# 2.3.6 工具 | 2021-MACA: 一款自动注释细胞类型的工具

## 前言

题目：MACA: Marker-based automatic cell-type annotation for single cell expression data

日期：2021-10-25

期刊：biorxiv预印本

链接：<https://www.biorxiv.org/content/10.1101/2021.10.25.465734v1>

GitHub：<https://github.com/ImXman/MACA>

## 设计的初衷

目前细胞类型鉴定工具中，Support Vector Machine(SVM) 的准确性超过大多数监督注释方法

> “Support Vector Machine” (SVM) is a **supervised machine learning** algorithm that can be used for both classification or regression challenges. However, it is mostly used in classification problems.

但是，由于监督注释方法在大多数单细胞数据中缺乏真实参照，所以易用性没有非监督方法好，这也是目前非监督方法占主流的原因之一。既然使用非监督方法，那么就需要人工介入，调整分群的分辨率，以及提供marker基因，这样带来的问题就是挑选marker基因耗费时间，并且重复性差（因为每个人选择的marker基因也不同）。

基于第一个问题（挑选marker基因耗费时间），有人做了数据库，专门收录这些基因，比如PanglaoDB, CellMarker涵盖了人和小鼠多种细胞类型的marker 基因；另外NeuroExpresso是大脑组织的marker基因数据库。

这个工具MACA，全称是marker-based automatic cell-type annotation，旨在解决细胞注释的速度和准确性

## 设计的结构

> 整个设计逻辑还是很容易理解的：先判断单个细胞属于什么类型，然后聚类再判断一次属于什么类型

读入数据：single cell或者single nuclei RNA-Seq的表达矩阵

对每个细胞计算2个label：

* 首先，结合marker数据库，使用raw count表达矩阵，计算每个细胞的cell-type score =》 将 gene expression matrix转换成cell-type score matrix
* 然后，对每个细胞分配细胞类型：将score最大的细胞类型对应到这个细胞 =》 Label 1 产生
* 同时，利用cell-type score matrix + Louvain community detection algorithm，将细胞聚类 =》 Label 2 产生（也就是某群细胞属于什么类型）

因为一开始不知道具体有几种细胞类型，这里**MACA默认将分辨率调大**，避免很多同源的细胞被拆分成很多小的cluster

之后就是通过一系列统计知识，将Label1 和Label2利用起来：MACA records **significant or at least the top-3 celltypes for each cell** in cluster based on cell-type scores

意思就是挑出个人很能打，并且整体也很符合要求的那组

![image-20211027151738178](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-27-071738.png)

## 实际测试

### **使用的方法、数据**

* investigated **4 scoring methods** that have been proposed to transform gene expression matrix to cell-type score matrix
* 2 public marker databases
* 6 single cell studies comprised of 3000 to 20000 cells
* 使用Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI)进行评测

> ARI & NMI are measuring similarity or agreement between our annotations and authors’ annotations

### **评测打分工具**

发现全部6个数据集中 annotations using **PlinerScore** with markers in **PanglaoDB** have the largest agreement。于是采用PlinerScore作为打分方法

### **接下来和自动细胞注释工具比较**

利用PanglaoDB的marker，和CellAssign, SCINA, Cell-ID, and scCATCH比较

发现速度差异：

* **MACA** can finish annotation **within 1 minute (cells around 3,000)** and **less than 2 minutes** for a relatively large dataset (cells up to **20,000** cells)
* scCATCH and Cell-ID took longer than MACA
* SCINA took around 20-minute for a large dataset
* CellAssign took the longest time with > 20,000 cells due to lack of memory

发现注释结果差异：

* **MACA** labels cells had a higher consensus than CellAssign, SCINA, Cell-ID, and scCATCH
* 和作者的结果相似：**MACA and scCATCH** identify similar numbers of cell-types to author’s annotations


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