From 9c1029fa2f0ad89dfc2ed4c9466d1194890566b9 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 10:32:46 +0200 Subject: [PATCH 01/21] Initializing IOC single cell structure --- docs/scRNAseq_basics/program_sc.md | 1 + mkdocs.yml | 41 +++++++++++++++--------------- 2 files changed, 22 insertions(+), 20 deletions(-) create mode 100644 docs/scRNAseq_basics/program_sc.md diff --git a/docs/scRNAseq_basics/program_sc.md b/docs/scRNAseq_basics/program_sc.md new file mode 100644 index 00000000..db476507 --- /dev/null +++ b/docs/scRNAseq_basics/program_sc.md @@ -0,0 +1 @@ +In this Interactive Online Companionship which will be held in **April 2024**. We will train to use the R programming language for single cell RNAseq analysis. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 56af72d5..e52e5296 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -148,28 +148,29 @@ nav: - Visualization: R-IOC/r09_viz_ggplot2.md - Storage room: R-IOC/pick-up.md - - Single-cell RNAseq analysis using Seurat in R: - - Introduction: scRNAseq_basics/introduction.md - - Prerequisites: scRNAseq_basics/prerequisites.md - - Initialization of R analysis: scRNAseq_basics/intro_seurat.md - - Import data: + - IOC on Single-cell RNAseq analysis using Seurat in R: + - Program / Schedule : + - Schedule : scRNAseq_basics/program_sc.md + - Week0 : scRNAseq_basics/import_exo.md + - Week1 : scRNAseq_basics/preprocessing_exo.md + - Reference Manual: + - Introduction: scRNAseq_basics/introduction.md + - Prerequisites: scRNAseq_basics/prerequisites.md + - Initialization of R analysis: scRNAseq_basics/intro_seurat.md - Import data: scRNAseq_basics/import.md - - Exercices: scRNAseq_basics/import_exo.md - - Preprocessing: - Preprocessing: scRNAseq_basics/preprocessing.md - - Exercices: scRNAseq_basics/preprocessing_exo.md - - Reduction of dimensionality: scRNAseq_basics/redim.md - - Clustering: scRNAseq_basics/clustering.md - - Cell population identification: - - Introduction and markers identification: scRNAseq_basics/intro_markers.md - - Markers Annotation: - - Presentation of annotation methods: scRNAseq_basics/marker_annot.md - - Over-representation analysis: scRNAseq_basics/over.md - - Gene Set Enrichment Analysis: scRNAseq_basics/gsea.md - - Cluster annotation: scRNAseq_basics/clusannot.md - - Comparison between 2 populations: scRNAseq_basics/comp.md - - Differents visualizations of Seurat: scRNAseq_basics/visualization.md - - References: scRNAseq_basics/references.md + - Reduction of dimensionality: scRNAseq_basics/redim.md + - Clustering: scRNAseq_basics/clustering.md + - Cell population identification: + - Introduction and markers identification: scRNAseq_basics/intro_markers.md + - Markers Annotation: + - Presentation of annotation methods: scRNAseq_basics/marker_annot.md + - Over-representation analysis: scRNAseq_basics/over.md + - Gene Set Enrichment Analysis: scRNAseq_basics/gsea.md + - Cluster annotation: scRNAseq_basics/clusannot.md + - Comparison between 2 populations: scRNAseq_basics/comp.md + - Differents visualizations of Seurat: scRNAseq_basics/visualization.md + - References: scRNAseq_basics/references.md - Introduction to Deep-seq Analysis: - INTRODUCTION: deepseq/index.md From 03900427432aa79017578d95d66dd27b62295aff Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 15:44:12 +0200 Subject: [PATCH 02/21] add scRNAseq program --- program_sc.md | 73 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 program_sc.md diff --git a/program_sc.md b/program_sc.md new file mode 100644 index 00000000..7b90f395 --- /dev/null +++ b/program_sc.md @@ -0,0 +1,73 @@ +In this Interactive Online Companionship which will be held in **April 2024**. +We will train to use the R programming language for single cell RNAseq data analysis. + +### Week 0 - 24/04/2024 + +1. Introduction of the Companions and Instructors (10 min) +2. Presentation of the IOC general workflow (Scheme) (15 min) +3. Work Program of the week 0 + a. Import data + b. Create a genome annotation with Biomart + c. First steps with Seurat + +### Week 1 - 30/04/2024¶ + +1. Questions on Week 0 [Visioconference] +2. Preprocessing +3. How to filter out low quality barcodes +4. Normalize data +5. Identify Highly Variable Genes (HVG) +6. Program of Week 1 + a. Exercices with RStudio and R scripting + +### Week 2 - 08/05/2024 + +1. Questions on Week 1 [Visioconference] +2. Reduction of dimensionality (PCA & UMAP) +3. Inspect PCA +4. Program of Week 2 + a. Exercices with RStudio and R scripting + +### Week 3 - 15/05/2024¶ + +1. Questions on Week 2 [Visioconference] +2. Clustering barcodes +3. Choosing a meaningful partition +4. Program of Week 3 + a. Exercices with RStudio and R scripting + +### Week 4 - 22/05/2024¶ + +1. Questions on Week 3 [Visioconference] +2. Visualization of gene markers for each cluster +3. Differential Gene Expression +4. Program of Week 4 + a. Exercices with RStudio and R scripting + +### Week 5 - 29/05/2024¶ + +1. Questions on Week 4 [Visioconference] +2. Over-representation Analysis with ClusterProfiler +3. Program of Week 5 + a. Exercices with RStudio and R scripting + +### Week 6 - 05/06/2024¶ + +1. Questions on Week 5 [Visioconference] +2. Gene Set Enrichment Analysis with ClusterProfiler +3. Program of Week 6 + a. Exercices with RStudio and R scripting + +### Week 7 - 12/06/2024¶ + +1. Questions on Week 6 [Visioconference] +2. Assign cell type identity to clusters +3. Visualizations +4. Program of Week 7 + a. Exercices with RStudio and R scripting + b. Small project with a specific objective + +### Week 8 - Presentations of the analyses by the companions¶ (september 2024) + +1. Questions on Week 7 (30 min max) [Visioconference] +2. 20 min presentations by the attendees From 35c779d5a8e45f5b8dd7b0f58d3434ce1af5ada1 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 15:44:25 +0200 Subject: [PATCH 03/21] include scRNAseq program --- mkdocs.yml | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index e52e5296..1fb1347b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -151,8 +151,15 @@ nav: - IOC on Single-cell RNAseq analysis using Seurat in R: - Program / Schedule : - Schedule : scRNAseq_basics/program_sc.md - - Week0 : scRNAseq_basics/import_exo.md - - Week1 : scRNAseq_basics/preprocessing_exo.md + - Week 0: scRNAseq_basics/00_IOCsc_week0.md # import_annot + - Week 1: scRNAseq_basics/01_IOCsc_week1.md # preprocessing_hvg + - Week 2: scRNAseq_basics/02_IOCsc_week2.md # reduction_dimension + - Week 3: scRNAseq_basics/03_IOCsc_week3.md # clustering + - Week 4: scRNAseq_basics/04_IOCsc_week4.md # differential_expression + - Week 5: scRNAseq_basics/05_IOCsc_week5.md # ora + - Week 6: scRNAseq_basics/06_IOCsc_week6.md # gsea + - Week 7: scRNAseq_basics/07_IOCsc_week7.md # visualisation + - Week 8: scRNAseq_basics/08_IOCsc_week8.md # presentation_results - Reference Manual: - Introduction: scRNAseq_basics/introduction.md - Prerequisites: scRNAseq_basics/prerequisites.md From fe6f949f0c34ea6fa8a9f10bcef4152a67bb6d44 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 15:51:17 +0200 Subject: [PATCH 04/21] init weekly md files --- docs/scRNAseq_basics/00_IOCsc_week0.md | 0 docs/scRNAseq_basics/01_IOCsc_week1.md | 0 docs/scRNAseq_basics/02_IOCsc_week2.md | 0 docs/scRNAseq_basics/03_IOCsc_week3.md | 0 docs/scRNAseq_basics/04_IOCsc_week4.md | 0 docs/scRNAseq_basics/05_IOCsc_week5.md | 0 docs/scRNAseq_basics/06_IOCsc_week6.md | 0 docs/scRNAseq_basics/07_IOCsc_week7.md | 0 docs/scRNAseq_basics/08_IOCsc_week8.md | 0 9 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/scRNAseq_basics/00_IOCsc_week0.md create mode 100644 docs/scRNAseq_basics/01_IOCsc_week1.md create mode 100644 docs/scRNAseq_basics/02_IOCsc_week2.md create mode 100644 docs/scRNAseq_basics/03_IOCsc_week3.md create mode 100644 docs/scRNAseq_basics/04_IOCsc_week4.md create mode 100644 docs/scRNAseq_basics/05_IOCsc_week5.md create mode 100644 docs/scRNAseq_basics/06_IOCsc_week6.md create mode 100644 docs/scRNAseq_basics/07_IOCsc_week7.md create mode 100644 docs/scRNAseq_basics/08_IOCsc_week8.md diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/01_IOCsc_week1.md b/docs/scRNAseq_basics/01_IOCsc_week1.