Slabify-et 安装使用(https://github.com/CellArchLab/slabify-et)

Slabify是一款基于Python的命令行工具，专用于在细胞冷冻电子断层扫描（cryo-ET）三维数据中自动分割薄层切片（lamella slab）。该程序通过分析断层图像内随机采样点的局部方差来定位"目标密度"区域，提供三种操作模式：

1. 默认模式：通过迭代拟合两个平面确定薄层边界
   程序将反复优化断层顶部与底部的平面拟合，最终确定薄层的上下边界。

2. 简化模式（启用 --simple 参数）：单平面拟合后扩展厚度
   在断层中心拟合单一平面，随后沿Z轴方向扩展指定厚度（通过 --thickness 参数设置）。该模式在"复杂场景"中通常表现更优。

3. 手动模式：交互式平面拟合
   当自动处理失效时，用户可在IMOD软件中精准标记12个定位点，Slabify将根据这些点拟合定义薄层上下边界的双平面（详见操作指南）。

前两种模式采用基于RANSAC算法的稳健拟合技术，能有效抵御离群值干扰，确保边界定位的准确性。

1. Installation

在指定目录下创建虚拟环境：

conda create --prefix /path/to/your/env/slabify python=3.12

如果创建很慢，使用镜像源，修改 ~/.condarc:

show_channel_urls: true channels: - defaults - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2 - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r

成功后激活环境

conda activate slabify

或者

conda activate /path/to/your/env/slabify

安装Slabify 包

git clone https://github.com/CellArchLab/slabify-et.git cd slabify-et pip install . -i https://pypi.tuna.tsinghua.edu.cn/simple

2.Usage

Using Slabify is straightforward:

slabify --input tomogram.mrc --output tomogram_slab_mask.mrc

You may want to turn a few knobs to fine tune your results of course. Type

slabify --help

to see all options.

3. FAQ

The estimated slab is too thin, how can I improve it?

It is known that Slabify can be quite conservative in its estimation of the lamella boundaries. There are a few tips you can try to get thicker slab masks:

• The easiest way is to use the--offsetoption to arbitrarily grow your slab mask in the Z direction. Note that a negative value can be provided to make the slabthinner!
• You can increase the number of--iterations. 3 to 5 iterations are usually good.
• You can slightly decrease the--percentileof highest variance points in order to capture more information, say from 95 to 94.
• Tweaking the--n-samplesand--boxsizevalues can be beneficial in some cases (needs more testing).
• Finally, the--simplemode with an arbitrary slab--thicknessis generally a safe option.