Merge Daniel's developmental branch

.gitattributes

@@ -0,0 +1,2 @@
+spateo/tools/database/human_GRN.csv filter=lfs diff=lfs merge=lfs -text
+spateo/tools/database/mouse_GRN.csv filter=lfs diff=lfs merge=lfs -text

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/python/black
-    rev: 22.3.0
+    rev: 23.10.1
     hooks:
       - id: black
 

README.md

@@ -7,32 +7,6 @@
 
 [Installation](https://spateo-release.readthedocs.io/en/latest/installation.html) - [Tutorials](https://spateo-release.readthedocs.io/en/latest/tutorials/index.html) - [API](https://spateo-release.readthedocs.io/en/latest/autoapi/spateo/index.html) - [Citation](https://www.biorxiv.org/content/10.1101/2022.12.07.519417v1) - [Technical](https://spateo-release.readthedocs.io/en/latest/technicals/index.html)
 
-# Installation Notes
-A few notes on installation: Spateo utilizes the `mpi4py` package and a working MPI implementation.
-This can be most easily installed by first using `conda`. There are three options for MPI implementation:
-1. OpenMPI (Linux and macOS)
-2. MPICH (Linux and macOS)
-3. Microsoft MPI (Windows)
-
-To use MPICH:
-```
-conda install -c conda-forge mpich 
-```
-
-To use OpenMPI:
-```
-conda install -c conda-forge openmpi
-```
-
-To use Microsoft MPI:
-```
-conda install -c conda-forge msmpi
-```
-
-On Linux, this can also be achieved using `sudo apt-get install libopenmpi-dev` or `sudo apt-get install mpich`.
-mpi4py can also be installed at this time using the same command, however the version Spateo uses may differ from
-the most current version. 
-
 # Citation
 Xiaojie Qiu1$\*, Daniel Y. Zhu3$, Jiajun Yao2, 4, 5, 6$, Zehua Jing2, 4,7$, Lulu Zuo8$, Mingyue Wang2, 4, 9, 10$, Kyung Hoi (Joseph) Min11, Hailin Pan2, 4, Shuai Wang2, 4, 7, Sha Liao4, Yiwei Lai4, Shijie Hao2, 4, 7, Yuancheng Ryan Lu1, Matthew Hill17, Jorge D. Martin-Rufino17, Chen Weng1, Anna Maria Riera-Escandell18, Mengnan Chen2, 4, Liang Wu4, Yong Zhang4, Xiaoyu Wei2, 4, Mei Li4, Xin Huang4, Rong Xiang2, 4, 7, Zhuoxuan Yang4, 12, Chao Liu4, Tianyi Xia4, Yingxin Liang10, Junqiang Xu4,7, Qinan Hu9, 10, Yuhui Hu9, 10, Hongmei Zhu8, Yuxiang Li4, Ao Chen4, Miguel A. Esteban4, Ying Gu2, 4,7, Douglas A. Lauffenburger3, Xun Xu2, 4, 13, Longqi Liu2, 4, 14, 15\*, Jonathan S. Weissman1,19, 20\*, Shiping Liu2, 4, 14, 15, 16\*, Yinqi Bai2, 4\*  $Co-first authors; *:Corresponding authors
 

requirements.txt

@@ -1,3 +1,4 @@
+adjustText
 anndata>=0.8.0
 colorcet>=2.0.1
 cvxopt>=1.2.3
@@ -25,6 +26,7 @@ pandas>=0.25.1
 paste-bio>=1.4.0
 plotly>=5.1.0
 POT>=0.8.1
+psutil>=5.6.3
 pynndescent>=0.4.8
 pysal>=1.14.4
 pyro-ppl

spateo/alignment/methods/paste.py

@@ -342,4 +342,4 @@ def generalized_procrustes_analysis(X, Y, pi):
     Y = R.dot(Y.T).T
     mapping_dict = {"tX": tX, "tY": tY, "R": R}
 
-    return X, Y, mapping_dict
+    return X, Y, mapping_dict

spateo/configuration.py

@@ -19,6 +19,10 @@
 from .errors import ConfigurationError
 from .logging import logger_manager as lm
 
+# Global tolerance value:
+EPS = np.finfo(float).eps
+MAX = np.finfo(np.float32).max
+
 
 class SpateoConfig:
     def __init__(

spateo/digitization/boundary_old.py

@@ -25,7 +25,6 @@ def identify_boundary(
     min_area=30,
     dilate_k_size: int = 3,
 ):
-
     lm.main_info(f"Setting up source and target area.")
     adata_tmp = adata.copy()
     adata_tmp.obs["tmp_boundary"] = 0
@@ -88,7 +87,6 @@ def boundary_gridding(
     spatial_key: str = "spatial",
     init: bool = False,
 ):
-
     bdl_seg_inner_list = []
     bdl_seg_outer_list = []
 

spateo/digitization/utils_old.py

@@ -24,7 +24,6 @@ def fill_grid_label(
     column_label_key: str = "column_label",
     init: bool = False,
 ):
-
     # mask image should be 2 pixels wider and higher, according to cv2.floodFill
     layer_grid_img = seg_grid_img.copy()
     layer_mask = np.zeros((layer_grid_img.shape[0] + 2, layer_grid_img.shape[1] + 2), dtype=np.uint8)
@@ -104,7 +103,6 @@ def draw_seg_grid(
     gridline_width=1,
     mode="grid",
 ):
-
     seg_grid_img = np.zeros_like(boundary_line_img, dtype=np.uint8)
 