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/02_IOCsc_week2.md b/docs/scRNAseq_basics/02_IOCsc_week2.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/03_IOCsc_week3.md b/docs/scRNAseq_basics/03_IOCsc_week3.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/04_IOCsc_week4.md b/docs/scRNAseq_basics/04_IOCsc_week4.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/05_IOCsc_week5.md b/docs/scRNAseq_basics/05_IOCsc_week5.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/06_IOCsc_week6.md b/docs/scRNAseq_basics/06_IOCsc_week6.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/07_IOCsc_week7.md b/docs/scRNAseq_basics/07_IOCsc_week7.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/scRNAseq_basics/08_IOCsc_week8.md b/docs/scRNAseq_basics/08_IOCsc_week8.md new file mode 100644 index 00000000..e69de29b From dfb9c0e547c5bbf09f4fad75fa451bda18e4d031 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:18:19 +0200 Subject: [PATCH 05/21] move the file to the right place --- docs/scRNAseq_basics/program_sc.md | 74 +++++++++++++++++++++++++++++- program_sc.md | 73 ----------------------------- 2 files changed, 73 insertions(+), 74 deletions(-) delete mode 100644 program_sc.md diff --git a/docs/scRNAseq_basics/program_sc.md b/docs/scRNAseq_basics/program_sc.md index db476507..7b90f395 100644 --- a/docs/scRNAseq_basics/program_sc.md +++ b/docs/scRNAseq_basics/program_sc.md @@ -1 +1,73 @@ -In this Interactive Online Companionship which will be held in **April 2024**. We will train to use the R programming language for single cell RNAseq analysis. \ No newline at end of file +In this Interactive Online Companionship which will be held in **April 2024**. +We will train to use the R programming language for single cell RNAseq data analysis. + +### Week 0 - 24/04/2024 + +1. Introduction of the Companions and Instructors (10 min) +2. Presentation of the IOC general workflow (Scheme) (15 min) +3. Work Program of the week 0 + a. Import data + b. Create a genome annotation with Biomart + c. First steps with Seurat + +### Week 1 - 30/04/2024¶ + +1. Questions on Week 0 [Visioconference] +2. Preprocessing +3. How to filter out low quality barcodes +4. Normalize data +5. Identify Highly Variable Genes (HVG) +6. Program of Week 1 + a. Exercices with RStudio and R scripting + +### Week 2 - 08/05/2024 + +1. Questions on Week 1 [Visioconference] +2. Reduction of dimensionality (PCA & UMAP) +3. Inspect PCA +4. Program of Week 2 + a. Exercices with RStudio and R scripting + +### Week 3 - 15/05/2024¶ + +1. Questions on Week 2 [Visioconference] +2. Clustering barcodes +3. Choosing a meaningful partition +4. Program of Week 3 + a. Exercices with RStudio and R scripting + +### Week 4 - 22/05/2024¶ + +1. Questions on Week 3 [Visioconference] +2. Visualization of gene markers for each cluster +3. Differential Gene Expression +4. Program of Week 4 + a. Exercices with RStudio and R scripting + +### Week 5 - 29/05/2024¶ + +1. Questions on Week 4 [Visioconference] +2. Over-representation Analysis with ClusterProfiler +3. Program of Week 5 + a. Exercices with RStudio and R scripting + +### Week 6 - 05/06/2024¶ + +1. Questions on Week 5 [Visioconference] +2. Gene Set Enrichment Analysis with ClusterProfiler +3. Program of Week 6 + a. Exercices with RStudio and R scripting + +### Week 7 - 12/06/2024¶ + +1. Questions on Week 6 [Visioconference] +2. Assign cell type identity to clusters +3. Visualizations +4. Program of Week 7 + a. Exercices with RStudio and R scripting + b. Small project with a specific objective + +### Week 8 - Presentations of the analyses by the companions¶ (september 2024) + +1. Questions on Week 7 (30 min max) [Visioconference] +2. 20 min presentations by the attendees diff --git a/program_sc.md b/program_sc.md deleted file mode 100644 index 7b90f395..00000000 --- a/program_sc.md +++ /dev/null @@ -1,73 +0,0 @@ -In this Interactive Online Companionship which will be held in **April 2024**. -We will train to use the R programming language for single cell RNAseq data analysis. - -### Week 0 - 24/04/2024 - -1. Introduction of the Companions and Instructors (10 min) -2. Presentation of the IOC general workflow (Scheme) (15 min) -3. Work Program of the week 0 - a. Import data - b. Create a genome annotation with Biomart - c. First steps with Seurat - -### Week 1 - 30/04/2024¶ - -1. Questions on Week 0 [Visioconference] -2. Preprocessing -3. How to filter out low quality barcodes -4. Normalize data -5. Identify Highly Variable Genes (HVG) -6. Program of Week 1 - a. Exercices with RStudio and R scripting - -### Week 2 - 08/05/2024 - -1. Questions on Week 1 [Visioconference] -2. Reduction of dimensionality (PCA & UMAP) -3. Inspect PCA -4. Program of Week 2 - a. Exercices with RStudio and R scripting - -### Week 3 - 15/05/2024¶ - -1. Questions on Week 2 [Visioconference] -2. Clustering barcodes -3. Choosing a meaningful partition -4. Program of Week 3 - a. Exercices with RStudio and R scripting - -### Week 4 - 22/05/2024¶ - -1. Questions on Week 3 [Visioconference] -2. Visualization of gene markers for each cluster -3. Differential Gene Expression -4. Program of Week 4 - a. Exercices with RStudio and R scripting - -### Week 5 - 29/05/2024¶ - -1. Questions on Week 4 [Visioconference] -2. Over-representation Analysis with ClusterProfiler -3. Program of Week 5 - a. Exercices with RStudio and R scripting - -### Week 6 - 05/06/2024¶ - -1. Questions on Week 5 [Visioconference] -2. Gene Set Enrichment Analysis with ClusterProfiler -3. Program of Week 6 - a. Exercices with RStudio and R scripting - -### Week 7 - 12/06/2024¶ - -1. Questions on Week 6 [Visioconference] -2. Assign cell type identity to clusters -3. Visualizations -4. Program of Week 7 - a. Exercices with RStudio and R scripting - b. Small project with a specific objective - -### Week 8 - Presentations of the analyses by the companions¶ (september 2024) - -1. Questions on Week 7 (30 min max) [Visioconference] -2. 20 min presentations by the attendees From 98c89a62990376b110740013cb968e355f5039f7 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:29:29 +0200 Subject: [PATCH 06/21] remove useless week --- docs/scRNAseq_basics/08_IOCsc_week8.