multi_cox.r 基因表达量做多因素cox分析

multi_cox.r 多因素cox分析

使用方法:

指定多个基因表达量做多因素cox回归分析,并构建预后模型,以及评估预后模型:


$Rscript $scriptdir/multi_cox.r -h
usage: /share/nas1/huangls/test/TCGA_immu/scripts/multi_cox.r
       [-h] -i data -t time -e event -v variate [variate ...]
       [-P predict.time [predict.time ...]] [-c cut.score] [-s seed]
       [-o outdir] [-p prefix]
multi variate cox regression analysis using gene expression
optional arguments:
  -h, --help            show this help message and exit
  -i data, --data data  input data file path[required]
  -t time, --time time  set suvival time column name [required]
  -e event, --event event
                        set event column name must 0 or 1 code format
                        [required]
  -v variate [variate ...], --variate variate [variate ...]
                        variate for cox analysis [required]
  -P predict.time [predict.time ...], --predict.time predict.time [predict.time ...]
                        Time point to draw the ROC curve [default 365 1095
                        1825]
  -c cut.score, --cut.score cut.score
                        set cut score value to divide high and low risk groups
                        [default median]
  -s seed, --seed seed  set random seed [default 2021]
  -o outdir, --outdir outdir
                        output file directory [default cwd]
  -p prefix, --prefix prefix
                        out file name prefix [default cox]

使用举例:



Rscript $scriptdir/multi_cox.r -i imm.unicox.metadata-exp.tsv -e EVENT -t TIME \
    -v PDGFRL CXCR4 PAK3 CSF1R PDCD1 -P 365 1095 1825 \
    -o multicox   -p  multicox

参数说明:

-i 输入生存数据与基因表达文件 


barcodeTIMEEVENTFGRCD38ITGALCX3CL1CEACAM21MATKCD79BMMP25
TCGA-B7-A5TK-01A-12R-A36D-31288016.3440886.8677240.26903603.01321.8685362.283423.45319813.72829
TCGA-BR-7959-01A-11R-2343-131010011.9673915.794517.35856626.913532.5719170.8641161.8799573.451148
TCGA-IN-8462-01A-11R-2343-1357205.3508463.1113423.76912520.222380.6108390.5197762.8221921.106563
TCGA-CG-4443-01A-01R-1157-1391201.538020.8629552.3735119.040971.0921270.7603481.9265920.878735
TCGA-KB-A93J-01A-11R-A39E-311124015.2401613.304738.0859114.152953.4835593.1929513.65174210.43186
TCGA-HU-A4H3-01A-21R-A251-3188206.2617612.6751737.0258864.0502710.5841591.0393361.9792142.312993
TCGA-RD-A8MV-01A-11R-A36D-313720027.0741520.1588534.9130934.718214.1131122.61555716.5194617.72674
TCGA-VQ-A91X-01A-12R-A414-3128911.0623410.7520182.3805134.4158150.5181420.2121971.2392030.582114


结果展示:


预后模型构建:

The Risk score was calculated with the following formula: The  risk score=


attachments-2021-06-PCuozLir60d59d023b1ad.png

, where Expri represents the expression level of gene i and coefi represents the regression coefficient of gene i in the signature.


风险评分

根据模型计算各样本分风险值,按照风险值的中位数将样本划分为高低风险组,分别绘制风险值分布散点图,生存时间散点图,signature基因表达热图。
attachments-2021-07-pixMJ7Ct60dd1ab017657.png

模型预测预后差异

高低风险组预后差异分析:绘制Kaplan-Meier生存曲线,并用Log Rank法检验两组的生存率是否有差异。

attachments-2021-07-kmtIVrSl60dd1ace16f74.png




模型预测性能评估

模型的好坏可以从区分度(Discrimination)和一致性(Calibration)两方面考虑。区分度主要用于反映预测模型的区分能力,是评估模型有多大把握确定它所预测的患者发生该事件的能力。一致性指结局实际发生的概率和预测的概率的一致性或者接近程度。前者可通过ROC曲线下面积(AUC)或C统计量来评价,后者可通过校准图来评价。以下为模型ROC曲线:


attachments-2021-07-S5ZQDzHc60dd1a799c093.png

To reflect the prediction ability of the XXXX‐based risk signature, we generated the time-dependent receiver operating characteristic curve (ROC) and calculated the area under the curve (AUC)  (R package “survivalROC” ) for 1-year, 3-year, and 5-year overall survival (OS). The Kaplan-Meier, log‐rank, ROC curve, and calibration analyses were all performed and visualized by the “survivalROC”, “rms”, “survival”, and “survminer” packages.


脚本获取与使用课程:https://study.163.com/course/introduction/1211864801.htm?share=1&shareId=1030291076

  • 发表于 2021-08-30 16:54
  • 阅读 ( 626 )
  • 分类:TCGA

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