Merge pull request #10 from schillerlab/release/0.2.0

release/0.2.0

.cookietemple.yml

@@ -16,5 +16,5 @@ email: [email protected]
 project_name: sc-toolbox
 project_short_description: A collection of project templates and useful code snippets
   for single-cell data analysis with Scanpy.
-version: 0.1.0
+version: 0.2.0
 license: MIT

.github/release-drafter.yml

@@ -1,5 +1,5 @@
-name-template: "0.1.0 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
-tag-template: 0.1.0 # <<COOKIETEMPLE_FORCE_BUMP>>
+name-template: "0.2.0 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
+tag-template: 0.2.0 # <<COOKIETEMPLE_FORCE_BUMP>>
 categories:
     - title: "🚀 Features"
       labels:

cookietemple.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.0
+current_version = 0.2.0
 
 [bumpversion_files_whitelisted]
 init_file = sc_toolbox/__init__.py

docs/conf.py

@@ -49,9 +49,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = "0.1.0"
+version = "0.2.0"
 # The full version, including alpha/beta/rc tags.
-release = "0.1.0"
+release = "0.2.0"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "sc-toolbox"
-version = "0.1.0"  # <<COOKIETEMPLE_FORCE_BUMP>>
+version = "0.2.0"  # <<COOKIETEMPLE_FORCE_BUMP>>
 description = "A collection of project templates and useful code snippets for single-cell data analysis with Scanpy."
 authors = ["Lukas Heumos <[email protected]>"]
 license = "MIT"
@@ -9,7 +9,6 @@ homepage = "https://github.com/schillerlab/sc-toolbox"
 repository = "https://github.com/schillerlab/sc-toolbox"
 documentation = "https://sc-toolbox.readthedocs.io"
 classifiers = [
-    "Programming Language :: Python :: 3.6",
     "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",

sc_toolbox/__init__.py

@@ -2,7 +2,7 @@
 
 __author__ = "Lukas Heumos"
 __email__ = "[email protected]"
-__version__ = "0.1.0"
+__version__ = "0.2.0"
 
 from sc_toolbox.api import plot
 from sc_toolbox.api import calc

sc_toolbox/api/calc/__init__.py

@@ -59,7 +59,7 @@ def generate_expression_table(
     return gen_expression_table
 
 
-def calc_relative_frequencies(adata, group_by: str = "cell_type", xlabel: str = "days", condition: str = "batch"):
+def relative_frequencies(adata, group_by: str = "cell_type", xlabel: str = "days", condition: str = "batch"):
     """
     Calculates the relative frequencies of conditions grouped by an observation.
 
@@ -98,6 +98,43 @@ def calc_relative_frequencies(adata, group_by: str = "cell_type", xlabel: str =
     return relative_frequencies
 
 
+def relative_frequency_per_cluster(adata, group_by: str = "cell_type", xlabel: str = "days", condition=None):
+    """
+    Calculates relative frequencies per cluster
+
+    Args:
+        adata: AnnData object containing the data
+        group_by: The label to group by for the clusters
+        xlabel: x-axis label
+        condition: condition to combine by
+
+    Returns:
+        Pandas DataFrame of relative frequencies
+    """
+    frequencies = adata.obs.groupby([group_by, xlabel]).size()
+    celltypes = np.unique(adata.obs[group_by])
+    ind = adata.obs[xlabel].cat.categories
+
+    relative_frequencies = [frequencies[ident] / sum(frequencies[ident]) for ident in celltypes]
+    relative_frequencies = pd.DataFrame(relative_frequencies, columns=ind, index=celltypes).fillna(0)
+
+    cell_types = {}
+    combinations = adata.obs.groupby([group_by, xlabel]).groups.keys()
+
+    for combination in combinations:
+        cell_types[combination[0]] = combination[1]
+    relative_frequencies[group_by] = relative_frequencies.index  # type: ignore
+
+    # Todo, add for condition
+    if condition:
+        combinations = adata.obs.groupby([group_by, condition]).groups.keys()
+        for combination in combinations:
+            cell_types[combination[0]] = combination[1]
+        relative_frequencies[condition] = [cell_types[label] for label in relative_frequencies.index]  # type: ignore
+
+    return relative_frequencies
+
+
 def correlate_to_signature(
     adata,
     marker: pd.DataFrame,
@@ -178,43 +215,6 @@ def remove_outliers(cords, eps: int = 1, min_samples: int = 2):
     return pd.Categorical(cluster, categories=natsorted(np.unique(cluster)))
 
 
-def calc_relative_frequency_per_cluster(adata, group_by: str = "cell_type", xlabel: str = "days", condition=None):
-    """
-    Calculates relative frequencies per cluster
-
-    Args:
-        adata: AnnData object containing the data
-        group_by: The label to group by for the clusters
-        xlabel: x-axis label
-        condition: condition to combine by
-
-    Returns:
-        Pandas DataFrame of relative frequencies
-    """
-    frequencies = adata.obs.groupby([group_by, xlabel]).size()
-    celltypes = np.unique(adata.obs[group_by])
-    ind = adata.obs[xlabel].cat.categories
-
-    relative_frequencies = [frequencies[ident] / sum(frequencies[ident]) for ident in celltypes]
-    relative_frequencies = pd.DataFrame(relative_frequencies, columns=ind, index=celltypes).fillna(0)
-
-    cell_types = {}
-    combinations = adata.obs.groupby([group_by, xlabel]).groups.keys()
-
-    for combination in combinations:
-        cell_types[combination[0]] = combination[1]
-    relative_frequencies[group_by] = relative_frequencies.index  # type: ignore
-
-    # Todo, add for condition
-    if condition:
-        combinations = adata.obs.groupby([group_by, condition]).groups.keys()
-        for combination in combinations:
-            cell_types[combination[0]] = combination[1]
-        relative_frequencies[condition] = [cell_types[label] for label in relative_frequencies.index]  # type: ignore
-
-    return relative_frequencies
-
-
 def add_percentages(adata, table, ids, group_by: str, threshold: int = 0, gene_label: str = "gene"):
     """
     Add columns to existing diffxpy table specifying percentage of expressing cells

sc_toolbox/api/plot/__init__.py

@@ -251,7 +251,7 @@ def average_expression_per_cluster(
         )
 
 
-def avg_expression_split_cluster(
+def average_expression_split_cluster(
     gene_expression,
     genes,
     order,
@@ -328,7 +328,7 @@ def avg_expression_split_cluster(
         )
 
 
-def avg_expression_per_cell(
+def average_expression_per_cell(
     gene_expression,
     genes,
     order,
@@ -923,7 +923,7 @@ def colors_overview(colors: Dict, ncols: int = 2, figsize: Tuple[int, int] = (8,
     plt.show()
 
 
-def rel_frequencies_lineplot(
+def relative_frequencies_lineplot(
     relative_frequencies: pd.DataFrame,
     order,
     cluster,

sc_toolbox/api/plot/colormaps/__init__.py

@@ -1,5 +1,5 @@
 from matplotlib import colors
 
-gray_red = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "red", "darkred"], N=128)
-gray_violet = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "mediumvioletred", "indigo"], N=128)
-gray_blue = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "cornflowerblue", "darkblue"], N=128)
+grey_red = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "red", "darkred"], N=128)
+grey_violet = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "mediumvioletred", "indigo"], N=128)
+grey_blue = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "cornflowerblue", "darkblue"], N=128)