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docs/scRNAseq_basics/08_IOCsc_week8.md diff --git a/docs/scRNAseq_basics/08_IOCsc_week8.md b/docs/scRNAseq_basics/08_IOCsc_week8.md deleted file mode 100644 index e69de29b..00000000 From 7f62911063e76b365cf4497e4a86aa85f19dd089 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:30:53 +0200 Subject: [PATCH 07/21] add content name in program titles --- mkdocs.yml | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 1fb1347b..0c51a3d0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -151,15 +151,14 @@ nav: - IOC on Single-cell RNAseq analysis using Seurat in R: - Program / Schedule : - Schedule : scRNAseq_basics/program_sc.md - - Week 0: scRNAseq_basics/00_IOCsc_week0.md # import_annot - - Week 1: scRNAseq_basics/01_IOCsc_week1.md # preprocessing_hvg - - Week 2: scRNAseq_basics/02_IOCsc_week2.md # reduction_dimension - - Week 3: scRNAseq_basics/03_IOCsc_week3.md # clustering - - Week 4: scRNAseq_basics/04_IOCsc_week4.md # differential_expression - - Week 5: scRNAseq_basics/05_IOCsc_week5.md # ora - - Week 6: scRNAseq_basics/06_IOCsc_week6.md # gsea - - Week 7: scRNAseq_basics/07_IOCsc_week7.md # visualisation - - Week 8: scRNAseq_basics/08_IOCsc_week8.md # presentation_results + - Week 0 - Introduction, Data Import & Annotation: scRNAseq_basics/00_IOCsc_week0.md + - Week 1 - Data Processing & High Variable Genes: scRNAseq_basics/01_IOCsc_week1.md + - Week 2 - Reduction of Dimensionality: scRNAseq_basics/02_IOCsc_week2.md + - Week 3 - Clustering: scRNAseq_basics/03_IOCsc_week3.md + - Week 4 - Differential Gene Expression: scRNAseq_basics/04_IOCsc_week4.md + - Week 5 - Enrichment Analysis (ORA): scRNAseq_basics/05_IOCsc_week5.md + - Week 6 - Enrichment Analysis (GSEA): scRNAseq_basics/06_IOCsc_week6.md + - Week 7 - Visualisation: scRNAseq_basics/07_IOCsc_week7.md - Reference Manual: - Introduction: scRNAseq_basics/introduction.md - Prerequisites: scRNAseq_basics/prerequisites.md From 37ba10a293a03fe8270fa46819e7c4aca0b39e69 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 16:42:52 +0200 Subject: [PATCH 08/21] update v4 to v5 import data --- docs/scRNAseq_basics/import.md | 115 +++++++++++++++++---------------- 1 file changed, 60 insertions(+), 55 deletions(-) diff --git a/docs/scRNAseq_basics/import.md b/docs/scRNAseq_basics/import.md index 217945e1..e302c43c 100644 --- a/docs/scRNAseq_basics/import.md +++ b/docs/scRNAseq_basics/import.md @@ -232,31 +232,41 @@ pbmc_small <- CreateSeuratObject(tenX_matrix, #Expression mat ## Formal class 'Seurat' [package "SeuratObject"] with 13 slots ## ..@ assays :List of 1 - ## .. ..$ RNA:Formal class 'Assay' [package "SeuratObject"] with 8 slots - ## .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots - ## .. .. .. .. .. ..@ i : int [1:2286884] 70 166 178 326 363 410 412 492 494 495 ... - ## .. .. .. .. .. ..@ p : int [1:2701] 0 781 2133 3264 4224 4746 5528 6311 7101 7634 ... - ## .. .. .. .. .. ..@ Dim : int [1:2] 32738 2700 - ## .. .. .. .. .. ..@ Dimnames:List of 2 - ## .. .. .. .. .. .. ..$ : chr [1:32738] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009" ... + ## .. ..$ RNA:Formal class 'Assay5' [package "SeuratObject"] with 8 slots + ## .. .. .. ..@ layers :List of 1 + ## .. .. .. .. ..$ counts:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots + ## .. .. .. .. .. .. ..@ i : int [1:2286884] 70 166 178 326 363 410 412 492 494 495 ... + ## .. .. .. .. .. .. ..@ p : int [1:2701] 0 781 2133 3264 4224 4746 5528 6311 7101 7634 ... + ## .. .. .. .. .. .. ..@ Dim : int [1:2] 32738 2700 + ## .. .. .. .. .. .. ..@ Dimnames:List of 2 + ## .. .. .. .. .. .. .. ..$ : NULL + ## .. .. .. .. .. .. .. ..$ : NULL + ## .. .. .. .. .. .. ..@ x : num [1:2286884] 1 1 2 1 1 1 1 41 1 1 ... + ## .. .. .. .. .. .. ..@ factors : list() + ## .. .. .. ..@ cells :Formal class 'LogMap' [package "SeuratObject"] with 1 slot + ## .. .. .. .. .. ..@ .Data: logi [1:2700, 1] TRUE TRUE TRUE TRUE TRUE TRUE ... + ## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2 + ## .. .. .. .. .. .. .. ..$ : chr [1:2700] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ... + ## .. .. .. .. .. .. .. ..$ : chr "counts" + ## .. .. .. .. .. ..$ dim : int [1:2] 2700 1 + ## .. .. .. .. .. ..$ dimnames:List of 2 ## .. .. .. .. .. .. ..$ : chr [1:2700] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ... - ## .. .. .. .. .. ..@ x : num [1:2286884] 1 1 2 1 1 1 1 41 1 1 ... - ## .. .. .. .. .. ..@ factors : list() - ## .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots - ## .. .. .. .. .. ..@ i : int [1:2286884] 70 166 178 326 363 410 412 492 494 495 ... - ## .. .. .. .. .. ..@ p : int [1:2701] 0 781 2133 3264 4224 4746 5528 6311 7101 7634 ... - ## .. .. .. .. .. ..@ Dim : int [1:2] 32738 2700 - ## .. .. .. .. .. ..@ Dimnames:List of 2 + ## .. .. .. .. .. .. ..$ : chr "counts" + ## .. .. .. ..@ features :Formal class 'LogMap' [package "SeuratObject"] with 1 slot + ## .. .. .. .. .. ..@ .Data: logi [1:32738, 1] TRUE TRUE TRUE TRUE TRUE TRUE ... + ## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2 + ## .. .. .. .. .. .. .. ..$ : chr [1:32738] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009" ... + ## .. .. .. .. .. .. .. ..$ : chr "counts" + ## .. .. .. .. .. ..$ dim : int [1:2] 32738 1 + ## .. .. .. .. .. ..$ dimnames:List of 2 ## .. .. .. .. .. .. ..$ : chr [1:32738] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009" ... - ## .. .. .. .. .. .. ..$ : chr [1:2700] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ... - ## .. .. .. .. .. ..@ x : num [1:2286884] 1 1 2 1 1 1 1 41 1 1 ... - ## .. .. .. .. .. ..@ factors : list() - ## .. .. .. ..@ scale.data : num[0 , 0 ] - ## .. .. .. ..@ key : chr "rna_" - ## .. .. .. ..@ assay.orig : NULL - ## .. .. .. ..@ var.features : logi(0) - ## .. .. .. ..@ meta.features:'data.frame': 32738 obs. of 0 variables - ## .. .. .. ..@ misc : list() + ## .. .. .. .. .. .. ..$ : chr "counts" + ## .. .. .. ..@ default : int 1 + ## .. .. .. ..@ assay.orig: chr(0) + ## .. .. .. ..@ meta.data :'data.frame': 32738 obs. of 0 variables + ## .. .. .. ..@ misc :List of 1 + ## .. .. .. .. ..$ calcN: logi TRUE + ## .. .. .. ..@ key : chr "rna_" ## ..@ meta.data :'data.frame': 2700 obs. of 3 variables: ## .. ..$ orig.ident : Factor w/ 1 level "PBMC analysis": 1 1 1 1 1 1 1 1 1 1 ... ## .. ..$ nCount_RNA : num [1:2700] 2421 4903 3149 2639 981 ... @@ -271,20 +281,14 @@ pbmc_small <- CreateSeuratObject(tenX_matrix, #Expression mat ## ..@ project.name: chr "PBMC analysis" ## ..@ misc : list() ## ..@ version :Classes 'package_version', 'numeric_version' hidden list of 1 - ## .. ..$ : int [1:3] 4 1 0 + ## .. ..$ : int [1:3] 5 0 1 ## ..@ commands : list() ## ..