Step1——基于GENIE3包的潜在TF-靶基因网络识别
Last updated: 2021-08-20
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Knit directory: SCENIC_pipeline/
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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：①
w001：以每个TF为核心保留weight>0.001的基因形成共表达模块；②
w005：以每个TF为核心保留weight>0.005的基因形成共表达模块；③
top50：以每个TF为核心保留weight值top50的基因形成共表达模块；④
top5perTarget：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；⑤
top10perTarget：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；⑥
top50perTarget：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 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936
[2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
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):
[1] Rcpp_1.0.7 whisker_0.4 knitr_1.33 magrittr_2.0.1
[5] R6_2.5.0 rlang_0.4.11 fansi_0.5.0 stringr_1.4.0
[9] tools_4.0.2 xfun_0.24 utf8_1.2.1 git2r_0.28.0
[13] jquerylib_0.1.4 htmltools_0.5.1.1 ellipsis_0.3.2 rprojroot_2.0.2
[17] yaml_2.2.1 digest_0.6.27 tibble_3.1.2 lifecycle_1.0.0
[21] crayon_1.4.1 later_1.2.0 sass_0.4.0 vctrs_0.3.8
[25] promises_1.2.0.1 fs_1.5.0 glue_1.4.2 evaluate_0.14
[29] rmarkdown_2.9 stringi_1.5.3 bslib_0.2.5.1 compiler_4.0.2
[33] pillar_1.6.1 jsonlite_1.7.2 httpuv_1.6.1 pkgconfig_2.0.3