1.新增part《单细胞补充内容》，并完成章节《读取非标准格式的单细胞数据》、《细胞分群通用marker》

2.在《R语言基础》中新增章节《循环》 3.新增section：《17.2 单细胞转录组测序技术及细胞分离技术分类汇总》
djhcod · Jan 10, 2024 · 6d0bb2c · 6d0bb2c
1 parent 63d4ca5
commit 6d0bb2c
Show file tree

Hide file tree

Showing 80 changed files with 11,460 additions and 2,230 deletions.
diff --git a/_quarto.yml b/_quarto.yml
@@ -39,6 +39,7 @@ book:
         - r_basic/data_input_output.qmd
         - r_basic/basic_data_function.qmd
         - r_basic/character.qmd
+        - r_basic/loop.qmd
     - part: single_cell/seurat/seurat.qmd
       chapters:
         - single_cell/seurat/seurat_command_list.qmd
@@ -64,6 +65,11 @@ book:
         - single_cell/scRNA-seq_online/07_SC_clustering_cells_SCT.qmd
         - single_cell/scRNA-seq_online/08_SC_clustering_quality_control.qmd
         - single_cell/scRNA-seq_online/09_merged_SC_marker_identification.qmd
+    - part: "单细胞补充内容"
+      chapters:
+        - single_cell/sc_supplementary/read_sc_data.qmd
+        - single_cell/sc_supplementary/universal_marker.qmd
+        - single_cell/sc_supplementary/integrated_analysis_multiple_single_cell_datasets.qmd
     - part: quarto_foundation/quarto_foundation.qmd
       chapters:
         - quarto_foundation/yaml_settings.qmd

diff --git a/docs/r_basic/basic_data_function.html b/docs/r_basic/basic_data_function.html
diff --git a/docs/r_basic/loop.html b/docs/r_basic/loop.html
diff --git a/docs/r_basic/r_basics.html b/docs/r_basic/r_basics.html
diff --git a/docs/search.json b/docs/search.json
diff --git a/docs/single_cell/other_single_cell_content/GSE234933.html b/docs/single_cell/other_single_cell_content/GSE234933.html
diff --git a/...ell/other_single_cell_content/GSE234933_files/figure-html/unnamed-chunk-4-1.png b/...ell/other_single_cell_content/GSE234933_files/figure-html/unnamed-chunk-4-1.png
diff --git a/...ell/other_single_cell_content/GSE234933_files/figure-html/unnamed-chunk-5-1.png b/...ell/other_single_cell_content/GSE234933_files/figure-html/unnamed-chunk-5-1.png
diff --git a/docs/single_cell/other_single_cell_content/images/截屏2024-01-03 20.56.56.png b/docs/single_cell/other_single_cell_content/images/截屏2024-01-03 20.56.56.png
diff --git a/docs/single_cell/scRNA-seq_online/01_intro_to_scRNA-seq.html b/docs/single_cell/scRNA-seq_online/01_intro_to_scRNA-seq.html
diff --git a/docs/single_cell/scRNA-seq_online/02_SC_generation_of_count_matrix.html b/docs/single_cell/scRNA-seq_online/02_SC_generation_of_count_matrix.html
diff --git a/docs/single_cell/scRNA-seq_online/03_SC_quality_control-setup.html b/docs/single_cell/scRNA-seq_online/03_SC_quality_control-setup.html
diff --git a/docs/single_cell/scRNA-seq_online/06_SC_SCT_normalization.html b/docs/single_cell/scRNA-seq_online/06_SC_SCT_normalization.html
diff --git a/docs/single_cell/scRNA-seq_online/09_merged_SC_marker_identification.html b/docs/single_cell/scRNA-seq_online/09_merged_SC_marker_identification.html
diff --git a/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1hOTg0ZTFjNjU3NzM3NGRmLnBuZw.png b/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1hOTg0ZTFjNjU3NzM3NGRmLnBuZw.png
diff --git a/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1iNGQzNzE4ZTdlOGNiNzI1LnBuZw.png b/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1iNGQzNzE4ZTdlOGNiNzI1LnBuZw.png
