distanX 0.1.2

A Python Package for Getting ROIs and Calculating Group Distances in Spatial Transcriptomics Data

distanX

distanX是一个用于从手绘区域中提取ROI，并计算空间转录组数据中细胞群体或ROI间距离的python包。

ENGLISH README

English 🌏

distanX is a Python package for extracting ROIs from hand-drawn regions and calculating distances between cell populations or ROIs in spatial transcriptomics data.

Highlights

• Extract ROIs from hand-drawn regions
• Fully customizable distance calculation methods, including minimum distance, average distance, maximum distance, and Chamfer distance.

Installation

pip install distanX

Usage

1. Prepare an image that uses colors to distinguish ROIs from the remaining areas. For example, you can create a new layer on a hires image, then draw closed solid areas with white background and black fill, and export only that layer.
2. Use the Curve2Line class:
   • load_and_preprocess to load the above image
   • detect_contours to detect contours
   • approximate_contours to approximate contours with line segments (epsilon_factor for approximation precision)
   • extract_polygons to extract polygons
3. Use the Line2ROI class:
   • load_adata to load spatial transcriptomics data
   • set_scalefactor to extract scale factors (can be overridden using the override_scalefactor parameter)
   • append_polygons to add polygon sets
   • extract_ROI to extract obs_names within ROIs
4. Use the CloudDistance class:
   • set_pp_distance_function to set the distance calculation method (default Euclidean distance)
   • set_cloud_distance_function to set the point cloud distance calculation method (the min、mean、max of the distance from one point to another point cloud, or a custom function, default min)
   • compute_cloud_distance to calculate the point cloud distance, return the distance from each point to another point cloud, and further calculate the distance between two point clouds

Demo

See notebook on github or nbviewer

API reference

Curve2Line

The Curve2Line class converts drawn curve regions into line-approximated polygons.

load_and_preprocess(self, image_path: str, threshold_value: int = 127) -> bool

Load and preprocess images.

• image_path: Image path
• threshold_value: Threshold value for image binarization, default 127

Returns: bool, whether successful

detect_contours(self, retrieval_mode: int = cv2.RETR_LIST) -> bool

Detect contours.

• retrieval_mode: Contour retrieval mode

Returns: bool, whether successful

approximate_contours(self, epsilon_factor: float = 0.002) -> bool

Approximate contours with polygons.

• epsilon_factor: Approximation precision

Returns: bool, whether successful

extract_polygons(self) -> list[list[tuple[int, int]]]

Output identified hand-drawn regions.

Returns: list[list[tuple[int, int]]], list of polygons

Line2ROI

The Line2ROI class converts line-approximated polygons into ROIs in spatial transcriptomics data.

load_adata(self, adata: ad.AnnData)

Load spatial transcriptomics data.

• adata: Spatial transcriptomics data

set_scalefactor(self, image_path: str, library_id: str | None = None, reference_image_key: str = 'hires', override_scalefactor: float | None = None)

Set the scale factor between hand-drawn images and adata.obsm['spatial'].

• image_path: Hand-drawn image path
• library_id: library_id in spatial transcriptomics data, defaults to the first one
• reference_image_key: Reference image in spatial transcriptomics data, used to set the scale factor of hand-drawn images in conjunction with its scale factor
• override_scalefactor: Directly override the scale factor of hand-drawn images

append_polygons(self, polygons: list[list[tuple[int, int]]], ROI_name: str)

Add hand-drawn region sets.

• polygons: Hand-drawn region sets returned by Curve2Line().extract_polygons()
• ROI_name: Name of the ROI to add to

extract_ROI(self, ROI_name: str, method: str = 'winding number') -> list[str]

Export obs_names of specified ROI.

• ROI_name: Name of specified ROI
• method: Method to determine if a point is within ROI, currently only supports winding number

Returns: list[str], list of ROI obs_names

set_adata_ROI(self, ROI_name: str) -> ad.AnnData

Set obs['ROI_'+ROI_name] within ROI to True, others to False.

