Ns126:Calendar/NOTES/2015-4-21: Difference between revisions
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>Shicheng No edit summary |
>Shicheng No edit summary |
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***sample size | ***sample size | ||
***clinical information: gender, age | ***clinical information: gender, age | ||
tar xvf 08177614-f305-4fbf-84ca-fd2fbfe26755.tar # | |||
tar xvf 548dd1cf-84a1-4d96-86f0-b47d6300daa6.tar # | |||
tar xvf 9cd95b1c-782c-478d-9ec9-de7c9c441cc4.tar # | |||
tar xvf c5da6cd3-0266-4ebb-bd11-713ffe9b5ef9.tar # | |||
cd /home/sguo/monod/data/coad/DNA_Methylation/JHU_USC__HumanMethylation450/Level_3 |
Revision as of 21:42, 21 April 2015
- Now, I do know how to insert figures, but I don't know how to insert tables into MediaWiki. Therefore, I write a small code to transfer routine table to wikitable.
- I must say: mediawiki is really a excellent tool to manage the lab and monitor the progress or task to help us complete the project quickly.
perl table2wikitable.pl input.table.txt
#!/usr/bin/perl #table2wikitable.pl use strict; use Cwd; chdir getcwd; my $input=@ARGV[0]; open F,$input; print "{| class=\"wikitable\" style=\"text-align: right; color: red;\"\n"; while(<F>){ chomp; my @line=split /\t/; my $tmp=join("||",@line); print "| $tmp\n|-\n"; } print"|}\n";
- corresponding table is as the following. Pan-Cancer Methylation 450K dataset were collected from TCGA Project to identify optional padloc regions.
Symbol | Case | Control | Cancer | 肿瘤名称 |
KIRC | 160 | 160 | Kidney renal clear cell carcinoma | 肾透明细胞癌 |
BRCA | 92 | 92 | Breast invasive carcinoma | 浸润性乳腺癌 |
THCA | 56 | 56 | Thyroid carcinoma | 甲状腺癌 |
HNSC | 50 | 50 | Head and Neck squamous cell carcinoma | 头颈部鳞状细胞癌 |
PRAD | 49 | 49 | Prostate adenocarcinoma | 前列腺癌 |
LIHC | 49 | 49 | Liver hepatocellular carcinoma | 肝癌 |
KIRP | 45 | 45 | Kidney renal papillary cell carcinoma | 肾乳头状细胞癌 |
LUSC | 41 | 41 | Lung squamous cell carcinoma | 肺腺癌 |
COAD | 39 | 39 | Colon adenocarcinoma | 结肠癌 |
UCEC | 30 | 30 | Uterine Corpus Endometrial carcinoma | 子宫内膜癌 |
LUAD | 26 | 26 | Lung adenocarcinoma | 肺鳞癌 |
- In order to publish MONOD paper to a good paper quickly. 1) fast 2) large data
- collect methylation 450 data of cancer tissues and normals from TCGA Project
- sample size
- clinical information: gender, age
- collect methylation 450 data of cancer tissues and normals from TCGA Project
tar xvf 08177614-f305-4fbf-84ca-fd2fbfe26755.tar # tar xvf 548dd1cf-84a1-4d96-86f0-b47d6300daa6.tar # tar xvf 9cd95b1c-782c-478d-9ec9-de7c9c441cc4.tar # tar xvf c5da6cd3-0266-4ebb-bd11-713ffe9b5ef9.tar #
cd /home/sguo/monod/data/coad/DNA_Methylation/JHU_USC__HumanMethylation450/Level_3