# 2.3.7 工具 | 2021-一个很有想法的工具——Ikarus，想要在单细胞水平直接鉴定肿瘤细胞

## 前言

题目：Identifying tumor cells at the single cell level

日期：2021-10-25

期刊：biorxiv预印本

链接：<https://www.biorxiv.org/content/10.1101/2021.10.15.463909v2>

代码: [https://github.com/BIMSBbioinfo/ikarus](#qian-yan)

图表复现: [https://github.com/BIMSBbioinfo/ikarus---auxiliary](#qian-yan)

## 设计初衷

目前主要有三类细胞注释工具：

* manifold matching software：aligns the shape of an unlabeled dataset to an expertly labeled dataset
* deep learning based tools：model the batch effects through latent space embedding
* gene set based classifiers：使用已知的marker辅助分类

目前使用最多的应该是使用marker gene辅助

作者提出一个问题：能否做出一个分类器（**重点就是选出一系列基因**），可以直接从一群细胞中区分出tumor和normal？这样可以帮助肿瘤抗原的自动预测，帮助检查肿瘤细胞状态，还可以帮助诊断病理组织

## 设计思路

Ikarus工具为了检查肿瘤细胞，设计了2步：

* 首先还是根据比较靠谱的，经过专家审核的单细胞数据集，从中找到一些共有的肿瘤细胞基因signature
* 构建逻辑回归分类器 + network-based propagation of cell labels using a custom built cell-cell network

作者先整合了结直肠癌+肺癌的10X数据，其中数据注释了细胞是否是癌细胞。接下来寻找癌细胞相关的marker gene：

* 每个数据差异分析，然后取交集（**但是不同癌症的marker基因直接取交集是否有意义呢？那些特异性的gene岂不是被抛弃？**）=> 得到162 个交集基因集
* 同理选出正常组织的marker基因，它包含两部分：cell type specific markers & genes which are specifically depleted in the tumor cells

![image-20211028111814469](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-031814.png)

## 验证

找几个数据集验证一下tumor和normal 各自的基因集，找了5种癌症类型的patient-derived xenograft (PDX)、cancer cell line encyclopedia (CCLE)，发现 tumor signature score was significantly higher than the normal signature score

![image-20211028112352974](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-032353.png)

用几个测试数据集比较了不同方法区分tumor和normal细胞，包括标准的机器学习（SVM, random forest, and logistic regression）、SingleCellNet、ACTINN。发现**Ikarus的平均准确度可以达到0.98（A图），并且AUROC也很高**。

使用Lambrechts lung cancer dataset（图DE），看到Ikarus也能基本得到和作者一样的肿瘤细胞分布

![image-20211028122522894](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-042523.png)

### **测试一下如果没有足够的基因，或者缺少某几个关键基因，Ikarus还能预测准确吗**

* 图A：当然，使用的tumor gene 数量越多越准确
* 图B-D：当缺少serum amyloid A (SAA1) 和fibrinogen beta chain (FGB)，会导致准确度大大降低（**也就是说这个工具还是很依赖关键的marker gene的，因此前期还是要筛选合适的marker基因作为输入**）；当然作者说只在Lambrechts这个数据集中发现了这个情况，其他没发现

![image-20211028123705043](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-043705.png)

### **作者认为自己的signature找的很有效，于是看看它们具体有哪些特性**

发现了

* 在各个数据中，这些基因之间的Pearson相关性竟然大部分接近0
* tumor gene signature is partially related to cell cycle, and DNA replication（C图）
* tumor gene signature preferentially overlapped with the cell cycle hallmark（D图）

![image-20211028124223866](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-044223.png)

### **看一下筛出来的基因集和预后的联系**

* **more than 75%** of tumor signature genes are predictive of unfavourable prognosis in **at least one cancer** type
* 在5种癌症中（liver, renal, pancreatic, lung and endometrial）这个基因集对预后不利
* 与CNV： significantly **higher overlap with the known CNV regions** in the majority of profiled cancer types

![image-20211028125011561](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-28-045011.png)


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