diff --git a/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1mOWQ5MzI2NmRmYjI0NTZmLnBuZw.png b/...zLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1mOWQ5MzI2NmRmYjI0NTZmLnBuZw.png
diff --git a/docs/single_cell/sc_supplementary/GSE184880.html b/docs/single_cell/sc_supplementary/GSE184880.html
diff --git a/docs/single_cell/sc_supplementary/images/640-01.png b/docs/single_cell/sc_supplementary/images/640-01.png
diff --git a/docs/single_cell/sc_supplementary/images/640-2.png b/docs/single_cell/sc_supplementary/images/640-2.png
diff --git a/docs/single_cell/sc_supplementary/images/640.png b/docs/single_cell/sc_supplementary/images/640.png
diff --git a/docs/single_cell/sc_supplementary/images/Image-01.jpg b/docs/single_cell/sc_supplementary/images/Image-01.jpg
diff --git a/docs/single_cell/sc_supplementary/images/Image2.jpg b/docs/single_cell/sc_supplementary/images/Image2.jpg
diff --git a/docs/single_cell/sc_supplementary/images/infercnv.png b/docs/single_cell/sc_supplementary/images/infercnv.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-03 20.56.56.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-03 20.56.56.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-08 11.17.00.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-08 11.17.00.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.10.35.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.10.35.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.11.45.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.11.45.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.12.14.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-09 11.12.14.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-10 11.02.14.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-10 11.02.14.png
diff --git a/docs/single_cell/sc_supplementary/images/截屏2024-01-10 12.00.48.png b/docs/single_cell/sc_supplementary/images/截屏2024-01-10 12.00.48.png
diff --git a/docs/single_cell/sc_supplementary/integrated_analysis_multiple_single_cell_datasets.html b/docs/single_cell/sc_supplementary/integrated_analysis_multiple_single_cell_datasets.html
diff --git a/docs/single_cell/sc_supplementary/read_sc_data.html b/docs/single_cell/sc_supplementary/read_sc_data.html
diff --git a/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-14-1.png b/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-14-1.png
diff --git a/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-14-2.png b/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-14-2.png
diff --git a/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-16-1.png b/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-16-1.png
diff --git a/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-16-2.png b/...gle_cell/sc_supplementary/read_sc_data_files/figure-html/unnamed-chunk-16-2.png
diff --git a/docs/single_cell/sc_supplementary/universal_marker.html b/docs/single_cell/sc_supplementary/universal_marker.html
diff --git a/docs/single_cell/seurat/cell_cycle_regression.html b/docs/single_cell/seurat/cell_cycle_regression.html
diff --git a/docs/single_cell/seurat/seurat_command_list.html b/docs/single_cell/seurat/seurat_command_list.html
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