@ tools : list() === "Dimension of expression matrices" ``` r - dim(pbmc_small@assays$RNA@counts) - ``` - - ## [1] 32738 2700 - - ``` r - dim(pbmc_small@assays$RNA@data) + dim(pbmc_small[["RNA"]]) ``` ## [1] 32738 2700 @@ -306,29 +310,25 @@ pbmc_small <- CreateSeuratObject(tenX_matrix, #Expression mat We can observe several slots via the `str` command: -- `assays`: general slot that will include the different information of each - study. They are composed of several things: - - the starting expression matrix (`@counts`), usually raw counts or raw UMI - - the one that will "undergo" all the modifications (filters, - normalization, etc) (`@data`) - - dataframe that will be created when scaling the data (`@scale_data`) - - prefix used for each calculation that will use this assay (study) - (`@key`) - - vector of gene names that will be determined to have a variable - expression (`@var.features`) - - dataframe associated with genes with different metadata - (`@meta.features`) - -- `meta.data` : gathers all the information about the cells. At the beginning - Seurat will calculate the size of the library (nCount_RNA, or the total - number of UMI) and the number of detected genes (nFeatures_RNA) for each - cell. If a dataframe is given in the `meta.data` parameter of the - `CreateSeuratObject` function, its columns will be added after those - calculated by Seurat. - -- `active.assay` : study used by default -- `active.ident` : default cell identity, here the name of the given project, - also stored under the column `orig.ident` in the metadata +- `assays`: general slot that will include the different information + of each study. It’s a list where each element is linked to a + expression data type. For instance in the element `RNA` you retrieve + all information link to you scRNAseq. But for example, if you have a + more complicated study you can also have `ADT` or `peaks` if you + perform a CITEseq, scATACseq or Multiome. Here we have a simple + scRNAseq, so by default `assays` will be composed of only the + element `RNA`. It’s class is `Assay5` you can have a great + description in the documentation of the `SeuratObject` package. In + you console `?Assay5` to read it. +- `meta.data` : gathers all the information about the cells. At the + beginning Seurat will calculate the size of the library (nCount_RNA, + or the total number of UMI) and the number of detected genes + (nFeatures_RNA) for each cell. If a dataframe is given in the + `meta.data` parameter of the `CreateSeuratObject` function, its + columns will be added after those calculated by Seurat. +- `active.assay` : study used by default +- `active.ident` : default cell identity, here the name of the given + project, also stored under the column `orig.ident` in the metadata !!! note Navigation in the different slots is done via `@` or `$`. Each main slot is @@ -336,4 +336,9 @@ We can observe several slots via the `str` command: slots tree, most often complex objects are accessible with a `@` (dgCMatrix, dataframe) and lists, vectors are accessible via `$`. If in doubt, you can refer to the result of the `str` command and use the character in front of - each slot name. + each slot name. If you want to look to the expression matrix you can go : + + ```r + # Retrieve data in an expression matrix RNA counts matrix + pbmc_small[["RNA"]]$counts[1:10,1:5] + ``` From 6d08d1feb1ea11194ce41c9b0804544bd496f1c7 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 16:44:28 +0200 Subject: [PATCH 09/21] update v4 to v5 preprocessing and images + add small explainations --- docs/scRNAseq_basics/images/MitoGenes-1.png | Bin 133600 -> 134043 bytes docs/scRNAseq_basics/images/MitoGenes-2.png | Bin 60705 -> 61502 bytes docs/scRNAseq_basics/images/QCFilter-1.png | Bin 80140 -> 80068 bytes .../images/VariableFeature-1.png | Bin 57559 -> 57981 bytes docs/scRNAseq_basics/preprocessing.md | 26 ++++++++++-------- 5 files changed, 15 insertions(+), 11 deletions(-) diff --git a/docs/scRNAseq_basics/images/MitoGenes-1.png b/docs/scRNAseq_basics/images/MitoGenes-1.png index bebbb742c85b37dcdf8983f1e59df3805dd1b8e9..21a75d49f1ea9c4821db499ae889b7ebb8089bda 100644 GIT binary patch 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zU0)+hvUnZ!y?*i+55V&nM*v@gS)t*GD=_r>1O^ct>oTs$(CZ)I4Qug?j4AdJr=^r?uD15jyw2xpJH0$orXP^8F0y9?3a4!m?#|9=zV)&6b$ mc&DVg%5j7LGbpfmbIV6_6 -The function `FindVariableFeatures` updates two slots: +The function `FindVariableFeatures` updates a slot: -- `pbmc_small@assays$RNA@var.features` : vector of n genes - genes determined to be the most variable -- `pbmc_small@assays$RNA@meta.features` : dataframe containing the different +- `object@assays[["RNA"]]@meta.data` : dataframe containing the different variables calculated by the vst method. For each gene we have : - - `vst.mean` : expression mean - - `vst.variance` : expression variance - - `vst.variance.expected` : expected variance - - `vst.variance.standardized` : standardized variance - - `vst.variable` : logical, is the gene a variable gene TRUE / FALSE + - `vf_vst_counts_mean` : expression mean + - `vf_vst_counts_variance` : expression variance + - `vf_vst_counts_variance.expected` : expected variance + - `vf_vst_counts_variance.standardized` : standardized variance + - `vf_vst_counts_variable` : logical, is the gene a variable gene TRUE / FALSE + - `vf_vst_counts_rank` : rank of HGV (if `NA` the gene is not an HGV) + - `var.features` : if `NA` the gene is not HGV, else you'll see the gene name + - `var.features.rank` : rank of HGV (if `NA` the gene is not an HGV), same as + `vf_vst_counts_rank` column if `vst` is the last or only HGV method. !!! tip - We can directly access to HGV via : `VariableFeatures(pbmc_small)` + We can directly access to HGV via : `VariableFeatures(object)` From 60b019b0b069506be8efbd60ed4b248c1af6698b Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 16:47:04 +0200 Subject: [PATCH 10/21] update v4 to v5 redim and images + add pc_to_keep variable --- docs/scRNAseq_basics/images/ElbowPlot-1.png | Bin 7039 -> 7078 bytes docs/scRNAseq_basics/images/PCA-1.png | Bin 80317 -> 80545 bytes docs/scRNAseq_basics/images/UMAP-1.png | Bin 80346 -> 80342 bytes .../images/plotJackStraw-1.png | Bin 186017 -> 186158 bytes docs/scRNAseq_basics/redim.md | 18 ++++++++++++++---- 5 files changed, 14 insertions(+), 4 deletions(-) diff --git a/docs/scRNAseq_basics/images/ElbowPlot-1.png b/docs/scRNAseq_basics/images/ElbowPlot-1.png index 1176a5e98f10858fa078f49b2e0256adcac5b4a6..ef93cc4f524393c0e106c06487c75c8df904c9c2 100644 GIT binary patch delta 5647 zcmZ`-c|6ox*#FHKTe9!6Oe$*jMBxO_U$3#Ut-e0N^n((AKaBOj#Ia@U`g0VO9h*;a`x^7KGI>u>^)F zmb1Oy*RSSJe!qd!)`^qUER`Cuy8b+xE8GfhsqH?`;PQ{1Bvx!ab6tmnVbHAc^+psS 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z4}&S!&2w{egL8$kFVJ&5$HW9JIVEpzZ!i%G(h~@xqs7V`Y?