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

barcode	TIME	EVENT	FGR	CD38	ITGAL	CX3CL1	CEACAM21	MATK	CD79B	MMP25
TCGA-B7-A5TK-01A-12R-A36D-31	288	0	16.34408	86.86772	40.26903	603.0132	1.868536	2.28342	3.453198	13.72829
TCGA-BR-7959-01A-11R-2343-13	1010	0	11.96739	15.79451	7.358566	26.91353	2.571917	0.864116	1.879957	3.451148
TCGA-IN-8462-01A-11R-2343-13	572	0	5.350846	3.111342	3.769125	20.22238	0.610839	0.519776	2.822192	1.106563
TCGA-CG-4443-01A-01R-1157-13	912	0	1.53802	0.862955	2.37351	19.04097	1.092127	0.760348	1.926592	0.878735
TCGA-KB-A93J-01A-11R-A39E-31	1124	0	15.24016	13.3047	38.08591	14.15295	3.483559	3.192951	3.651742	10.43186
TCGA-HU-A4H3-01A-21R-A251-31	882	0	6.261761	2.675173	7.025886	4.050271	0.584159	1.039336	1.979214	2.312993
TCGA-RD-A8MV-01A-11R-A36D-31	3720	0	27.07415	20.15885	34.91309	34.71821	4.113112	2.615557	16.51946	17.72674
TCGA-VQ-A91X-01A-12R-A414-31	289	1	1.062341	0.752018	2.380513	4.415815	0.518142	0.212197	1.239203	0.582114

结果展示：

预后模型构建：

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

, 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。

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。

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：

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 ( 4546 )
分类：TCGA