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R语言的univariate_cox_batch.r怎么用

发布时间:2022-03-21 10:51:14 来源:亿速云 阅读:191 作者:iii 栏目:开发技术

本文小编为大家详细介绍“R语言的univariate_cox_batch.r怎么用”,内容详细,步骤清晰,细节处理妥当,希望这篇“R语言的univariate_cox_batch.r怎么用”文章能帮助大家解决疑惑,下面跟着小编的思路慢慢深入,一起来学习新知识吧。

univariate_cox_batch.r  基因表达量批量单因素cox回归分析

使用说明:

输入生存数据与基因表达量可以做批量单因素cox回归分析

$ Rscript $scriptdir/univariate_cox_batch.r --h
usage:  univariate_cox_batch.r [-h] -m metadata -g
                                                         expset [-t time]
                                                         [-e event]
                                                         [-l pvalue]
                                                         [-b blocksize]
                                                         [--log2] [-o outdir]
                                                         [-p prefix]
batch unvariate cox regression gene expression
optional arguments:
  -h, --help            show this help message and exit
  -m metadata, --metadata metadata
                        input metadata file path with suvival time [required]
  -g expset, --expset expset
                        input gene expression set file [required]
  -t time, --time time  set suvival time column name in metadata [default
                        TIME]
  -e event, --event event
                        set event column name in metadata [default EVENT]
  -l pvalue, --pvalue pvalue
                        pvalue cutoff to choose sig gene [default 0.01]
  -b blocksize, --blocksize blocksize
                        Number of variables Parallel to test in each [default
                        2]
  --log2                whether do log2 transfrom for expression data
                        [optional, default: False]
  -o outdir, --outdir outdir
                        output file directory [default cwd]
  -p prefix, --prefix prefix
                        out file name prefix [default cox]

参数说明:

-m 输入生存数据:

event 列: 0表示事件没有发生,1表示事件发生;  0表示alive,1表示死亡;

barcodeTIMEEVENT
TCGA-B7-A5TK-01A-12R-A36D-312880
TCGA-BR-7959-01A-11R-2343-1310100
TCGA-IN-8462-01A-11R-2343-135720
TCGA-CG-4443-01A-01R-1157-139120
TCGA-KB-A93J-01A-11R-A39E-3111240
TCGA-HU-A4H3-01A-21R-A251-318820
TCGA-RD-A8MV-01A-11R-A36D-3137200
TCGA-VQ-A91X-01A-12R-A414-312891
TCGA-D7-8575-01A-11R-2343-135541
TCGA-BR-8485-01A-11R-2402-132800
TCGA-D7-A748-01A-12R-A32D-311321
TCGA-VQ-A91Z-01A-11R-A414-3116900

-g 输入基因表达量文件

IDTCGA-B7-A5TK-01A-12R-A36D-31TCGA-BR-7959-01A-11R-2343-13TCGA-IN-8462-01A-11R-2343-13TCGA-BR-A4CR-01A-11R-A24K-31TCGA-CG-4443-01A-01R-1157-13TCGA-KB-A93J-01A-11R-A39E-31TCGA-BR-4371-01A-01R-1157-13TCGA-IN-A6RO-01A-12R-A33Y-31TCGA-HU-A4H3-01A-21R-A251-31
FGR16.3440811.967395.3508462.2093511.5380215.240164.5011182.6024376.261761
CD3886.8677215.794513.1113421.2407070.86295513.30473.7287081.6739522.675173
ITGAL40.269037.3585663.7691252.3878692.3735138.085918.3052833.6227817.025886
CX3CL1603.013226.9135320.222384.19526219.0409714.1529513.758856.6753744.050271
CEACAM211.8685362.5719170.6108390.6745581.0921273.4835591.1343094.4712740.584159
MATK2.283420.8641160.5197762.4420930.7603483.1929511.1618810.3478821.039336
CD79B3.4531981.8799572.8221920.5235871.9265923.6517420.8312880.8836431.979214
MMP2513.728293.4511481.1065631.1312170.87873510.431861.4758521.9142842.312993
TRAF3IP35.244011.8801860.8752640.7561530.6032513.3250132.3474730.5704621.315916
CD477.7469151.8371922.7707611.0781135.20445122.557831.1010715.0661915.41347
BTK6.8562354.3622611.4826881.3715991.9812366.911543.1878480.9554991.48269
FMO17.1685677.7118173.2231740.9790340.4503071.0934121.0018080.9102041.558515
SYT71.15310581.940682.673384191.611282.493940.5103734.4704821.285060.91944
TYROBP591.7796338.0271184.813369.18483150.6397480.5691121.09672.4588116.9793
CD220.8192952.5216071.5885050.412590.3872881.1236330.4882440.2580940.713988


使用举例:

Rscript $scriptdir/univariate_cox_batch.r -m metadata_survival_time.tsv \
   -g deg_gene_exp_tpm.tsv  -e EVENT -t TIME -p  imm.unicox  --pvalue 0.01

批量cox分析 结果:

VariableTermBetaStandardErrorZPLRTWaldLogRankHRHRlowerHRupper
SYT12SYT120.0911210.0194954.6740352.95E-060.0001282.95E-061.80E-061.0954021.0543361.138067
CDH2CDH20.0132660.0030144.4018031.07E-050.0019931.07E-052.11E-071.0133541.0073861.019357
GPNMBGPNMB0.0027590.0007683.5901180.0003310.0013130.0003310.0004091.0027621.0012531.004274
TMIGD3TMIGD30.067880.0193143.5144680.0004410.0012480.0004410.0004191.0702371.030481.111528
LINC01094LINC010940.1324410.0403753.2802930.0010370.0022420.0010370.0010261.1416111.0547541.235621
SLC22A20PSLC22A20P0.0494150.015833.121650.0017980.0120650.0017980.0007361.0506561.0185591.083765
IGHV4-61IGHV4-610.0017910.0005823.0775730.0020870.008590.0020870.0017421.0017931.0006511.002937
IGHV2-5IGHV2-50.0022360.0007373.0349610.0024060.0077820.0024060.0022051.0022381.0007921.003686
SERPINA5SERPINA50.0076810.0025583.0024280.0026780.0097990.0026780.0020641.0077111.002671.012776
MS4A4AMS4A4A0.014460.005052.8632180.0041940.0084270.0041940.0047221.0145651.0045721.024657
FAM83AFAM83A0.0062370.0023682.6334450.0084520.0285230.0084520.0072951.0062561.0015961.010938
IGLV3-9IGLV3-90.0005470.000212.6082810.00910.0394270.00910.0102441.0005471.0001361.000958
STARD3STARD30.0009280.0003582.5889130.0096280.0302580.0096280.0067431.0009281.0002251.001631

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