60-topics.Rmd

# (PART) 研究热点 {-}

# 四大研究主题 {#research-topic}

我们都知道，对于肠道菌群研究而言，饮食、免疫、代谢、疾病等是四大最重要的研究主题。在这一部分，我们将在这四大研究主题之间逐步展开。

```{r}

# 整合多个关键词为一个关键词
keywords_from <- function(..., name = "primary"){
  keyword_list <- list(...)
  result <- lapply(keyword_list, function(x){
    x[[name]]
  })
  paste0(unlist(result), collapse = "|")
}




# 提取历史引证网络中的论文
extract_from_hist_graph <- function(M=NULL, g=NULL){
  name <- V(g)$name
  doi <- str_extract_all(name, "10\\.[0-9]+\\/\\S+")
  doi <- unlist(doi)
  M %>% filter(DI %in% doi)
}


```



```{r}

diet_keywords <- list(primary=c("\\bdiet[:alpha:]+"), 
                      secondary = c("food","nutrition","supplement","fasting","calorie restriction",
                                    "fibre|fiber","carbohydrate","meat","fish","egg","milk","dairy",
                                    "fruit","vegetable","additives","sweetener"))

immunity_keywords <- list(primary=c("immun[:alpha:]+"),
                          secondary = c("inflamm[:alpha:]+","barrier","defense","T cell","B cell",
                                        "lymphocyte","macrophage","cytokine","dendritic","neutrophil",
                                        "interleukin","antibody"))

metabolism_keywords <- list(primary = c("\\bmetabol[:alpha:]+"),
                            secondary = c("obesity","diabet[:alpha:]+","insulin","lipid","\\bfat[:alpha:]+",
                                          "adipose","NAFLD","NASH","nonalcoholic steatohepatitis","SCFA"))

# 癌症
cancer_keywords <- list(primary = c("cancer|\\btumo[:alpha:]+|oncology|melanoma|carcino[:alpha:]+"),
                        secondary = NULL)
# 心血管疾病
cardio_keywords <- list(primary = c("heart|cardi[:alpha:]+|hypertension"),
                        secondary = c("stroke","cardiac","arterial","athero[:alpha:]+","blood pressure"))
# 炎症性肠病
ibd_keywords <- list(primary = c("IBD|inflammatory bowl disease|ulcerative colitis|crohn.{1,5}disease|\\bUC\\b"),
                     secondary = NULL)
# 肠易激综合征
ibs_keywords <- list(primary = c("\\bIBS\\b|irritable bowel syndrome"),
                     secondary = NULL)
# 阿尔兹海默症
alzheimer_keywords <- list(primary = "Alzheimer.{1,5}disease",
                           secondary = NULL)
# 自闭症
autism_keywords <- list(primary = "autism",
                        secondary = NULL)
# 肝病
hepatology_keywords <- list(primary = c("NAFLD|non.?alcoholic fatty liver disease"),
                            secondary = NULL)
# 过敏
allergy_keywords <- list(primary = "\\ballerg.*\\b",
                         secondary = NULL)
# 肥胖
obesity_keywords <- list(primary = "obesity",
                         secondary = NULL)
# 糖尿病
diabetes_keywords <- list(primary = "diabetes",
                          secondary = NULL)
# 哮喘
asthma_keywords <- list(primary = "asthma",
                          secondary = NULL)
# 腹泻
diarrhea_keywords <- list(primary = "diarrhea",
                          secondary = NULL)
# 便秘
constipation_keywords <- list(primary = "\\bconstip.*\\b",  
                          secondary = NULL)
## 全部疾病
disease_keywords <- list(primary = c(keywords_from(cancer_keywords,cardio_keywords,ibd_keywords,ibs_keywords,
                                                   alzheimer_keywords,autism_keywords,hepatology_keywords,
                                                   allergy_keywords,obesity_keywords,diabetes_keywords,asthma_keywords,
                                                   diarrhea_keywords,constipation_keywords),
                                     "disease"),
                         secondary = NULL)

gut_axis_keywords <- list(primary = c("gut.{4,15}axis"),
                          secondary = NULL)


metabolism_effect_keywords <- list(primary = c("\\bmetabol[:alpha:]+"),
                        secondary = c("SCFA","short-chain fatty acids","butyrate","TMAO","TMA","secondary bile acids"))

medicine_keywords <- list(primary = c("medicine|medication|drug|[:alpha:]+pharmaco[:alpha:]+|xenobiotic"),
                        secondary = NULL)

early_life_keywords <- list(primary = c("early.{1,2}life|infan[:alpha:]+|maternal"),
                        secondary = c("pregnan[:alpha:]+","[:alpha:]+natal","in utero","uterus","placent[:alpha:]+","vertical transmission"))

cohort_keywords <- list(primary = c("cohort"),
                        secondary = NULL)

dark_matter_keywords <- list(primary = c("dark matter|virome|mycobiome|culturomics"),
                             secondary = NULL)


translation_keywords_precision <- list(primary = c("precision|personal"))
translation_keywords_biomarker <- list(primary = c("diagnos|prognos|biomarker"))
translation_keywords_engineer <- list(primary = c("engineer"))
translation_keywords_phage_therapy <- list(primary = c("phage.{1,30}therapy"))
translation_keywords_vaccine <- list(primary = c("\\bvaccin"))
translation_keywords_fmt <- list(primary = c("FMT|fecal.{10,30}transplant"))
translation_keywords_probiotics <- list(primary = c("probiotic|prebiotic|dietary fib[er]{2}"))

chinese_medicine_keywords <- list(primary = c("chinese herbal medicine|traditional Chinese medicine"))

topic_keywords <- list(diet_keywords, immunity_keywords, metabolism_keywords, disease_keywords)
topic_en <- c("diet","immunity","metabolism","disease")
# topic_en <- c("diet","immunity","metabolism","cancer","cardiac","axis","medicine","early life","cohort","dark matter",
#               "precision","biomarker","engineer","phage therapy","vaccine","FMT","probiotics","Chinese medicine")
topic_cn <- c("饮食","免疫","代谢","疾病")
# topic_cn <- c("饮食","免疫","代谢","癌症","心血管疾病","轴","药物互作","生命早期","人群","暗物质",
#               "精准医疗","生物标记物","工程菌","噬菌体疗法","疫苗","粪菌移植","益生菌益生元","中医药")
topic <- topic_cn
names(topic) <- topic_en
nTopic <- length(topic)
```