• ROI_name: Name of specified ROI

CloudDistance

The CloudDistance class calculates point cloud distances between cell populations or ROIs.

set_pp_distance_function(self, pp_distance_function: Callable[[float, float, float, float], float])

Set the distance calculation method between two points, default is Euclidean distance.

• pp_distance_function: Function to calculate distance between two points (x1,y1) and (x2,y2), input is (x1,y1,x2,y2), output is float

set_cloud_distance_function(self, cloud_distance_function: Union[Literal['min', 'mean', 'max'], Callable])

Set the point cloud distance calculation method, default is min.

• cloud_distance_function: Function to calculate distance from a point in one class to another point cloud, options are min(np.min), mean(np.mean), max(np.max) or custom function, this function should operate on a one-dimensional array

compute_distance_matrix(self, adata: ad.AnnData, class_key_1: str | None = None, class_name_1: str | None = None, class_key_2: str | None = None, class_name_2: str | None = None) -> pd.DataFrame

Calculate distance matrix between two point clouds.

• adata: Spatial transcriptomics data
• class_key_1: First class
• class_name_1: First class name
• class_key_2: Second class
• class_name_2: Second class name

Returns: pd.DataFrame, distance matrix between two point clouds, row index is the first class, column index is the second class, values are distances between two points

compute_cloud_distance(self, on: Literal['class_1', 'class_2'] = 'class_1')

Calculate point cloud distance.

• on: Set to calculate distance from individual points in which class to all points in another class, default is the first class

Returns: Generally numpy.ndarray, custom distance from each point in the class specified by on to all points in another class

extract_points(self, adata: ad.AnnData, class_key: str, class_name: str) -> pd.DataFrame

Extract points and coordinates of specified category.

• adata: Spatial transcriptomics data
• class_key: Specified category
• class_name: Classification name within specified category

Returns: pd.DataFrame, points and coordinates of specified category, row index is adata.obs_names, column index is x, y

亮点

• 从手绘区域中提取ROI
• 完全可自定义的距离计算方法，可实现最小距离、平均距离、最大距离以及Chamfer距离等。

安装

pip install distanX

用法

1. 准备一张用颜色区分出ROI和剩余区域的图像，例如可以在hires图像上新建新图层，然后绘制白底、黑色的封闭实心区域，然后只导出该图层。
2. 使用Curve2Line类
   • load_and_preprocess加载上述图像
   • detect_contours检测轮廓
   • approximate_contours用线段近似轮廓（epsilon_factor为近似精度）
   • extract_polygons提取多边形
3. 使用Line2ROI类
   • load_adata加载空转数据
   • set_scalefactor提取缩放因子（可以使用override_scalefactor参数覆盖）
   • append_polygons添加多边形集
   • extract_ROI提取ROI中的obs_names
4. 使用CloudDistance类
   • set_pp_distance_function设置两点间距离计算方法（默认欧几里得距离）
   • set_cloud_distance_function设置点云距离计算方法（类中一点到另一点云距离的min、mean、max或自定义函数，默认min）
   • compute_cloud_distance计算点云距离，返回每点到另一个点云的距离（默认从第一类中的点到第二类中的点云），可以进一步计算两点云距离

示例

在github或nbviewer 中查看notebook。

API参考

Curve2Line

Curve2Line类将绘制的曲线区域转换为直线近似的多边形。

load_and_preprocess(self, image_path: str, threshold_value: int = 127) -> bool

加载、预处理图像。

• image_path: 图像路径
• threshold_value: 阈值，用于二值化图像，默认127

返回值：bool，是否成功

detect_contours(self, retrieval_mode: int = cv2.RETR_LIST) -> bool

检测轮廓。

• retrieval_mode: 轮廓检索模式

返回值：bool，是否成功

approximate_contours(self, epsilon_factor: float = 0.002) -> bool

用多边形近似轮廓。

• epsilon_factor: 近似精度

返回值：bool，是否成功

extract_polygons(self) -> list[list[tuple[int, int]]]