     if len(bdl_seg_coor_x) != len(bdl_seg_coor_y):
@@ -187,7 +185,6 @@ def extend_layer(
     boundary_line_list,
     extend_width=10,
 ):
-
     lm.main_info(f"Generating layer area.")
     extend_layer_mask = np.zeros_like(boundary_line_img, dtype=np.uint8)
     extend_layer_img = np.zeros_like(boundary_line_img, dtype=np.uint8)
@@ -226,7 +223,6 @@ def field_contour_line(
     min_pnt,
     max_pnt,
 ):
-
     ctr_seq_rev = ctr_seq[::-1].copy()
     min_idx = ctr_seq.index(min_pnt)
     max_idx = ctr_seq.index(max_pnt) + 1

spateo/io/nanostring.py

@@ -19,7 +19,7 @@
 
 from ..configuration import SKM
 from ..logging import logger_manager as lm
-from .utils import get_points_props
+from .utils import bin_indices, get_bin_props, get_points_props
 
 try:
     import ngs_tools as ngs

spateo/io/slideseq.py

@@ -1,6 +1,6 @@
 """IO functions for Slide-seq technology.
 """
-from typing import List, NamedTuple, Optional, Union
+from typing import NamedTuple, Optional
 
 import numpy as np
 import pandas as pd
@@ -10,6 +10,7 @@
 
 from ..configuration import SKM
 from ..logging import logger_manager as lm
+from .utils import bin_indices, get_bin_props
 
 try:
     import ngs_tools as ngs

spateo/io/tenx.py

@@ -85,7 +85,7 @@ def read_10x(matrix_dir: str, positions_path: str, version: Literal["visium"] =
     SKM.init_adata_type(adata, SKM.ADATA_UMI_TYPE)
     SKM.init_uns_pp_namespace(adata)
     SKM.init_uns_spatial_namespace(adata)
-    SKM.set_uns_spatial_attribute(adata, SKM.UNS_SPATIAL_BINSIZE_KEY, binsize)
+    # SKM.set_uns_spatial_attribute(adata, SKM.UNS_SPATIAL_BINSIZE_KEY, binsize)
     SKM.set_uns_spatial_attribute(adata, SKM.UNS_SPATIAL_SCALE_KEY, scale)
     SKM.set_uns_spatial_attribute(adata, SKM.UNS_SPATIAL_SCALE_UNIT_KEY, scale_unit)
     return adata

spateo/plotting/interactive/agg.py

@@ -1,18 +1,17 @@
 """Interactive plotting functions for aggregated UMI counts.
 """
-from typing import Dict, List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
+import anndata
 import cv2
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import plotly.graph_objects as go
 from anndata import AnnData
 from matplotlib.axes import Axes
-from matplotlib.figure import Figure
 from matplotlib.widgets import PolygonSelector
-from skimage.color.colorlabel import DEFAULT_COLORS, color_dict
-from typing_extensions import Literal
+from skimage.color.colorlabel import DEFAULT_COLORS
 
 from ...configuration import SKM
 from ...errors import PlottingError

spateo/plotting/static/__init__.py

@@ -4,7 +4,6 @@
 from .align import multi_slices
 from .bbs import delaunay, polygon
 from .colorlabel import color_label
-from .contour import spatial_domains
 from .dotplot import dotplot
 from .geo import geo
 from .glm import glm_fit, glm_heatmap

spateo/plotting/static/colorlabel.py

@@ -280,3 +280,46 @@ def color_label(
     "#A4E804",
     "#324E72",
 ]
+
+# D. Zhu's color scheme for categorical data- used in plotting interaction-related things:
+interaction_colors = [
+    "#FF0000",  # Red
+    "#FF69B4",  # Hot Pink
+    "#FFF68F",  # Khaki
+    "#FF8C00",  # Dark Orange
+    "#C71585",  # Medium Violet Red
+    "#00CED1",  # Dark Turquoise
+    "#1874CD",  # Dodger Blue
+    "#8B4726",  # Saddle Brown
+    "#9400D3",  # Dark Violet
+    "#00C957",  # Medium Spring Green
+    "#8EE5EE",  # Sky Blue
+    "#CDC673",  # Dark Khaki
+    "#7CFC00",  # Lawn Green
+    "#8A2BE2",  # Blue Violet
+    "#FFD700",  # Gold
+    "#FF1493",  # Deep Pink
+    "#008000",  # Green
+    "#4682B4",  # Steel Blue
+    "#B22222",  # Fire Brick
+    "#FF00FF",  # Magenta
+    "#32CD32",  # Lime Green
+    "#1E90FF",  # Dodger Blue
+    "#FFD700",  # Gold
+    "#9AFF9A",  # Pale Green
+    "#EE0000",  # Crimson
+    "#808080",  # Gray
+    "#C1FFC1",  # Light Green
+    "#B22222",  # Fire Brick
+    "#FFFF00",  # Yellow
+    "#FFFFFF",  # White
+    "#FA8072",  # Salmon
+    "#FFC1C1",  # Light Pink
+    "#836FFF",  # Medium Slate Blue
+    "#FF4500",  # Orange Red
+    "#698B22",  # Olive Drab
+    "#7CCD7C",  # Pale Green
+    "#C6E2FF",  # Powder Blue
+    "#FFA500",  # Orange
+    "#00FFFF",  # Cyan
+]