(HQjMpngPA4ZPH~8?v zuYc4xF$;o4{PIfpuNtxl+(QJ3C_G8_>*q#>|M`822K?JH>~P}n!aFtm*MI;0pSO;Bjl diff --git a/docs/scRNAseq_basics/redim.md b/docs/scRNAseq_basics/redim.md index a933addc..9b06720f 100644 --- a/docs/scRNAseq_basics/redim.md +++ b/docs/scRNAseq_basics/redim.md @@ -23,8 +23,14 @@ $$ scaled.data(x) = \frac{x - mean(x)}{ standard.deviation(x)} $$ pbmc_small <- ScaleData(pbmc_small) ``` -The slot `object@assays$RNA@scale.data` is now filled. It is a -dataframe of dimensions `n HVG x n cells` of reduced centered data. +The slot `object@assays[["RNA"]]@layers$scale.data` is now filled. +It is a dataframe of dimensions `n HVG x n cells` of reduced centered data. + +!!! note + You can use `ScaleData` on all genes (easier if you need to visualize + gene expression in an heatmap but be carefull and specify + `features = VariableFeatures(pbmc_small)` in the `RunPCA` function + explained below) ## Principal Component Analysis (PCA) @@ -101,7 +107,7 @@ of the PCs with that of PCs computed from permuted data. The `JackStraw` function will perform 100 permutations (default value of the `num.replicate` parameter). At each permutation, it will randomly select 1% of the most variable genes (default proportion, parameter `prop.freq`). -`JackStraw` will mix the values of the slot `object@assays$RNA@scale.data` +`JackStraw` will mix the values of the slot `object@assays[["RNA"]]@layers$scale.data` filter on these genes and then perform a PCA on this fake matrix. From these results and for each PC, it calculates the number of times the values of the fake loadings ($fakevals$) is greater than each value of the @@ -175,6 +181,10 @@ Straw results. -> We can determine the choice of the number of PCs at **10**. +``` r +pc_to_keep <- 10 +``` + !!! note I find the Elbow Plot method much more complicated to decide how many PCs to keep. But it allows to have a second opinion, at choice I prefer to @@ -212,7 +222,7 @@ pbmc_small <- RunUMAP(pbmc_small, #SeuratObject reduction = "pca", #Reduction used to compute UMAP reduction.key = "UMAP_", #Dimension prefix assay = "RNA", #Assay to use - dims = 1:10) #Number of PCs to keep (previously determined) + dims = 1:pc_to_keep) #Number of PCs to keep (previously determined) ## Plot UMAPPlot(pbmc_small) From 2d481912ed01a7f745775914b1f18dbca54d643f Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:53:38 +0200 Subject: [PATCH 11/21] change sub list symbol --- docs/scRNAseq_basics/program_sc.md | 37 +++++++++++++++--------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/docs/scRNAseq_basics/program_sc.md b/docs/scRNAseq_basics/program_sc.md index 7b90f395..f8eab624 100644 --- a/docs/scRNAseq_basics/program_sc.md +++ b/docs/scRNAseq_basics/program_sc.md @@ -5,10 +5,9 @@ We will train to use the R programming language for single cell RNAseq data anal 1. Introduction of the Companions and Instructors (10 min) 2. Presentation of the IOC general workflow (Scheme) (15 min) -3. Work Program of the week 0 - a. Import data - b. Create a genome annotation with Biomart - c. First steps with Seurat +3. Work Program of the week 0 + - Import data + - Create a genome annotation with Biomart ### Week 1 - 30/04/2024¶ @@ -17,57 +16,57 @@ We will train to use the R programming language for single cell RNAseq data anal 3. How to filter out low quality barcodes 4. Normalize data 5. Identify Highly Variable Genes (HVG) -6. Program of Week 1 - a. Exercices with RStudio and R scripting +6. Program of Week 1 + - Exercices with RStudio and R scripting ### Week 2 - 08/05/2024 1. Questions on Week 1 [Visioconference] 2. Reduction of dimensionality (PCA & UMAP) 3. Inspect PCA -4. Program of Week 2 - a. Exercices with RStudio and R scripting +4. Program of Week 2 + - Exercices with RStudio and R scripting ### Week 3 - 15/05/2024¶ 1. Questions on Week 2 [Visioconference] 2. Clustering barcodes 3. Choosing a meaningful partition -4. Program of Week 3 - a. Exercices with RStudio and R scripting +4. Program of Week 3 + - Exercices with RStudio and R scripting ### Week 4 - 22/05/2024¶ 1. Questions on Week 3 [Visioconference] 2. Visualization of gene markers for each cluster 3. Differential Gene Expression -4. Program of Week 4 - a. Exercices with RStudio and R scripting +4. Program of Week 4 + - Exercices with RStudio and R scripting ### Week 5 - 29/05/2024¶ 1. Questions on Week 4 [Visioconference] 2. Over-representation Analysis with ClusterProfiler -3. Program of Week 5 - a. Exercices with RStudio and R scripting +3. Program of Week 5 + - Exercices with RStudio and R scripting ### Week 6 - 05/06/2024¶ 1. Questions on Week 5 [Visioconference] 2. Gene Set Enrichment Analysis with ClusterProfiler 3. Program of Week 6 - a. Exercices with RStudio and R scripting + - Exercices with RStudio and R scripting ### Week 7 - 12/06/2024¶ 1. Questions on Week 6 [Visioconference] 2. Assign cell type identity to clusters 3. Visualizations -4. Program of Week 7 - a. Exercices with RStudio and R scripting - b. Small project with a specific objective +4. Program of Week 7 + - Exercices with RStudio and R scripting + - Small project with a specific objective ### Week 8 - Presentations of the analyses by the companions¶ (september 2024) -1. Questions on Week 7 (30 min max) [Visioconference] +1. Questions on Week 7 (30 minutes max) [Visioconference] 2. 20 min presentations by the attendees From 2c7d69c7886d5cc3d64b541e647c888eb1ecd15a Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:58:17 +0200 Subject: [PATCH 12/21] add exo section --- docs/scRNAseq_basics/02_IOCsc_week2.md | 13 +++++++++++++ docs/scRNAseq_basics/03_IOCsc_week3.md | 13 +++++++++++++ docs/scRNAseq_basics/04_IOCsc_week4.md | 13 +++++++++++++ docs/scRNAseq_basics/05_IOCsc_week5.md | 13 +++++++++++++ docs/scRNAseq_basics/06_IOCsc_week6.md | 13 +++++++++++++ docs/scRNAseq_basics/07_IOCsc_week7.md | 13 +++++++++++++ 6 files changed, 78 insertions(+) diff --git a/docs/scRNAseq_basics/02_IOCsc_week2.md b/docs/scRNAseq_basics/02_IOCsc_week2.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/02_IOCsc_week2.md +++ b/docs/scRNAseq_basics/02_IOCsc_week2.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/03_IOCsc_week3.md b/docs/scRNAseq_basics/03_IOCsc_week3.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/03_IOCsc_week3.md +++ b/docs/scRNAseq_basics/03_IOCsc_week3.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/04_IOCsc_week4.md b/docs/scRNAseq_basics/04_IOCsc_week4.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/04_IOCsc_week4.md +++ b/docs/scRNAseq_basics/04_IOCsc_week4.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/05_IOCsc_week5.md b/docs/scRNAseq_basics/05_IOCsc_week5.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/05_IOCsc_week5.md +++ b/docs/scRNAseq_basics/05_IOCsc_week5.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/06_IOCsc_week6.md b/docs/scRNAseq_basics/06_IOCsc_week6.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/06_IOCsc_week6.md +++ b/docs/scRNAseq_basics/06_IOCsc_week6.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/07_IOCsc_week7.md b/docs/scRNAseq_basics/07_IOCsc_week7.md index e69de29b..d90195b7 100644 --- a/docs/scRNAseq_basics/07_IOCsc_week7.md +++ b/docs/scRNAseq_basics/07_IOCsc_week7.md @@ -0,0 +1,13 @@ + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + + + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file From c54e0b287a3f5b92fd65c3a8c348c8d3483a8095 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 16:59:57 +0200 Subject: [PATCH 13/21] draft week 0 and 1 --- docs/scRNAseq_basics/00_IOCsc_week0.md | 190 +++++++++++++++++++++++++ docs/scRNAseq_basics/01_IOCsc_week1.md | 19 +++ 2 files changed, 209 insertions(+) diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md index e69de29b..655dc6e5 100644 --- a/docs/scRNAseq_basics/00_IOCsc_week0.md +++ b/docs/scRNAseq_basics/00_IOCsc_week0.md @@ -0,0 +1,190 @@ +**3-hours Zoom video-conference** + +## Introduction of the Companions and Instructors (10 min) +## Presentation of the IOC general workflow (Scheme) (15 min) +![](../R-IOC/images/IOC-workflow.png) +## Presentation of the IOC tools (2 hours) +### Zoom +We currently use the Zoom software for our video-conferences. +They will be recorded and available off