.NORMALIZATION
+            ]
+        )
         + ", Imputation: "
-        + str(st.session_state.dataset.preprocessing_info["Imputation"])
+        + str(
+            st.session_state.dataset.preprocessing_info[
+                PreprocessingStateKeys.IMPUTATION
+            ]
+        )
         + ", Log2-transformed: "
-        + str(st.session_state.dataset.preprocessing_info["Log2-transformed"])
+        + str(
+            st.session_state.dataset.preprocessing_info[
+                PreprocessingStateKeys.LOG2_TRANSFORMED
+            ]
+        )
     )
 
     st.markdown("**DataFrame used for analysis** *preview*")

alphastats/gui/utils/preprocessing_helper.py

@@ -4,7 +4,7 @@
 import pandas as pd
 from st_cytoscape import cytoscape
 
-from alphastats import DataSet
+from alphastats.DataSet import DataSet
 
 CYTOSCAPE_STYLESHEET = [
     {

alphastats/plots/VolcanoPlot.py

@@ -1,5 +1,6 @@
+from alphastats.DataSet_Preprocess import PreprocessingStateKeys
 from alphastats.plots.PlotUtils import PlotUtils, plotly_object
-from alphastats.utils import ignore_warning, check_for_missing_values
+from alphastats.utils import ignore_warning
 
 import numpy as np
 import pandas as pd
@@ -158,7 +159,10 @@ def _sam(self):
 
         transposed = self.dataset.mat.transpose()
 
-        if self.dataset.preprocessing_info["Normalization"] is None:
+        if (
+            self.dataset.preprocessing_info[PreprocessingStateKeys.NORMALIZATION]
+            is None
+        ):
             # needs to be lpog2 transformed for fold change calculations
             transposed = transposed.transform(lambda x: np.log2(x))
 

alphastats/statistics/DifferentialExpressionAnalysis.py

@@ -4,6 +4,8 @@
 import scipy
 from typing import Union
 
+from alphastats.DataSet_Preprocess import PreprocessingStateKeys
+
 
 class DifferentialExpressionAnalysis:
     def __init__(
@@ -99,7 +101,10 @@ def sam(self) -> pd.DataFrame:
 
         transposed = self.dataset.mat.transpose()
 
-        if self.dataset.preprocessing_info["Normalization"] is None:
+        if (
+            self.dataset.preprocessing_info[PreprocessingStateKeys.NORMALIZATION]
+            is None
+        ):
             # needs to be lpog2 transformed for fold change calculations
             transposed = transposed.transform(lambda x: np.log2(x))
 
@@ -230,7 +235,7 @@ def _calculate_foldchange(
     ):
         mat_transpose += 0.00001
 
-        if self.dataset.preprocessing_info["Log2-transformed"]:
+        if self.dataset.preprocessing_info[PreprocessingStateKeys.LOG2_TRANSFORMED]:
             fc = (
                 mat_transpose[group1_samples].T.mean().values
                 - mat_transpose[group2_samples].T.mean().values

docs/functions.rst

@@ -87,13 +87,3 @@ All GO-analysis functions will return a DataFrame with the results.
 
 * Plot Scatterplot with -log10(p-value) on x-axis and effect size on y-axis. `df.plot_scatter()`
 * Plot p-values as Barplot `df.plot_bar`
-
-
-Misc
-------
-
-Get an overview over your dataset
-
-* :py:meth:`~alphastats.DataSet.overview`
-
-* :py:meth:`~alphastats.DataSet_Preprocess.Preprocess.preprocess_print_info`

docs/import_data.md

@@ -115,7 +115,7 @@ To compare samples across various conditions in the downstream analysis, a metad
 
 ## Creating a DataSet
 
-The whole downstream analysis can be perforemd on the alphastats.DataSet. To create the DataSet you need to provide the loader object as well as the metadata.
+The whole downstream analysis can be performed on the alphastats.DataSet. To create the DataSet you need to provide the loader object as well as the metadata.
 
 ```python
 import alphastats

nbs/getting_started.ipynb

@@ -9,6 +9,9 @@
    "source": [
     "import pandas as pd\n",
     "import warnings\n",
+    "\n",
+    "from alphastats.DataSet import DataSet\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"openpyxl\") # remove warning from mac"
    ]
   },
@@ -713,7 +716,7 @@
     }
    ],
    "source": [
-    "ds = alphastats.DataSet(\n",
+    "ds = DataSet(\n",
     "    loader = maxquant_data, \n",
     "    metadata_path = \"../testfiles/maxquant/metadata.xlsx\",\n",
     "    sample_column = \"sample\" # specify the column that corresponds to the sample names in proteinGroups\n",

nbs/liu_2019.ipynb

@@ -20,6 +20,9 @@
    "source": [
     "import alphastats\n",
     "import plotly.io as pio\n",
+    "\n",
+    "from alphastats.DataSet import DataSet\n",
+    "\n",
     "pio.renderers.default = \"plotly_mimetype+notebook\" "
    ]
   },
@@ -87,7 +90,7 @@
     "    index_column=\"Gene names\",\n",
     "    gene_names_column=None\n",
     ")\n",
-    "dataset = alphastats.DataSet(\n",
+    "dataset = DataSet(\n",
     "    loader = loader, \n",
     "    metadata_path=\"../testfiles/maxquant/metadata.xlsx\", \n",
     "    sample_column=\"sample\"\n",