@@ -10,7 +10,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/r_basic/r_basics.html</loc>
-    <lastmod>2023-12-26T04:58:39.282Z</lastmod>
+    <lastmod>2024-01-08T10:18:39.709Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/r_basic/environment_configuration.html</loc>
@@ -22,7 +22,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/r_basic/basic_data_function.html</loc>
-    <lastmod>2023-12-26T04:58:39.347Z</lastmod>
+    <lastmod>2024-01-03T11:54:28.470Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/r_basic/character.html</loc>
@@ -34,7 +34,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/seurat/seurat_command_list.html</loc>
-    <lastmod>2023-12-26T04:58:39.393Z</lastmod>
+    <lastmod>2024-01-10T03:33:53.049Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/seurat/seurat_tutorial.html</loc>
@@ -50,7 +50,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/seurat/cell_cycle_regression.html</loc>
-    <lastmod>2023-12-26T04:58:39.485Z</lastmod>
+    <lastmod>2024-01-03T11:54:28.486Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/seurat/integration.html</loc>
@@ -78,15 +78,15 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/01_intro_to_scRNA-seq.html</loc>
-    <lastmod>2023-12-26T04:58:39.622Z</lastmod>
+    <lastmod>2024-01-09T09:30:39.368Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/02_SC_generation_of_count_matrix.html</loc>
-    <lastmod>2023-12-26T04:58:39.632Z</lastmod>
+    <lastmod>2024-01-10T03:33:53.058Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/03_SC_quality_control-setup.html</loc>
-    <lastmod>2023-12-26T04:58:39.647Z</lastmod>
+    <lastmod>2024-01-09T04:36:48.540Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/04_SC_quality_control.html</loc>
@@ -102,7 +102,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/06_SC_SCT_normalization.html</loc>
-    <lastmod>2023-12-26T04:58:39.717Z</lastmod>
+    <lastmod>2024-01-03T11:54:28.510Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/06_integration.html</loc>
@@ -118,7 +118,7 @@
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/single_cell/scRNA-seq_online/09_merged_SC_marker_identification.html</loc>
-    <lastmod>2023-12-26T04:58:39.827Z</lastmod>
+    <lastmod>2024-01-03T11:54:28.556Z</lastmod>
   </url>
   <url>
     <loc>https://djhcod.github.io/r-notes/quarto_foundation/quarto_foundation.html</loc>
@@ -152,4 +152,28 @@
     <loc>https://djhcod.github.io/r-notes/references.html</loc>
     <lastmod>2023-12-26T04:58:39.985Z</lastmod>
   </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/r_basic/loop.html</loc>
+    <lastmod>2024-01-08T10:18:45.337Z</lastmod>
+  </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/single_cell/other_single_cell_content/GSE234933.html</loc>
+    <lastmod>2024-01-03T15:14:23.896Z</lastmod>
+  </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/single_cell/sc_supplementary/universal_marker.html</loc>
+    <lastmod>2024-01-10T04:07:05.770Z</lastmod>
+  </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/single_cell/sc_supplementary/read_sc_data.html</loc>
+    <lastmod>2024-01-10T04:06:39.398Z</lastmod>
+  </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/single_cell/sc_supplementary/GSE184880.html</loc>