line in the psilo data server (see below) + +Please, follow these guides lines for Zoom usage + +1. Use a local, desktop Zoom application instead of the online web application. You can + download Zoom [here](https://zoom.us/fr/download){:target="_blank"} +- Test your Zoom application once if you never used it. We will be happy to arrange a quick + Zoom session a few days before the IOC if you feel that there may be an issue. +- Be sure that your internet connection is reasonably fast to allow __the use of your camera__. + We much value visual interactions! +- Arrange a quiet local place for your Zoom weekly session. People talking around you are + disturbing you as well as the other conference participants. If you cannot arrange to be + alone in your office, please warn you colleagues well ahead the session that you will + need peace. +- **Use a headset with a built-in microphone**. It's not a gimmick! There are now cheap + headsets for video-conference that works well. Test your headset with your computer + and Zoom well ahead the first IOC session. +- Prefer a Desktop (generally more powerful) to a Laptop computer. +- Use the largest screen you have (another reason not using a laptop). If you have two + screens, even better, but then test zoom with your dual screen setup. We may have to + leave open several windows and applications during the Zoom session. +- Be on time at the session! +- You are welcome to use the chat panel of Zoom to exchange links, code issues, *etc.*, but + Slack (see below) is likely better suited to this, especially because the Zoom chat is + lost when the application is shutdown. Therefore, be sure to have you Slack board available + during the Zoom sessions. + +There are many other interesting functionalities with Zoom, which will be covered in the +presentation. + +### Trello + +One of our favorite tools is Trello. +You will be invited to access to the [trello board](https://trello.com/b/GFH3LBOR/artbio063iocsinglecellrnaseq){:target="_blank"} +of the IOC. Not a lot to say about Trello. It is just a great tool to capture information, +collaborate, and organize projects. + +We hope that you will still use Trello for your own projects and purposes when the IOC is +finished! + +### STARTbio +Our [STARTbio web site](https://artbio.github.io/startbio/) is the hub where we connect +all the training materials for IOCs. +To access rapidly to your IOC-R, +use this [URL shortcut](https://artbio.github.io/startbio/R-IOC/00_IOC_R_program/). + +Here, you'll find all weekly lessons, exercises, instructions, *etc*. + +**Importantly**, you, yes, you, are welcome to propose modifications or fixes to the STARTbio +IOC web pages! +Assuming that during this IOC you will become familiar with the use of GitHub, all you +have to do is click on the pencil icon ![](images/github_pencil.png){width="25"} +at the top of each page and propose your modifications in a branch of our GitHub startbio +repository. + +### Slack +Slack is a workspace to exchange messages or files, follow conversations, communicate +about issues and ideas. + +If you haven't already done so, you will first need to open a +[Slack account](https://slack.com/get-started#/createnew){:target="_blank"} by providing +a username and password (you can also use authentication through Google or Apple). + +If you have already a Slack account, you can connect to this account using this +[URL](https://slack.com/signin#/signin){:target="_blank"}. + +!!! warning "Attention!" + If you have multiple login emails for your Slack account, it can become confusing if + some of your workspaces are identified with one email and others with another email. + + This might happen, for example, if you were invited to a Slack workspace with a + different email than the one you initially used to create your first Slack workspace. + +!!! tip "Get Slack app on your local computer" + We really strongly recommend that you use a desktop version of the slack application + on your computer(s). + + Once installed, this desktop Slack application will connect to + your Slack account(s) and import locally your workspace, including the workspace + dedicated to this IOC + + [Apple Desktop Slack](https://apps.apple.com/us/app/slack-for-desktop/id803453959?mt=12){:target="_blank"} + | [Windows Desktop Slack](https://slack.com/intl/fr-fr/downloads/windows){:target="_blank"} + +Last but not least, Slack is not an option for this IOC! + +We will be extremely reluctant to communicate by email with you about this IOC. + +Indeed, emails capture information very poorly, because very often the subject headings +are poorly chosen (or not chosen at all...), conversations by email deal with heterogeneous +subjects, the recipients of a series of messages vary over time, and other joyful things - +the imagination of Internet users is limitless (and exhausting)... + +Instead, use your [IOC Slack](https://ioc-r.slack.com). + +### GitHub +Git is a powerful versionning system. +The software was implemented in web environments to create even more powerful system of +continuous development and continuous integration. + +This is the case of [GitHub](https://github.com){:target="_blank"} which we have chosen in ARTbio. +[GitLab](https://about.gitlab.com/) is another option, which will not use here. + + +!!! info "Disclosure" + The learning curve of git and github is not steep for a biologist... which + unfortunately means that you will have to make a substantial effort before + understanding the benefit of GitHub and being able to manipulate it without + discomfort. But if you make the necessary effort, rest assured that you won't regret + it. + + For a very good introductory journal to Git and GitHub, although a bit old, see this + [article](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004668) + + GitHub is also very good at teaching how to use it... You can go from + [there](https://docs.github.com/en/get-started/quickstart)! + + +### R and RStudio + +We do not intend to redo yet another version of the introductory R tutorials: +there are many of them, they are often excellent and deserve to be used! + +Therefore, what is following is a rather classical plan to learn R and for each notion to +be learned, we made not only one but multiple links to content built by other authors. This +is the occasion here to thank them collectively for their efforts; detailed credits will +be given in addition all along the following pages. + +The most specific feature of the programming language R compared to other languages is its +extensive and specialized support for statistical analysis, data manipulation, and +visualization. + +#### An overview of R and RStudio + +Brief, but the essential is [here](https://www.youtube.com/watch?v=FIrsOBy5k58&t=3s) or +[here](https://youtu.be/yZ0bV2Afkjc). We will do the same, but at your own pace! + +* Transcript of the presentation + * Getting started with the RStudio interface + * Creating a project + * First contact with the console + * arithmetic operations + * variables and variable assignment + * The history panel + * The script panel + * script creation + * script execution + * Environment + * Packages + * Help and R cheatsheets + * Communities: [stack**overflow**](https://stackoverflow.com/) + * [https://github.com/rstudio/cheatsheets](https://github.com/rstudio/cheatsheets) + * [https://posit.co/resources/cheatsheets/](https://posit.co/resources/cheatsheets/) + + + +## First Steps with Seurat + + +[Introduction for the Seurat package](intro_seurat.md) + +### Import Data + +[Reference](import.md) for the data import. + +### Create Genome Annotation + + + + + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + +[Exercices](preprocessing_exo.md) + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- diff --git a/docs/scRNAseq_basics/01_IOCsc_week1.md b/docs/scRNAseq_basics/01_IOCsc_week1.md index e69de29b..6c319e2a 100644 --- a/docs/scRNAseq_basics/01_IOCsc_week1.md +++ b/docs/scRNAseq_basics/01_IOCsc_week1.md @@ -0,0 +1,19 @@ +## Data Preporcessing + +[Reference of preprocessing.](preprocessing.md) + +## Normalization + +## Identification of Highly Variable Genes (HGV) + + +--- + +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** + +[Exercices](preprocessing_exo.md) + + +**Thank you for your attention and see you next week :clap: :clap: :clap:** + +---- \ No newline at end of file From 8669ae3e14051f073c99a10e61c98407bf6aa5db Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 17:02:31 +0200 Subject: [PATCH 14/21] add pc_to_keep variable --- docs/scRNAseq_basics/clustering.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/scRNAseq_basics/clustering.md 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z9cUuAlNr2-olYup&7oj;IYx~~-kkB?Nls6AJ!du}3hj=Cj`5AG&D=0SC`T7TQ{#|A zu61w_I+}UW?DZFVh%{Vy+bD^4@7WXahzB5T5T1a6FKQT88^;~l)B0JJ9_$R6Tf!ZF zqR;tDt%PP8!P<|AJ-5%!J diff --git a/docs/scRNAseq_basics/intro_markers.md b/docs/scRNAseq_basics/intro_markers.md index b2df88aa..01bb0cf3 100644 --- a/docs/scRNAseq_basics/intro_markers.md +++ b/docs/scRNAseq_basics/intro_markers.md @@ -79,12 +79,12 @@ kable(head(pbmc_markers)) | | p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene | |:----------------|------:|-----------:|------:|------:|----------:|:--------|:----------------| -| ENSG00000137154 | 0 | 0.6674692 | 0.998 | 0.997 | 0 | 0 | ENSG00000137154 | -| ENSG00000144713 | 0 | 0.6134636 | 0.998 | 0.997 | 0 | 0 | ENSG00000144713 | -| ENSG00000112306 | 0 | 0.7002797 | 1.000 | 0.994 | 0 | 0 | ENSG00000112306 | -| ENSG00000177954 | 0 | 0.7041791 | 0.998 | 0.994 | 0 | 0 | ENSG00000177954 | -| ENSG00000164587 | 0 | 0.5988615 | 1.000 | 0.997 | 0 | 0 | ENSG00000164587 | -| ENSG00000118181 | 0 | 0.7149172 | 1.000 | 0.978 | 0 | 0 | ENSG00000118181 | +| ENSG00000137154 | 0 | 0.6952948 | 0.998 | 0.997 | 0 | 0 | ENSG00000137154 | +| ENSG00000144713 | 0 | 0.6444963 | 0.998 | 0.997 | 0 | 0 | ENSG00000144713 | +| ENSG00000112306 | 0 | 0.7383383 | 1.000 | 0.994 | 0 | 0 | ENSG00000112306 | +| ENSG00000177954 | 0 | 0.7437027 | 0.998 | 0.994 | 0 | 0 | ENSG00000177954 | +| ENSG00000164587 | 0 | 0.6352699 | 1.000 | 0.997 | 0 | 0 | ENSG00000164587 | +| ENSG00000118181 | 0 | 0.7973422 | 1.000 | 0.978 | 0 | 0 | ENSG00000118181 | The result of this function is a dataframe with several columns: From 5ac665c46adc97a44c73fdb4527d96da110dd591 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 17:35:41 +0200 Subject: [PATCH 17/21] add materials and exo for week 0 --- docs/scRNAseq_basics/00_IOCsc_week0.md | 206 +++++-------------------- 1 file changed, 38 insertions(+), 168 deletions(-) diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md index 655dc6e5..aa606cc5 100644 --- a/docs/scRNAseq_basics/00_IOCsc_week0.md +++ b/docs/scRNAseq_basics/00_IOCsc_week0.md @@ -1,189 +1,59 @@ -**3-hours Zoom video-conference** - -## Introduction of the Companions and Instructors (10 min) -## Presentation of the IOC general workflow (Scheme) (15 min) -![](../R-IOC/images/IOC-workflow.png) -## Presentation of the IOC tools (2 hours) -### Zoom -We currently use the Zoom software for our video-conferences. -They will be recorded and available off line in the psilo data server (see below) - -Please, follow these guides lines for Zoom usage - -1. Use a local, desktop Zoom application instead of the online web application. You can - download Zoom [here](https://zoom.us/fr/download){:target="_blank"} -- Test your Zoom application once if you never used it. We will be happy to arrange a quick - Zoom session a few days before the IOC if you feel that there may be an issue. -- Be sure that your internet connection is reasonably fast to allow __the use of your camera__. - We much value visual interactions! -- Arrange a quiet local place for your Zoom weekly session. People talking around you are - disturbing you as well as the other conference participants. If you cannot arrange to be - alone in your office, please warn you colleagues well ahead the session that you will - need peace. -- **Use a headset with a built-in microphone**. It's not a gimmick! There are now cheap - headsets for video-conference that works well. Test your headset with your computer - and Zoom well ahead the first IOC session. -- Prefer a Desktop (generally more powerful) to a Laptop computer. -- Use the largest screen you have (another reason not using a laptop). If you have two - screens, even better, but then test zoom with your dual screen setup. We may have to - leave open several windows and applications during the Zoom session. -- Be on time at the session! -- You are welcome to use the chat panel of Zoom to exchange links, code issues, *etc.*, but - Slack (see below) is likely better suited to this, especially because the Zoom chat is - lost when the application is shutdown. Therefore, be sure to have you Slack board available - during the Zoom sessions. - -There are many other interesting functionalities with Zoom, which will be covered in the -presentation. - -### Trello - -One of our favorite tools is Trello. -You will be invited to access to the [trello board](https://trello.com/b/GFH3LBOR/artbio063iocsinglecellrnaseq){:target="_blank"} -of the IOC. Not a lot to say about Trello. It is just a great tool to capture information, -collaborate, and organize projects. - -We hope that you will still use Trello for your own projects and purposes when the IOC is -finished! - -### STARTbio -Our [STARTbio web site](https://artbio.github.io/startbio/) is the hub where we connect -all the training materials for IOCs. -To access rapidly to your IOC-R, -use this [URL shortcut](https://artbio.github.io/startbio/R-IOC/00_IOC_R_program/). - -Here, you'll find all weekly lessons, exercises, instructions, *etc*. - -**Importantly**, you, yes, you, are welcome to propose modifications or fixes to the STARTbio -IOC web pages! -Assuming that during this IOC you will become familiar with the use of GitHub, all you -have to do is click on the pencil icon ![](images/github_pencil.png){width="25"} -at the top of each page and propose your modifications in a branch of our GitHub startbio -repository. - -### Slack -Slack is a workspace to exchange messages or files, follow conversations, communicate -about issues and ideas. - -If you haven't already done so, you will first need to open a -[Slack account](https://slack.com/get-started#/createnew){:target="_blank"} by providing -a username and password (you can also