Step1——基于GENIE3包的潜在TF-靶基因网络识别

Last updated: 2021-08-18

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GENIE3 package; 基因co-expression关系。

一、代码

##==转录调控网络推断==##
## 1.基因过滤
#过滤标准是基因表达量之和>细胞数*3%，且在1%的细胞中表达
genesKept <- geneFiltering(exprMat, scenicOptions, 
              minCountsPerGene = 3 * 0.01 * ncol(exprMat),  # 每个基因的最低count数
              minSamples = ncol(exprMat) * 0.01) # 至少在1%细胞内检测到
exprMat_filtered <- exprMat[genesKept, ]
## 2.计算相关性矩阵，并将结果保存值int目录下。
runCorrelation(exprMat_filtered, scenicOptions)
## 3. TF-Targets相关性回归分析,并将结果保存至int目录下。
exprMat_filtered_log <- log2(exprMat_filtered+1)
runGenie3(exprMat_filtered_log, scenicOptions, nParts = 20)
#这一步消耗的计算资源非常大，个人电脑需要几个小时的运行时间
## 4. 推断共表达模块
runSCENIC_1_coexNetwork2modules(scenicOptions)

二、分析过程解析

（一）输入数据

表达矩阵。【优先count矩阵（read count或UMI count均可）；TPM或FPKM/RPKM也可】

（二）数据分析处理环节及对应的实现方式：

过滤低表达水平的基因。
计算相关性矩阵：所有基因对之间的spearman correlation。

并将结果储存至./int/1.2_corMat.Rds。数据内容如下：

涉及8810个基因，8810个基因✖️8810个基因的speaman correaltion coefficient

涉及8810个基因，8810个基因✖️8810个基因的speaman correaltion coefficient。

调用GENIE3，进行TF-Targets相关性回归分析【核心步骤】。

并将最终结果保存至./int/1.4_GENIE3_linkList.Rds

1.3 为中间结果。20个文件对应nPart

1.3 为中间输出结果，20个.Rds文件对应参数nParts = 20，相当于把表达矩阵拆成20个部分进行分析。

1.4 为1.3结果的整合。内容如下：

TF，转录因子名称；Target —— 潜在靶基因的名字； weight ——TF与Target之间的相关性权重【这个概念需要重点理解】。

将GENIE3/GRNBoost结果转化为co-expression module.

计算过程基于1.4文件的结果，即TF与每一个潜在target gene的相关性。处理过程涉及对低相关性共表达TF-target的过滤，作者尝试了多种策略，研究发现没有一种最佳策略，因此建议6种过滤标准均使用。（默认使用6种方法）。6种方法分别为：

①w001：以每个TF为核心保留weight>0.001的基因形成共表达模块；

②w005：以每个TF为核心保留weight>0.005的基因形成共表达模块；

③top50：以每个TF为核心保留weight值top50的基因形成共表达模块；

④top5perTarget：每个基因保留weight值top5的TF得到精简的TF-Target关联表，然后把基因分配给TF构建共表达模块；

⑤top10perTarget：每个基因保留weight值top10的TF得到精简的TF-Target关联表，然后把基因分配给TF构建共表达模块；

⑥top50perTarget：每个基因保留weight值top50的TF得到精简的TF-Target关联表，然后把基因分配给TF构建共表达模块；

并将最终结果保存至./int/1.6_tfModules_asDF.Rds。数据内容如下：

三、总结

以上运行结束后，会在SCENIC/int目录下生成以下rds格式数据。

第一步得到的final output: 1.6_tfModules_asDF.Rds 1. Target与TF：潜在的转录因子与靶基因对 2. method：筛选方法。六种筛选标准的解释见上。 3. corr：pearson correlation。 a. 基于基因的co-expression计算得出; b. 1.代表潜在激活效应；0代表无效应；-1代表潜在抑制效应。

小结：进入后续分析数据以“基因集”为单位进行分析。基因集的划分依据： 1. split by TF; 2. split by corr。最终进入后续分析的基因集满足以下两个条件：1. 通过上述1-6种过滤标准后基因数仍>20；2. corr =1。即只考虑TF共表达基因集。

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
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 [5] rlang_0.4.11      fansi_0.5.0       stringr_1.4.0     tools_4.1.0      
 [9] xfun_0.25         utf8_1.2.2        git2r_0.28.0      jquerylib_0.1.4  
[13] htmltools_0.5.1.1 ellipsis_0.3.2    rprojroot_2.0.2   yaml_2.2.1       
[17] digest_0.6.27     tibble_3.1.3      lifecycle_1.0.0   crayon_1.4.1     
[21] later_1.2.0       sass_0.4.0        vctrs_0.3.8       promises_1.2.0.1 
[25] fs_1.5.0          glue_1.4.2        evaluate_0.14     rmarkdown_2.10   
[29] stringi_1.7.3     bslib_0.2.5.1     compiler_4.1.0    pillar_1.6.2     
[33] jsonlite_1.7.2    httpuv_1.6.1      pkgconfig_2.0.3