+    <lastmod>2024-01-08T05:55:02.979Z</lastmod>
+  </url>
+  <url>
+    <loc>https://djhcod.github.io/r-notes/single_cell/sc_supplementary/integrated_analysis_multiple_single_cell_datasets.html</loc>
+    <lastmod>2024-01-10T04:55:28.560Z</lastmod>
+  </url>
 </urlset>
diff --git a/images/截屏2024-01-03 20.56.56-01.png b/images/截屏2024-01-03 20.56.56-01.png
diff --git a/images/截屏2024-01-03 20.56.56.png b/images/截屏2024-01-03 20.56.56.png
diff --git a/r_basic/basic_data_function.qmd b/r_basic/basic_data_function.qmd
@@ -413,44 +413,6 @@ datstd <- dat %>% unite("datehour",date,hour,sep = ' ',remove = T) %>% unite("da
 datstd
 ```
 
-## `apply`函数家族
-
-主要应用apply()函数。apply()以数据帧或矩阵作为输入，并以向量、列表或数组的形式给出输出。apply()函数主要用于避免显式使用循环结构。与之类似的lapply()函数返回与输入列表对象长度相似的列表对象，其中的每个元素都是应用指定函数到列表中相应元素的结果，其作用相当于避免了for循环的使用，更适合转换数据类型等操作。
-
-生成案例数据
-
-```{r}
-mydata<-matrix(1:9,ncol = 3,nrow=6)#生成一个3列、6行的矩阵数据
-mydata[3,3]<-NA#生成一个缺失值
-mydata<-as.data.frame(mydata)#如果要生成新的一列需要转换为数据框形式
-mydata
-```
-
-计算mydata数据集中每一行的均值并添加到每一行后面
-
-```{r}
-mydata$Row_Means<-apply(mydata,
-                        MARGIN=1,#1:对每行进行运算；2:对列进行运算；MARGIN=c(1,2)对行、列运算
-                        mean,#要应用的函数
-                        na.rm=T)#是否忽略缺失值
-mydata
-```
-
-对mydata的第一列和第二列数据求均值
-
-```{r}
-mydata$Row_Means12<-apply(mydata[,c(1:2)],MARGIN=1,mean,na.rm=T)
-mydata
-```
-
-对mydata的每一列进行求和运算
-
-```{r}
-Col_Sums<-apply(mydata,MARGIN=2,sum,na.rm=T)
-mydata<-rbind(mydata,Col_Sums)
-mydata
-```
-
 ## 自定义函数
 
 R语言可以自定义函数，也可以使用其自带的函数。

diff --git a/r_basic/loop.qmd b/r_basic/loop.qmd
@@ -1,6 +1,46 @@
 # 循环
 
-`assign()`函数
+## `apply`函数家族
+
+主要应用apply()函数。apply()以数据帧或矩阵作为输入，并以向量、列表或数组的形式给出输出。apply()函数主要用于避免显式使用循环结构。与之类似的lapply()函数返回与输入列表对象长度相似的列表对象，其中的每个元素都是应用指定函数到列表中相应元素的结果，其作用相当于避免了for循环的使用，更适合转换数据类型等操作。
+
+生成案例数据
+
+```{r}
+mydata <- matrix(1:9, ncol = 3,nrow = 6) #生成一个3列、6行的矩阵数据
+mydata[3,3] <- NA #生成一个缺失值
+mydata <- as.data.frame(mydata) #如果要生成新的一列需要转换为数据框形式
+mydata
+```
+
+计算mydata数据集中每一行的均值并添加到每一行后面
+
+```{r}
+mydata$Row_Means <- apply(mydata,
+                          MARGIN = 1, #1:对每行进行运算；2:对列进行运算；MARGIN=c(1,2)对行、列运算
+                        mean, #要应用的函数
+                        na.rm = T) #是否忽略缺失值
+mydata
+```
+
+对mydata的第一列和第二列数据求均值
+
+```{r}
+mydata$Row_Means12 <- apply(mydata[, c(1:2)], MARGIN = 1, mean,na.rm = T)
+mydata
+```
+
+对mydata的每一列进行求和运算
+
+```{r}
+Col_Sums <- apply(mydata, MARGIN = 2, sum,na.rm = T)
+mydata <- rbind(mydata, Col_Sums)
+mydata
+```
+
+## 在循环语句中的其他常用函数
+
+### `assign()`函数
 
 `assign`函数能够将某个值赋值给指定名称，从而实现循环中将每次运行的结果保存到一个对象中，而不覆盖上一次运行的结果。
 
@@ -17,4 +57,48 @@ for (x in c("A", "B", "C", "D")){
 }
 ```
 
-![](images/截屏2023-12-24 12.24.12.png){width="282"}
+### `append`函数
+
+`append()`函数被广泛应用于将新的向量添加到现有的向量、列表或数据框中。
+
+-   将新向量添加到已有向量中：
+
+```{r}
+v1 <- c(1, 2, 3, 4, 5)
+v2 <- c(6, 7, 8)
+
+v3 <- append(v1, v2)
+v3
+
+#等价于
+v3 <- c(v1, v2)
+```
+
+-   将新列表添加到已有列表中：
+
+```{r}
+list1 <- list(a = 1, b = 2, c = 3)
+list2 <- list(d = 4, e = 5, f = 6)
+list3 <- append(list1, list2)
+list3
+```
+
+实际应用场景：在批量读取构建Seurat对象时，通过append()函数将每次的Seurat对象添加到列表中，最终得到一个包含了所有样本的单细胞数据的列表：
+
+```{r}
+#| eval: false
+for (file in file_list) {
+  # 拼接文件路径
+  data.path <- paste0("data/other_single_cell_content/GSE234933_MGH_HNSCC_gex_raw_counts/", file)
+  # 读取RDS文件数据
