v2.0.1 first updates

Author: serjisa

docs/source/conf.py

@@ -3,11 +3,11 @@
 # -- Project information
 
 project = "scLiTr"
-copyright = "2024, Kharchenko lab, Adameyko lab"
+copyright = "2026, Kharchenko lab, Adameyko lab"
 authors = "Isaev"
 
-release = "2.0.0"
-version = "2.0.0"
+release = "2.0.1"
+version = "2.0.1"
 
 # -- General configuration ------------------------------------------------
 

sclitr/__init__.py

@@ -5,7 +5,7 @@
 from . import utils
 from . import seurat
 
-__version__ = "2.0.0"
+__version__ = "2.0.1"
 
 # Restrict top-level tab-completion to submodules only
 __all__ = [

sclitr/associations.py

@@ -628,6 +628,29 @@ def graph_associations(
     key_added: str = "X_gPCA",
 ) -> None:
     """
+    Computes the supervised principal components analysis (sPCA) of the data
+    [10.1016/j.patcog.2010.12.015]. Shortly, it finds the axes that maximize the correlation
+    between the gene expression and the graph Laplacian (one can see it as an
+    autocorrelation-aware PCA).
+
+    Parameters
+    ----------
+    adata : sc.AnnData
+        AnnData object containing the data.
+    layer : None | str
+        Layer to use for the analysis.
+    graph_key : str
+        Key to use for the graph.
+    n_pcs : int
+        Number of principal components to compute.
+    adj_method : str
+        Method for adjusting p-values.
+    key_added : str
+        Key to use for adding the results to the AnnData object.
+
+    Returns
+    -------
+    None
     """
     from scipy.sparse.linalg import eigsh
     import scipy.stats as stats

sclitr/catboost.py

@@ -17,6 +17,18 @@
 logg = sc.logging
 
 class _catboost_tqdm:
+    """
+    Custom progress bar for CatBoost training.
+
+    Parameters
+    ----------
+    total_iterations : int
+        Total number of iterations.
+    show : bool, optional
+        Whether to show the progress bar, by default True.
+    prefix : str, optional
+        Prefix for the progress bar, by default "".
+    """
     def __init__(self, total_iterations, show: bool = True, prefix: str = ""):
         if show:
             self.pbar = tqdm(

sclitr/datasets.py

@@ -33,7 +33,7 @@ def Erickson_murine_development(
     file_path: str | Path | None = None,
 ) -> AnnData:
     """
-    Dataset from [PMID: None] with clonal atlas of murine development.
+    Dataset from [PMID: 40502176] with clonal atlas of murine development.
 
     Parameters
     ----------

sclitr/eigenvalues.py

@@ -36,6 +36,16 @@
 def _tracy_widom_cdf(x: float) -> float:
     """
     Computes the CDF of the Tracy-Widom distribution (with beta = 1).
+
+    Parameters
+    ----------
+    x : float
+        Value at which to evaluate the CDF.
+
+    Returns
+    -------
+    float
+        The value of the CDF at x.
     """
     if x > 3:
         tail_prob = np.exp(-2.0/3.0 * x**1.5) / (4.0 * np.sqrt(np.pi) * x**1.5)
@@ -57,6 +67,17 @@ def _tracy_widom_cdf(x: float) -> float:
 
 def _calculate_statistics(X: np.ndarray) -> float:
     """
+    Calculates the test statistic for the Tracy-Widom distribution.
+
+    Parameters
+    ----------
+    X : np.ndarray
+        Matrix of shape (n, p) containing the data.
+
+    Returns
+    -------
+    float
+        The value of the test statistic.
     """
     n, p = X.shape
     X_centered = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
@@ -85,6 +106,28 @@ def eigenvalue_test(
 ):
     """
     Performs Johnstone’s Spiked Covariance Test to identify if the embedding is random.
+
+    Parameters
+    ----------
+    adata : sc.AnnData | np.ndarray
+        AnnData object or numpy array containing the data.
+    key : str | None
+        Key to use for AnnData input.
+    key_added : str
+        Key to use for adding the results to the AnnData object.
+    flavor : Literal["asymptotic", "synthetic"]
+        Flavor of the test to use.
+    n_simulations : int
+        Number of simulations to use for the synthetic approach.
+    progress_bar : bool
+        Whether to show a progress bar.
+    null_distribution : np.ndarray | None
+        Null distribution to use for the synthetic approach.
+
+    Returns
+    -------
+    dict
+        Dictionary containing the test statistic and p-value.
     """
     start = logg.info(f"computing eigenvalues test via {flavor} approach")
 

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = scLiTr
-version = 2.0.0
+version = 2.0.1
 author = Sergey Isaev
 author_email = sergey.isaev@meduniwien.ac.at
 description = Package for an analysis of lineage-tracing scRNA-Seq data