add new file

R/transID.R

@@ -14,13 +14,11 @@
 #' @examples
 #' transId(
 #'   id = c("Cyp2c23", "Fhit", "Gal3st2b", "Trp53", "Tp53"),
-#'   trans_to = "ensembl", org = "mouse", simple = TRUE
-#' )
+#'   trans_to = "ensembl", org = "mouse", simple = TRUE)
 #' # input id contains duplicates,fake id and one-to-many match id
 #' transId(
 #'   id = c("MMD2", "HBD", "TP53", "RNR1", "TEC", "BCC7", "FAKEID", "TP53"),
-#'   trans_to = "entrez", org = "hg", simple = FALSE
-#' )
+#'   trans_to = "entrez", org = "hg", simple = FALSE)
 transId <- function(id, trans_to, org, simple = TRUE) {
 
   #--- args ---#

README.md

@@ -96,6 +96,7 @@ remotes::install_github("GangLiLab/genekitr", build_vignettes = TRUE, dependenci
 
 - [x] ID转换`transId` 允许错误的id匹配，结果为NA，并且提交的顺序和结果的顺序一致
 - [x] 从`genInfo`的结果中提取转换后的id，更快更准确，并且可以保证output和input顺序一致
+- [ ] 加快大型数据的ID转换速度，需要改进`genInfo` 
 
 ##### 数据分析（Analyse）
 
@@ -122,11 +123,24 @@ remotes::install_github("GangLiLab/genekitr", build_vignettes = TRUE, dependenci
 ## DEBUG
 
 - [x] `genGO`的use_symbol参数不管用 （原因：如果提供的已经是symbol，那么就忽略了这个参数）
+
 - [x] 函数正常使用，但是帮助文档出不来（原因：写完函数忘记`devtools::document()` ，跳过这一步直接刷新包就会导致文档没更新）
+
 - [x] 一个symbol对应多个entrez时，会默认按照数值从小到大排序，然后再进行合并。因为同一个symbol name，数值比较小的entrez更常用
+
 - [x] 更新了`genInfo` 和`transID` ，增加参数`simple = TRUE` ，方便应对一个id同时存在多个match结果的情况（如果`simple = TRUE` 就返回和input id 同样顺序的结果；如果`simple = F` ，就返回所有结果）
+
 - [ ] `biomart`的结果也不全？发现`ENSG00000002079`[这个基因](http://asia.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000002079;r=7:99238829-99311130)在ensembl中对应基因`MYH16` ，但是biomart的结果中`ENSG00000002079 ` 没有对应，而且`MYH16` 对应的ensemble id也是NA
 
+  - 解决方法1：尝试下载ensemble物种所有的mapping数据：
+
+    ```xml
+    # 以human为例，下载ensembl、symbol、uniprot的对应
+    wget -O result.txt 'http://www.ensembl.org/biomart/martservice?query=<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE Query><Query  virtualSchemaName = "default" formatter = "TSV" header = "0" uniqueRows = "0" count = "" datasetConfigVersion = "0.6" ><Dataset name = "hsapiens_gene_ensembl" interface = "default" ><Attribute name = "ensembl_gene_id" /><Attribute name = "hgnc_symbol" /><Attribute name = "uniprotswissprot" /></Dataset></Query>'
+    ```
+
+
+
 
 
 ## Let's mining data!

test-zone/generate-expdat.R

@@ -4,3 +4,39 @@ airway
 as.data.frame(colData(airway))
 summary(colSums(assay(airway))/1e6)
 metadata(rowRanges(airway))
+
+exprSet=assay(airway)
+group_list=colData(airway)[,3]
+colnames(exprSet)
+pheatmap::pheatmap(cor(exprSet))
+group_list
+tmp=data.frame(g=group_list)
+rownames(tmp)=colnames(exprSet)
+# 组内的样本的相似性应该是要高于组间的！
+pheatmap::pheatmap(cor(exprSet),annotation_col = tmp)
+dim(exprSet)
+exprSet=exprSet[apply(exprSet,1, function(x) sum(x>1) > 5),]
+dim(exprSet)
+
+exprSet=log(edgeR::cpm(exprSet)+1)
+dim(exprSet)
+exprSet=exprSet[names(sort(apply(exprSet, 1,mad),decreasing = T)[1:500]),]
+dim(exprSet)
+M=cor(log2(exprSet+1))
+tmp=data.frame(g=group_list)
+rownames(tmp)=colnames(M)
+pheatmap::pheatmap(M,annotation_col = tmp)
+
+pheatmap::pheatmap(scale(cor(log2(exprSet+1))))
+
+length(unique(rownames(exprSet)))
+id = transId(rownames(exprSet),'symbol','hs',simple = F)
+
+id2 = clusterProfiler::bitr(rownames(exprSet),'ENSEMBL','SYMBOL','org.Hs.eg.db')
+
+table(is.na(id))
+
+
+x = genInfo(rownames(exprSet),'hs')
+
+

test-zone/test_biomart.R

@@ -67,6 +67,15 @@ bmt2 <- getBM( values = mm_id,
                               dataset = paste0(organism,"_gene_ensembl"),
                               host = "asia.ensembl.org"))
 
+
+getBM( values = 'ENSG00000002079',
+       attributes = c("uniprot_gn_symbol",'uniprotswissprot','ensembl_gene_id',
+                      'uniprot_gn_id','uniprotsptrembl'),
+       filters = "ensembl_gene_id",
+       mart = useMart("ensembl",
+                      dataset = paste0(organism,"_gene_ensembl"),
+                      host = "asia.ensembl.org"))
+
 # %>%
 #   data.table::setnames(., old =colnames(.),
 #                        new = c('ensembl','chr','start','end','strand','gc_content','gene_biotype','transcript_count')) %>%