+  seurat_data <- readRDS(data.path)
+  # 创建Seurat对象，并指定项目名称为文件名（去除后缀）
+  sample_name <- file_path_sans_ext(file)
+  seurat_obj <- CreateSeuratObject(counts = seurat_data,
+                                   project = sample_name,
+                                   min.features = 200,
+                                   min.cells = 3)
+  # 将Seurat对象添加到列表中
+  seurat_list <- append(seurat_list, seurat_obj)
+}
+```
diff --git a/references.bib b/references.bib
@@ -572,3 +572,65 @@ @article{tran2020
 	url = {http://dx.doi.org/10.1186/s13059-019-1850-9},
 	langid = {en}
 }
+
+@article{xu2022,
+	title = {Single-Cell RNA Sequencing Reveals the Tissue Architecture in Human High-Grade Serous Ovarian Cancer},
+	author = {Xu, Junfen and Fang, Yifeng and Chen, Kelie and Li, Sen and Tang, Sangsang and Ren, Yan and Cen, Yixuan and Fei, Weidong and Zhang, Bo and Shen, Yuanming and Lu, Weiguo},
+	year = {2022},
+	month = {08},
+	date = {2022-08-15},
+	journal = {Clinical Cancer Research: An Official Journal of the American Association for Cancer Research},
+	pages = {3590--3602},
+	volume = {28},
+	number = {16},
+	doi = {10.1158/1078-0432.CCR-22-0296},
+	note = {PMID: 35675036
+PMCID: PMC9662915
+remark: HGSOC组织内的异质性及其与肿瘤微环境的关系。},
+	langid = {en}
+}
+
+@article{Bill2023,
+	title = {{\emph{CXCL9:SPP1}}
+            macrophage polarity identifies a network of cellular programs that control human cancers},
+	author = {Bill, Ruben and Wirapati, Pratyaksha and Messemaker, Marius and Roh, Whijae and Zitti, Beatrice and Duval, Florent and Kiss, {Máté} and Park, Jong Chul and Saal, Talia M. and Hoelzl, Jan and Tarussio, David and Benedetti, Fabrizio and Tissot, {Stéphanie} and Kandalaft, Lana and Varrone, Marco and Ciriello, Giovanni and McKee, Thomas A. and Monnier, Yan and Mermod, Maxime and Blaum, Emily M. and Gushterova, Irena and Gonye, Anna L. K. and Hacohen, Nir and Getz, Gad and Mempel, Thorsten R. and Klein, Allon M. and Weissleder, Ralph and Faquin, William C. and Sadow, Peter M. and Lin, Derrick and Pai, Sara I. and Sade-Feldman, Moshe and Pittet, Mikael J.},
+	year = {2023},
+	month = {08},
+	date = {2023-08-04},
+	journal = {Science},
+	pages = {515--524},
+	volume = {381},
+	number = {6657},
+	doi = {10.1126/science.ade2292},
+	url = {http://dx.doi.org/10.1126/science.ade2292},
+	langid = {en}
+}
+
+@article{Li2020,
+	title = {Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue},
+	author = {Li, Yanming and Ren, Pingping and Dawson, Ashley and Vasquez, Hernan G. and Ageedi, Waleed and Zhang, Chen and Luo, Wei and Chen, Rui and Li, Yumei and Kim, Sangbae and Lu, Hong S. and Cassis, Lisa A. and Coselli, Joseph S. and Daugherty, Alan and Shen, Ying H. and LeMaire, Scott A.},
+	year = {2020},
+	month = {10},
+	date = {2020-10-06},
+	journal = {Circulation},
+	pages = {1374--1388},
+	volume = {142},
+	number = {14},
+	doi = {10.1161/circulationaha.120.046528},
+	url = {http://dx.doi.org/10.1161/CIRCULATIONAHA.120.046528},
+	langid = {en}
+}
+
+@article{gong2023,
+	title = {Nasopharyngeal carcinoma cells promote regulatory T cell development and suppressive activity via CD70-CD27 interaction},
