一个公共数据集最多可以被挖掘多少次呢？
是我太年轻
学员群有咨询 Agilent-038314 CBC Homo sapiens lncRNA + mRNA microarray V2.0 这个表达量芯片的数据处理问题，当然了，主要是芯片的探针ID对应基因名字的问题。 链接是；https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL18109
因为大家还是初学者，所以我就想着先打击一下，说这样的芯片比较难，肯定是很少有人挖掘它，因为它仅仅是提供了探针的碱基序列，有一个费时费力的流程去拿到该芯片的注释信息：
比如我们可以看到《LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma》 这个2019的文献，链接是: https://doi.org/10.1016/j.ygeno.2018.01.003
就对这个芯片做了非常复杂的处理:
Microarray data contained 71,584 probes. After applying the criteria for re-annotation, 39,068 of the probes were retained, among which 20,323 were corresponding to mRNAs and 18,745 to lncRNA.
最后这些探针还需要去冗余，得到：These probes were mapped to 25,018 unique genes, including 13,490 protein coding genes (PCGs) and 11,528 lncRNAs.
而且这些数据，文章都整理好了，都在附件：
现在你还在发愁，这样的芯片，如何做ID转化吗？
让我吃惊的是
出于职业习惯，我去看了看这个数据集页面： https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53625
发现它居然链接到了4个文献:
Li J, Chen Z, Tian L, Zhou C et al. LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma. Gut 2014 Nov;63(11):1700-10. PMID: 24522499 Li W, Zhang L, Guo B, Deng J et al. Exosomal FMR1-AS1 facilitates maintaining cancer stem-like cell dynamic equilibrium via TLR7/NFκB/c-Myc signaling in female esophageal carcinoma. Mol Cancer 2019 Feb 8;18(1):22. PMID: 30736860 Li Y, Lu Z, Che Y, Wang J et al. Immune signature profiling identified predictive and prognostic factors for esophageal squamous cell carcinoma. Oncoimmunology 2017;6(11):e1356147. PMID: 29147607 Shi X, Chen Z, Hu X, Luo M et al. AJUBA promotes the migration and invasion of esophageal squamous cell carcinoma cells through upregulation of MMP10 and MMP13 expression. Oncotarget 2016 Jun 14;7(24):36407-36418. PMID: 27172796 Liu J, Wang Y, Chu Y, Xu R et al. Identification of a TLR-Induced Four-lncRNA Signature as a Novel Prognostic Biomarker in Esophageal Carcinoma. Front Cell Dev Biol 2020;8:649. PMID: 32850794
我又好奇去谷歌搜了一下这个数据集:
粗略看了看，起码几十篇数据挖掘文献， 就针对这一个数据集，变着花样各种挖，活脱脱的一个数据挖掘发展史！