把tcga大计划的CNS级别文章标题画一个词云

时间:2022-07-23
本文章向大家介绍把tcga大计划的CNS级别文章标题画一个词云，主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。

TCGA计划官方文章在：https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/publications

全部的标题的英文很容易提取和整理，如下：

Comprehensive genomic characterization defines human glioblastoma genes and core pathways
Integrated genomic analyses of ovarian carcinoma
Comprehensive molecular characterization of human colon and rectal cancer
Comprehensive molecular portraits of human breast tumours
Comprehensive genomic characterization of squamous cell lung cancers
Integrated genomic characterization of endometrial carcinoma
Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Pan-Cancer analysis project
The somatic genomic landscape of glioblastoma
Comprehensive molecular characterization of urothelial bladder carcinoma
Comprehensive molecular profiling of lung adenocarcinoma
Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin
The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma
Comprehensive molecular characterization of gastric adenocarcinoma
Integrated genomic characterization of papillary thyroid carcinoma
Comprehensive genomic characterization of head and neck squamous cell carcinomas
Genomic Classification of Cutaneous Melanoma
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas
Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer
The Molecular Taxonomy of Primary Prostate Cancer
Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma
Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma
Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas
Integrated genomic characterization of oesophageal carcinoma
Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma
Integrated Molecular Characterization of Uterine Carcinosarcoma
Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles
Integrated genomic and molecular characterization of cervical cancer
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma
Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer
Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas
The Integrated Genomic Landscape of Thymic Epithelial Tumors
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines
Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers
Systematic Analysis of Splice-Site-Creating Mutations in Cancer
Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
Driver Fusions and Their Implications in the Development and Treatment of Human Cancers
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types
SnapShot: TCGA-Analyzed Tumors
The Cancer Genome Atlas: Creating Lasting Value beyond Its Data
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Oncogenic Signaling Pathways in The Cancer Genome Atlas
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
Comprehensive Characterization of Cancer Driver Genes and Mutations
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
Pathogenic Germline Variants in 10,389 Adult Cancers
A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples
Genomic and Functional Approaches to Understanding Cancer Aneuploidy
A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers
Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas
lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer
The Immune Landscape of Cancer
Integrated Molecular Characterization of Testicular Germ Cell Tumors
Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients
A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-β Superfamily
Integrative Molecular Characterization of Malignant Pleural Mesothelioma
The chromatin accessibility landscape of primary human cancers
Comprehensive Molecular Characterization of the Hippo Signaling Pathway in Cancer
Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data
Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer

简单的使用bing搜索一下关键词：word clound in r ，就可以找到解决方案，第一个链接就是：http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know，代码分成5个步骤。

  • Step 1: Create a text file
  • Step 2 : Install and load the required packages
  • Step 3 : Text mining
  • Step 4 : Build a term-document matrix
  • Step 5 : Generate the Word cloud

把R的知识点路线图搞定，如下：

  • 了解常量和变量概念
  • 加减乘除等运算（计算器）
  • 多种数据类型（数值，字符，逻辑，因子）
  • 多种数据结构（向量，矩阵，数组，数据框，列表）
  • 文件读取和写出
  • 简单统计可视化
  • 无限量函数学习

核心代码就是wordcloud函数，但是这个wordcloud函数要求的输入数据就需要认真做出来。

# 安装R包相信无需再强调了
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")
# 这里我们直接读取自己电脑剪切的数据即可
# 运行下面这句代码的同时，需要保证你已经复制了前面我们整理好的文章标题哦！
text=readLines(pipe("pbpaste"))
# 好像这里Mac系统跟Windows系统稍微不一样，大家需要自行把握
# Load the data as a corpus
docs <- Corpus(VectorSource(text))
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\|")
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

词云绘图结果每次布局都不一样哦，如下所示：

其实就是把词频给可视化了一下：

> head(d, 10)
               word freq
1  characterization   25
2         molecular   25
3           genomic   24
4            cancer   23
5     comprehensive   22
6          analysis   13
7        integrated   12
8         carcinoma   11
9              cell    8
10           genome    8

出现次数很多的单词，在词云就显示大一点，仅此而已。

在三年前我就整理并且制作了TCGA肿瘤数据库知识图谱视频教程，一年半前免费公布在生信技能树的B站，现在勉勉强强也快有两万的观看量。

  • 视频地址：https://www.bilibili.com/video/av49363776
  • 代码地址：https://github.com/jmzeng1314/tcga_example

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