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README.md

 # scRNAseq_tutorial
 
-
-
 ## Getting started
 
 To make it easy for you to get started with GitLab, here's a list of recommended next steps.
@@ -10,10 +8,10 @@ Already a pro? Just edit this README.md and make it your own. Want to make it ea
 
 ## Add your files
 
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+-   [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
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-```
+```         
 cd existing_repo
 git remote add origin https://git.imp.fu-berlin.de/yadit97/scrnaseq_tutorial.git
 git branch -M main
@@ -22,29 +20,29 @@ git push -uf origin main
 
 ## Integrate with your tools
 
-- [ ] [Set up project integrations](https://git.imp.fu-berlin.de/yadit97/scrnaseq_tutorial/-/settings/integrations)
+-   [ ] [Set up project integrations](https://git.imp.fu-berlin.de/yadit97/scrnaseq_tutorial/-/settings/integrations)
 
 ## Collaborate with your team
 
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-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
+-   [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
+-   [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
+-   [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
+-   [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
+-   [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
 
 ## Test and Deploy
 
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-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
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-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+-   [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
+-   [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
+-   [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
+-   [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
+-   [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
 
-***
+------------------------------------------------------------------------
 
-# Editing this README
+# Editing this README {#editing-this-readme}
 
 When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
 
@@ -53,30 +51,39 @@ When you're ready to make this README your own, just edit this file and use the
 Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
 
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+
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+
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+
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+
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+
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+
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+
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+
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+
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@@ -84,10 +91,13 @@ For people who want to make changes to your project, it's helpful to have some d
 You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
 
 ## Authors and acknowledgment
+
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 ## License
+
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 ## Project status
+
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scRNASeq.Rmd

+---
+title: "Single Cell RNASeq"
+author: "Yadik"
+date: "2024-06-03"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Annotate cell types
+
+## Step 1. Load library and your seurat object
+
+```{r}
+library(SingleR)
+library(Seurat)
+library(celldex)
+library(dplyr)
+library(pheatmap)
+
+sobj= readRDS("C:/Users/yadik/Downloads/bc_tutorial(1).rds")
+sobj
+```
+
+## Step 2. Prepare inputs for running automatic cell type annotaion
+
+**We need to extract the normalization data using GetAssayData()**
+
+```{r}
+sobj_count <- GetAssayData(sobj, layer = "data")
+str(sobj_count)
+```
+
+**Get the Blueprint and Encode reference data from celldex package:**
+
+```{r}
+ref <- celldex::BlueprintEncodeData()
+ref
+```
+
+## Step 3. Run SingleR to label cell types
+
+```{r}
+pred <- SingleR(test=sobj_count, ref = ref, labels = ref$label.main)
+pred %>% head()
+unique(pred$labels)
+```
+
+Next, we put the prediction results back to our seurat object for visualization
+
+```{r}
+sobj$labels <- pred[match(rownames(sobj@meta.data), rownames(pred)), "labels"]
+sobj@meta.data %>% head()
+
+```
+
+## Step 4. Visualize the data
+
+```{r}
+umap_pred <- DimPlot(
+  sobj, reduction = 'umap', group.by = 'labels', label = TRUE)
+
+umap_pred
+```
+
+**Compare with your previous data**
+
+```{r}
+umap_sobj <- DimPlot(
+  sobj, reduction = 'umap', group.by = 'seurat_clusters', label = TRUE)
+
+umap_sobj | umap_pred
+```
+
+```{r}
+plotScoreHeatmap(pred)
+```
+
+## Step 5. Marker-based method: CellMarker v2
+
+**Load the human markers from CellMarker 2.0, file uncludes cell marrkers of different cell types from different tissue in human**
+
+```{r}
+human_marker <- read.csv("C:/Users/yadik/OneDrive/Dokumente/Bioinformatik/CellMakerv2_Human.csv")
+str(human_marker)
+```
+
+**Select marker for ovary from the tissue class column**
+
+```{r}
+ovary_marker <- human_marker %>%
+  filter(species=='Human' & tissue_class=='Ovary') %>%
+  select(c(3,5,6,7,8,9,16))
+
+ovary_marker %>% head()
+```
+
+**Get the markers table from your seurat object**
+
+```{r}
+sobj.markers <- FindAllMarkers(sobj, only.pos = TRUE)
+sobj.markers %>% head()
+```
+
+**Combine CellMarker with our markers sobj.markers**
+
+```{r}
+merged_cellmarker <- sobj.markers %>%
+  filter(p_val_adj<0.05 & avg_log2FC>1 & pct.1 > 0.8 & pct.2 < 0.5) %>%
+    left_join(ovary_marker[,c(3,4,6)], join_by(gene==marker), relationship = "many-to-many")
+  
+merged_cellmarker %>% head()
+```
+
+**Let's try to see if there are fibroblast and astrocytes**
+
+```{r}
+merged_cellmarker %>% 
+  filter(cluster==2 & cell_type=="Cancer cell") %>%
+  select(c(7,8,9))
+```
+
+From the results, we found PDGRFA, CAV1 and ACTA2 pointing to Fibroblast in cancer cells
+
+Check again using FeaturePlot()
+
+```{r}
+genes <- c("PDGFRA", "CAV1", "ACTA2")
+FeaturePlot(sobj, features = genes)
+```
+
+## Step 6. Annotate the cells
+
+rename cluster 2 to Fibroblasts using RenameIdents()
+
+```{r}
+sobj_new <- RenameIdents(sobj, "2" = 'Fibroblasts')
+DimPlot(sobj_new, reduction = 'umap', label = TRUE)
+```
+
+## Step 7. Differentially expressed genes (Bonus)
+
+Can we distinguish cluster 7?
+
+```{r}
+merged_cellmarker %>%
+  filter(cluster==7 & cell_type=="Cancer cell") %>%
+  select(c(7,8,9)) %>%
+  str()
+```
+
+```{r}
+
+
+BiocManager::install("clusterProfiler")
+BiocManager::install("org.Hs.eg.db")
+BiocManager::install("DOSE")
+ 
+options(connectionObserver = NULL)
+
+library(clusterProfiler)
+library(org.Hs.eg.db)
+library(DOSE)
+```
+
+```{r}
+marker_7_2 <- FindMarkers(sobj, ident.1 = 7, ident.2 = 2)
+marker_7_2_sig <- marker_7_2 %>%
+  filter(abs(avg_log2FC)>=1 & p_val_adj<0.05 & pct.2<0.5)
+str(marker_7_2_sig)
+```
+
+```{r}
+log2 <- marker_7_2_sig$avg_log2FC
+names(log2) <- rownames(marker_7_2_sig)
+
+log2_sort <- sort(log2, decreasing = TRUE)
+log2_sort[1:5]
+```
+
+**Run gene set enrichment analysis:**
+
+```{r}
+gse <- gseGO(geneList =log2_sort,
+             ont="ALL",
+             keyType="SYMBOL",
+             nPerm=10000,
+             OrgDb = "org.Hs.eg.db")
+```
+
+Visualize the results
+
+```{r}
+dotplot(gse)
+```
+
+```         
+```
+
+Pur the identity back
+
+```{r}
+sobj_new <- RenameIdents(sobj, '7' = 'Mesenchymal Cells', '2' = 'Fibroblasts')
+DimPlot(sobj_new, reduction = 'umap', label = TRUE)
+```
+
+```{r}
+
+```

scrnaseq_tutorial.Rproj

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