first few 3D metrics

docs/index.rst

@@ -16,6 +16,7 @@ SegTraQ documentation
    modules/clustering_stability
    modules/nuclear_correlation
    modules/spillover_metrics
+   modules/threedimensional_metrics
 
 .. toctree::
    :maxdepth: 1

docs/modules/theedimensional_metrics.rst

@@ -0,0 +1,9 @@
+.. highlight:: shell
+
+===================================================
+The three-dimensional metrics (:code:`sp`) accessor
+===================================================
+
+The three-dimensional metrics accessor provides metrics to quantify the distribution of transcripts along the $z$-axis
+.. automodule:: segtraq.tm.threedimensional_metrics
+   :members:

src/segtraq/__init__.py

@@ -3,6 +3,6 @@
 __author__ = """Daria Lazic, Matthias Meyer-Bender, Martin Emons"""
 __email__ = "daria.lazic@embl.de, matthias.meyerbender@embl.de, martin.emons@uzh.ch"
 
-from . import bl, cs, nc, sp
+from . import bl, cs, nc, sp, tm
 
-__all__ = ["bl", "cs", "nc", "sp"]
+__all__ = ["bl", "cs", "nc", "sp", "tm"]

src/segtraq/tm/__init__.py

@@ -0,0 +1,7 @@
+from .threedimensional_metrics import (
+    shape_metrics,
+)
+
+__all__ = [
+    "shape_metrics", 
+]

src/segtraq/tm/threedimensional_metrics.py

@@ -0,0 +1,36 @@
+import numpy as np
+import pandas as pd
+import spatialdata as sd
+from scipy.stats import kurtosis, skew
+
+def shape_metrics(
+    sdata: sd.SpatialData,
+    transcript_key: str = "transcripts",
+    z_coordinate: str = "z"
+    ) -> pd.DataFrame:
+
+    """
+    Calculates shape statistics (mean, variance, skew, kurtosis) based on the first four moments of the distribution
+
+    Parameters
+    ----------
+    sdata : sd.SpatialData
+        The SpatialData object containing spatial transcriptomics data.
+    transcript_key : str, optional
+        The key in the transcript table indicating transcript identifiers. Default is "transcripts".
+    z_coordinate : str, optional
+        The coordinate name of the z coordinate
+
+
+    Returns
+    -------
+    pd.DataFrame
+        A DataFrame with keys `["mean", "variance", "skew, "kurtosis"]`
+    """
+
+
+    z = np.array(sdata.points[transcript_key][z_coordinate])
+
+    d = {"mean" : np.mean(z), "variance" : np.var(z), "skew" : skew(z), "kurtosis" : kurtosis(z)}
+    df = pd.DataFrame(data = d, index = [0])
+    return(df)

tests/tm/test_shape_metrics.py

@@ -0,0 +1,11 @@
+import pandas as pd
+
+import segtraq as st
+
+
+def test_shape_metrics_structure(sdata_new):
+    df = st.tm.shape_metrics(sdata_new)
+
+    assert isinstance(df, pd.DataFrame), f"shape_metrics should return a DataFrame, got {type(df)}"
+    expected_cols = {"mean", "variance", "skew", "kurtosis"}
+    assert set(df.columns) == expected_cols, f"Expected columns {expected_cols}, but got {set(df.columns)}"