update dependency

ChangeLog.md

@@ -3,9 +3,9 @@ v2.5.0dev
 
 ## Breaking changes:
 
-- Fragments are now stored in `.obms['fragment_single']` or `.obsm['fragment_paired']`.
-  The h5ad files generated prior to this version are no longer compatible.
-  We provide a script `scripts/XXX.py` to convert the old h5ad files to the new format.
+- Fragments are now stored in `.obms['fragment_single']` or `.obsm['fragment_paired']`,
+  depending on whether the data is single-end or paired-end.
+  As a result, the h5ad files generated prior to this version are no longer compatible.
 - `tl.call_peaks` has been renamed to `tl.macs3`, and the underlying algorithm has been
   significantly improved, see #142.
 - `pp.import_data` now doesn't compute the TSS enrichment scores. Use `metrics.tsse`

snapatac2-python/setup.py

@@ -34,15 +34,15 @@
     zip_safe=False,
     python_requires=">=3.7",
     install_requires=[
-        "anndata>=0.8.0",
+        "anndata>=0.8.0, <0.10.0",
         "kaleido",
         "multiprocess",
         "MACS3==3.0.0b3",
         "natsort",
         "numpy>=1.16.0",
         "pandas>=1.0",
         "plotly>=5.6.0",
-        "polars>=0.14.0, <=0.18.0",
+        "polars>=0.18.15, <0.20.0",
         "pooch>=1.6.0",
         "igraph>=0.10.0",
         "pynndescent",

snapatac2-python/snapatac2/tools/_call_peaks.py

@@ -81,6 +81,10 @@ def macs3(
     dict[str, 'polars.DataFrame'] | None
         If `inplace=True` it stores the result in `adata.uns[`key_added`]`.
         Otherwise, it returns the result as dataframes.
+
+    See Also
+    --------
+    merge_peaks
     """
     from MACS3.Signal.PeakDetect import PeakDetect
     from math import log
@@ -186,6 +190,10 @@ def merge_peaks(
     -------
     'polars.DataFrame'
         A dataframe with merged peaks.
+
+    See Also
+    --------
+    macs3
     """
     chrom_sizes = chrom_sizes.chrom_sizes if isinstance(chrom_sizes, Genome) else chrom_sizes
     return _snapatac2.py_merge_peaks(peaks, chrom_sizes, half_width)

snapatac2-python/snapatac2/tools/_smooth.py

@@ -3,10 +3,10 @@
 import numpy as np
 import scipy.sparse as ss
 
-from snapatac2._snapatac2 import AnnData, AnnDataSet
+import snapatac2._snapatac2 as internal
 
 def smooth(
-    adata: Union[AnnData, AnnDataSet, ss.spmatrix],
+    adata: Union[internal.AnnData, internal.AnnDataSet, ss.spmatrix],
     distances: Optional[Union[str, ss.spmatrix]] = None,
     inplace: bool = True,
 ) -> Optional[np.ndarray]:
@@ -30,7 +30,7 @@ def smooth(
 
     if distances is None: distances = "distances"
     if isinstance(str, distances): distances = adata.obsp[distances] 
-    data = adata.X[:] if isinstance(AnnData, adata) or isinstance(AnnDataSet, adata) else adata
+    data = adata.X[:] if isinstance(internal.AnnData, adata) or isinstance(internal.AnnDataSet, adata) else adata
     data = data * make_diffuse_operator(distances)
     if inplace:
         adata.X = data

snapatac2-python/src/call_peaks.rs

@@ -129,7 +129,7 @@ fn dataframe_to_narrow_peaks(df: &DataFrame) -> Result<Vec<NarrowPeak>> {
     let starts = df.column("start").unwrap().u64()?;
     let ends = df.column("end").unwrap().u64()?;
     let names = df.column("name").unwrap().utf8()?;
-    let scores = df.column("score").unwrap().i32()?;
+    let scores = df.column("score").unwrap().u16()?;
     let strands = df.column("strand").unwrap().utf8()?;
     let signal_values = df.column("signal_value").unwrap().f64()?;
     let p_values = df.column("p_value").unwrap().f64()?;