## 主题对应的核心论文数据


我们首先统计下各个研究主题相关的论文数量。


```{r}
MC_HC_article <- readRDS("data/MC_HC_article.RDS")


topic_articles_core <- lapply(topic_keywords, function(keyword){
  MC_HC_article %>%
    filter(str_detect(content, regex(paste0(c(keyword$primary,keyword$secondary),collapse = "|"), ignore_case = T))) %>%
    mutate(DT=factor(DT))
})

names(topic_articles_core) <- topic_en

topic_nRecords_core <- lapply(topic_articles_core, function(article){
  article %>% group_by(PY) %>%
    summarise(nRecord = n())
})
```

在接下来的分析中，我们将基于 `r nrow(MC_HC_article)` 篇**关键论文**进行。显而易见，每个研究主题都具有庞大的发文量（图 \@ref(fig:topic-nRecord-in-core-articles)）。


```{r eval=FALSE}
# 导出文献列表（仅研究论文）

all <- MC_HC_article %>% filter(DT == "ARTICLE")


# 全部文献
writeLines(all$UT, "all_article.id")



# 分主题
for (i in 1:nTopic){
  out <- paste0(topic_en[[i]],".id")
  out <- gsub(" ","_",out)
  writeLines(topic_articles_core[[i]] %>% filter(DT == "ARTICLE") %>% pull("UT"), out)
}
```



```{r topic-nRecord-in-core-articles, fig.cap="各研究主题关键论文数量对比",fig.width=4}
count <- sapply(topic_articles_core, nrow)
df <- data.frame(topic=topic, count=count)
df$topic <- factor(df$topic, levels = rev(topic))


ggplot(df, aes(topic,count)) + 
  geom_col(width = 0.8) +
  # geom_text(aes(label=topic),position = position_stack(0.5)) +
  labs(x="",y="") +
  coord_flip()

```
与此同时，也可以看到这四大研究主题都是历史比较悠久的研究领域（图 \@ref(fig:topic-trend-in-core-articles)），自 2000 年以来不断有关键的研究论文发表。


```{r topic-trend-in-core-articles, fig.cap="各研究主题发表的关键论文数量变化情况",fig.asp=1}
topic_trend_core_plot_list <- lapply(1:nTopic, function(i){
  df <- topic_nRecords_core[[i]]
  ggplot(df, aes(PY,nRecord)) + geom_col(width = 0.8) +
    labs(x="",y="",title = topic_cn[[i]])
})

names(topic_trend_core_plot_list) <- topic_en

plot_grid(plotlist = topic_trend_core_plot_list, ncol = 1)
```

四大研究主题间的重叠度也相当可观（图 \@ref(fig:overlap-of-topic-in-core-article)）。 其中，免疫和疾病的文献重叠度最高，免疫、代谢和疾病的重叠度次之，而四大研究主题共有的研究论文就有 511 篇，占到全部**关键论文**的近六分之一之多……

```{r overlap-of-topic-in-core-article, fig.cap="各研究主题核心论文间的重叠度"}
# 各研究主题间的重叠度

topic_articles_core_SR <- lapply(topic_articles_core, function(m){
  m$SR
})

names(topic_articles_core_SR) <- topic


# library(UpSetR)
# upset(fromList(topic_articles_core_SR),
#       sets=rev(names(topic_articles_core_SR)),
#       keep.order = FALSE,
#       mb.ratio = c(0.7, 0.3),
#       order.by = "freq")

ggVennDiagram::ggVennDiagram(topic_articles_core_SR)
```