https://github.com/likelet/cfdnamodelingcode
Rscripts for analysis cfDNA methylation level in multi cancers
https://github.com/likelet/cfdnamodelingcode
Last synced: 3 months ago
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Rscripts for analysis cfDNA methylation level in multi cancers
- Host: GitHub
- URL: https://github.com/likelet/cfdnamodelingcode
- Owner: likelet
- Created: 2017-04-12T03:11:55.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-04-04T13:37:12.000Z (about 7 years ago)
- Last Synced: 2025-01-07T20:45:54.636Z (5 months ago)
- Language: R
- Size: 21.5 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# cfDNAmodelingCode
Rscripts for analysis cfDNA methylation level in multi cancers.
Currently, only code for HCC analysis was released. All analysis started with well-imputed methlyation datamatrix from sequencing platform.
# To rerun analysis
If you are intreasted in perform the similar analysis on our data or your local dataset, plz git pull the repo directly.
However, its worth noting that my code for screening methylation martker involved several random functions which may result in a slight different marker list when rerun the same analysis. The random procedure includes splitting sampleset into training and validation dataset, subsampling training data for LASSO screening and perform RandomForest variable selection. Therefore, for reproduce analysis result, it is a good option to store the workspaces as well as files and list generated in each seperated analysis to make result or variables reproductive.
If you are insist in getting exactly the analysis figures between two seprated run, here is my advice that may help you:
1. Avoiding splitting data set by random function. On contrary, you may try to divide your data set by the collection date, collection position or other information reasonblely.
2. Increasing the times of resampling as many as possible if computational resources permited.
3. avoiding the randomn function utilized in your analysis.
# SessionInfor in R env
```r
sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
locale:
[1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C LC_TIME=zh_CN.UTF-8
[4] LC_COLLATE=zh_CN.UTF-8 LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=zh_CN.UTF-8
[7] LC_PAPER=zh_CN.UTF-8 LC_NAME=zh_CN.UTF-8 LC_ADDRESS=zh_CN.UTF-8
[10] LC_TELEPHONE=zh_CN.UTF-8 LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=zh_CN.UTF-8
attached base packages:
[1] grid parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] R2HTML_2.3.2 limma_3.26.9 risksetROC_1.0.4
[4] MASS_7.3-45 RColorBrewer_1.1-2 scales_0.4.1
[7] easyGgplot2_1.0.0.9000 devtools_1.12.0 ggthemes_3.3.0
[10] ggsci_2.0 pheatmap_1.0.8 varSelRF_0.7-5
[13] randomForest_4.6-12 papeR_1.0-1 xtable_1.8-2
[16] car_2.1-3 caret_6.0-73 glmnet_2.0-5
[19] foreach_1.4.3 pROC_1.8 mice_2.25
[22] Rcpp_0.12.8 rms_5.0-0 SparseM_1.74
[25] Hmisc_4.0-0 Formula_1.2-1 lattice_0.20-34
[28] survival_2.40-1 ggplot2_2.2.0.9000 ROCR_1.0-7
[31] gplots_3.0.1 Matrix_1.2-7.1
loaded via a namespace (and not attached):
[1] splines_3.2.3 RWeka_0.4-29 gtools_3.5.0 assertthat_0.1
[5] stats4_3.2.3 latticeExtra_0.6-28 quantreg_5.29 chron_2.3-47
[9] digest_0.6.10 minqa_1.2.4 colorspace_1.3-1 sandwich_2.3-4
[13] htmltools_0.3.5 plyr_1.8.4 gmodels_2.16.2 mvtnorm_1.0-5
[17] gdata_2.17.0 lme4_1.1-12 MatrixModels_0.4-1 htmlTable_1.7
[21] tibble_1.2 mgcv_1.8-16 FSelector_0.21 TH.data_1.0-7
[25] withr_1.0.2 nnet_7.3-12 lazyeval_0.2.0 pbkrtest_0.4-6
[29] magrittr_1.5 memoise_1.0.0 polspline_1.1.12 nlme_3.1-128
[33] foreign_0.8-67 RWekajars_3.9.0-1 tools_3.2.3 data.table_1.9.6
[37] multcomp_1.4-6 stringr_1.1.0 munsell_0.4.3 cluster_2.0.5
[41] entropy_1.2.1 caTools_1.17.1 nloptr_1.0.4 iterators_1.0.8
[45] bitops_1.0-6 gtable_0.2.0 ModelMetrics_1.1.0 codetools_0.2-15
[49] reshape2_1.4.2 gridExtra_2.2.1 zoo_1.7-13 knitr_1.15.1
[53] KernSmooth_2.23-15 rJava_0.9-8 stringi_1.1.2 rpart_4.1-10
[57] acepack_1.4.1 `
```