+	author = {Gong, Lanqi and Luo, Jie and Zhang, Yu and Yang, Yuma and Li, Shanshan and Fang, Xiaona and Zhang, Baifeng and Huang, Jiao and Chow, Larry Ka-Yue and Chung, Dittman and Huang, Jinlin and Huang, Cuicui and Liu, Qin and Bai, Lu and Tiu, Yuen Chak and Wu, Pingan and Wang, Yan and Tsao, George Sai-Wah and Kwong, Dora Lai-wan and Lee, Anne Wing-Mui and Dai, Wei and Guan, Xin-Yuan},
+	year = {2023},
+	month = {04},
+	date = {2023-04-06},
+	journal = {Nature Communications},
+	volume = {14},
+	number = {1},
+	doi = {10.1038/s41467-023-37614-6},
+	url = {http://dx.doi.org/10.1038/s41467-023-37614-6},
+	langid = {en}
+}
diff --git a/single_cell/scRNA-seq_online/01_intro_to_scRNA-seq.qmd b/single_cell/scRNA-seq_online/01_intro_to_scRNA-seq.qmd
@@ -26,6 +26,56 @@ Popular methods to address some of the more common investigations include:
 
 ![](images/sc_analyses.png)
 
+## 单细胞转录组测序技术及细胞分离技术分类汇总
+
+> 原文：[单细胞转录组测序技术(scRNA-seq)及细胞分离技术分类汇总](https://www.cnblogs.com/aipufu/p/11481055.html)
+
+在过去的十多年里，高通量测序技术被广泛应用于生物和医学的各种领域，极大促进了相关的研究和应用。其中转录组测序（RNA-seq）被广泛应用于测定和描绘各类物种的基因或转录本的表达情况。但传统的转录组测序技术（bulk RNA-seq）是基于群体细胞，每个样本包含成千上万个细胞，所以最终反映的是基因在群体细胞中平均表达水平，从而掩盖了不同细胞之间的表达异质性。近年来，单细胞转录组测序（single-cell RNA-seq，scRNA-seq）技术得到了蓬勃的发展，从而使得可在单细胞水平揭示全基因组范围内所有基因的表达情况，非常有利于研究细胞间的表达异质性。目前单细胞转录组测序技术（scRNA-seq）已经广泛应用于各类物种（特别是人、小鼠等）的不同类型组织和细胞系，包括正常和病变细胞等。自从2009年等由汤富酬等人开发出了第一种单细胞转录组测序技术，目前已经有几十种不同的单细胞转录组测序技术相继被开发出来，它们都有各自的特点，拥有特定的优势和缺点。为了正确利用相应的单细胞测序技术开展相关研究和应用，非常有必要充分了解这些不同技术的优缺点。
+
+![](images/aHR0cHM6Ly91cGxvYWQtaW1hZ2VzLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1mOWQ5MzI2NmRmYjI0NTZmLnBuZw.png)
+
+> 单细胞转录组测序技术的发展史(Svensson et al. *NATURE PROTOCOLS*, 2018)
+
+### 细胞分离技术
+
+进行单细胞测序前，首先需要分离单个的细胞，不同类型的单细胞转录组测序技术，使用的细胞分离技术可能不一样。总的来说，目前主要有以下几类细胞分离技术：
+
+1.  **Micropipetting micromanipulation（口吸管技术）；**
+
+2.  **Laser capture microdissection（激光捕获显微切割技术）；**
+
+3.  **Fluorescence activated Cell Sorting，FACS（流式细胞仪技术）；**
+
+4.  **Microdroplets（微滴技术）；**
+
+5.  **Microfluidics（微流体技术）；**
+
+这几类技术的优缺点具体如下图所示：
+
+![](images/aHR0cHM6Ly91cGxvYWQtaW1hZ2VzLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1iNGQzNzE4ZTdlOGNiNzI1LnBuZw.png)
+
+> 单细胞转录组测序细胞分离技术分类及各自的优缺点(Kolodziejczyk et al. Molecular Cell, 2015)
+
+### 单细胞转录组测序技术
+
+单细胞转录组测序技术种类根据测序捕获的转录本序列范围主要可分为：
+
+-   测**全长转录本（full-length transcript sequencing）**的技术（如**Smart-seq2**、MATQ-seq 、SUPeR-seq等）
+
+    -   优点：可测转录本的全长，**基因数多，测序深度大**，可进行各种类型的转录组测序数据分析
+
+    -   缺点：**细胞通量少**，价格较贵
+
+-   只测转录本 **3′ 或5′ 端（3′或5′-end sequencing）**的技术（如**10X Genomics**, CEL-seq2, Drop-seq, inDrops等）
+
+-   优点：**细胞通量高**，价格便宜；
+
+-   缺点：只测转录本的一端，检测基因表达灵敏度较低，不适合进行可变剪接、等位基因表达等分析。
+
+目前已有的主要单细胞转录组测序技术具体如下表所示 (Chen et al. Frontiers in Genetics, 2019)：
+
+![](images/aHR0cHM6Ly91cGxvYWQtaW1hZ2VzLmppYW5zaHUuaW8vdXBsb2FkX2ltYWdlcy8xOTQzMTY2OC1hOTg0ZTFjNjU3NzM3NGRmLnBuZw.png){width="556"}
+
 ## Challenges of scRNA-seq analysis
 
 Prior to scRNA-seq, transcriptome analysis was performed using **bulk RNA-seq**, which is a straight-forward method for comparing the **averages of cellular expression**. This method can be a good choice if looking at comparative transcriptomics (e.g. samples of the same tissue from different species), and for quantifying expression signatures in disease studies. It also has potential for the discovery of disease biomarkers if you are not expecting or not concerned about **cellular heterogeneity** in the sample.