输出识别出的手绘区域。

返回值：list[list[tuple[int, int]]]，多边形列表

Line2ROI

Line2ROI类将直线近似的多边形转换为空转数据中的ROI。

load_adata(self, adata: ad.AnnData)

加载空转数据。

• adata: 空转数据

set_scalefactor(self, image_path: str, library_id: str | None = None, reference_image_key: str = 'hires', override_scalefactor: float | None = None)

设置手绘图像与adata.obsm['spatial']的缩放因子。

• image_path: 手绘图像路径
• library_id: 空转数据中的library_id，默认使用第一个
• reference_image_key: 空转数据中的参考图像，用于配合其缩放因子设置手绘图像的缩放因子
• override_scalefactor: 直接覆写手绘图像的缩放因子

append_polygons(self, polygons: list[list[tuple[int, int]]], ROI_name: str)

添加手绘区域集合。

• polygons: Curve2Line().extract_polygons()返回的手绘区域集合
• ROI_name: 添加到的ROI的名称

extract_ROI(self, ROI_name: str, method: str = 'winding number') -> list[str]

导出指定ROI的obs_names。

• ROI_name: 指定ROI的名称
• method: 判断点是否在ROI中的方法，暂时只支持winding number

返回值：list[str]，ROI的obs_names列表

set_adata_ROI(self, ROI_name: str) -> ad.AnnData

将ROI内的obs['ROI_'+ROI_name]设置为True，其余设置为False。

• ROI_name: 指定ROI的名称

CloudDistance

CloudDistance类计算细胞群体或ROI间的点云距离。

set_pp_distance_function(self, pp_distance_function: Callable[[float, float, float, float], float])

设置两点间距离计算方法，默认是欧几里得距离。

• pp_distance_function: 计算两点(x1,y1)、(x2,y2)间距离的函数，输入为(x1,y1,x2,y2)，输出为float

set_cloud_distance_function(self, cloud_distance_function: Union[Literal['min', 'mean', 'max'], Callable])

设置点云距离计算方法，默认是min。

• cloud_distance_function: 计算类中一点到另一点云距离的函数，可选min(np.min)、mean(np.mean)、max(np.max)或自定义函数，该函数应当对一个一维数组进行操作

compute_distance_matrix(self, adata: ad.AnnData, class_key_1: str | None = None, class_name_1: str | None = None, class_key_2: str | None = None, class_name_2: str | None = None) -> pd.DataFrame

计算两点云距离矩阵。

• adata: 空转数据
• class_key_1: 第一类
• class_name_1: 第一类名称
• class_key_2: 第二类
• class_name_2: 第二类名称

返回值：pd.DataFrame，两点云距离矩阵，行索引为第一类，列索引为第二类，值为两点距离

compute_cloud_distance(self, on: Literal['class_1', 'class_2'] = 'class_1')

计算点云距离。

• on: 设置计算从哪类的单个点到另一类所有点的距离，默认是第一类

返回值：一般是numpy.ndarray，是on指定类别中的各个点到另一类所有点的自定义距离

extract_points(self, adata: ad.AnnData, class_key: str, class_name: str) -> pd.DataFrame

提取指定类别的点及坐标。

• adata: 空转数据
• class_key: 指定类别
• class_name: 指定类别中的分类名称

返回值：pd.DataFrame，指定类别的点及坐标，行索引为adata.obs_names，列索引为x、y

引用distanX

如果你觉得distanX对你的工作有帮助，欢迎引用它：

@misc{distanX,
  author = {kusurin},
  title = {distanX: A Python Package for Getting ROIs and Calculating Group Distances in Spatial Transcriptomics Data},
  howpublished = {Github},
  year = {2025},
  url = {https://github.com/kusurin/distanX}
}