use authentication through Google or Apple). - -If you have already a Slack account, you can connect to this account using this -[URL](https://slack.com/signin#/signin){:target="_blank"}. - -!!! warning "Attention!" - If you have multiple login emails for your Slack account, it can become confusing if - some of your workspaces are identified with one email and others with another email. - - This might happen, for example, if you were invited to a Slack workspace with a - different email than the one you initially used to create your first Slack workspace. - -!!! tip "Get Slack app on your local computer" - We really strongly recommend that you use a desktop version of the slack application - on your computer(s). - - Once installed, this desktop Slack application will connect to - your Slack account(s) and import locally your workspace, including the workspace - dedicated to this IOC - - [Apple Desktop Slack](https://apps.apple.com/us/app/slack-for-desktop/id803453959?mt=12){:target="_blank"} - | [Windows Desktop Slack](https://slack.com/intl/fr-fr/downloads/windows){:target="_blank"} +## First Steps with Seurat -Last but not least, Slack is not an option for this IOC! +Please go read the following pages : -We will be extremely reluctant to communicate by email with you about this IOC. +- [Introduction to Single-Cell RNAseq analysis](introduction.md) +- [Initialization of R analysis](intro_seurat.md) +- [Import data and intialization of Seurat object](import.md) -Indeed, emails capture information very poorly, because very often the subject headings -are poorly chosen (or not chosen at all...), conversations by email deal with heterogeneous -subjects, the recipients of a series of messages vary over time, and other joyful things - -the imagination of Internet users is limitless (and exhausting)... +--- -Instead, use your [IOC Slack](https://ioc-r.slack.com). +![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** -### GitHub -Git is a powerful versionning system. -The software was implemented in web environments to create even more powerful system of -continuous development and continuous integration. +Now it's your time to shine ! We are going to put into pratice what we +have just seen. By using a more complicated use case, you are going to +reproduce the whole scRNAseq analysis with Seurat. -This is the case of [GitHub](https://github.com){:target="_blank"} which we have chosen in ARTbio. -[GitLab](https://about.gitlab.com/) is another option, which will not use here. +# Dataset test +The dataset for this analysis will be single cell RNAseq from chick embryos +from [de Lima *et al*](https://doi.org/10.1038/s41467-021-24157-x). We are +going to analyse only single cells of E10 stage of embryos. You can download +the dataset at the GEO accession [GSE166981](https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/geo/query/acc.cgi?acc=GSE166981). -!!! info "Disclosure" - The learning curve of git and github is not steep for a biologist... which - unfortunately means that you will have to make a substantial effort before - understanding the benefit of GitHub and being able to manipulate it without - discomfort. But if you make the necessary effort, rest assured that you won't regret - it. +??? question "Do you need help to find the data ?" + ??? tip "First tip : " + Look to supplementary file.... + ??? tip "Second tip : " + To see what's inside the tar archive you can click on `(custom)` + ??? tip "Still no clue ? :" + You need the three files with *"E10"* in the filename. You need to check the three files and then click on download. You will have the archive `GSE166981_RAW.tar` in your downloads. - For a very good introductory journal to Git and GitHub, although a bit old, see this - [article](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004668) - - GitHub is also very good at teaching how to use it... You can go from - [there](https://docs.github.com/en/get-started/quickstart)! - - -### R and RStudio - -We do not intend to redo yet another version of the introductory R tutorials: -there are many of them, they are often excellent and deserve to be used! +Once you download the data, all you have to do is import it onto the Rstudio server. -Therefore, what is following is a rather classical plan to learn R and for each notion to -be learned, we made not only one but multiple links to content built by other authors. This -is the occasion here to thank them collectively for their efforts; detailed credits will -be given in addition all along the following pages. +## Biomart is your friend -The most specific feature of the programming language R compared to other languages is its -extensive and specialized support for statistical analysis, data manipulation, and -visualization. +Don't forget to import biomaRt in order to help you annotate your genes. +Make sure to tweak parameters to fit this new dataset. -#### An overview of R and RStudio +!!! question "Trouble to use biomaRt ?" + Here is some links to help you with biomaRt : -Brief, but the essential is [here](https://www.youtube.com/watch?v=FIrsOBy5k58&t=3s) or -[here](https://youtu.be/yZ0bV2Afkjc). We will do the same, but at your own pace! + - [Vignette of the R package](https://bioconductor.org/packages/release/bioc/vignettes/biomaRt/inst/doc/accessing_ensembl.html) + - [Short presentation](https://docs.google.com/presentation/d/1ck41d_0a6bMEreTfeeES67RExEc2pp_OXQvqbJ3ZdhU/edit?usp=sharing) of the use of the R package, comparing it with the Ensembl interface -* Transcript of the presentation - * Getting started with the RStudio interface - * Creating a project - * First contact with the console - * arithmetic operations - * variables and variable assignment - * The history panel - * The script panel - * script creation - * script execution - * Environment - * Packages - * Help and R cheatsheets - * Communities: [stack**overflow**](https://stackoverflow.com/) - * [https://github.com/rstudio/cheatsheets](https://github.com/rstudio/cheatsheets) - * [https://posit.co/resources/cheatsheets/](https://posit.co/resources/cheatsheets/) +# Render your script +To complete this week you'll need to : -## First Steps with Seurat - - -[Introduction for the Seurat package](intro_seurat.md) - -### Import Data - -[Reference](import.md) for the data import. - -### Create Genome Annotation - - - - - - ---- - -![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** - -[Exercices](preprocessing_exo.md) +- [x] 1. Retrieve the chick dataset +- [x] 2. Import data in Rstudio +- [x] 3. Import data in your global environment +- [x] 4. Create a Seurat Object +- [x] 5. Create an annotation table of chick genes using `biomaRt`. +Add your script in your trello card. **Thank you for your attention and see you next week :clap: :clap: :clap:** From f454c2b810c74e60a4bede02abcee317f18bf758 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 17:41:22 +0200 Subject: [PATCH 18/21] change test dataset for exercices --- docs/scRNAseq_basics/00_IOCsc_week0.md | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md index aa606cc5..10072bf2 100644 --- a/docs/scRNAseq_basics/00_IOCsc_week0.md +++ b/docs/scRNAseq_basics/00_IOCsc_week0.md @@ -16,18 +16,15 @@ reproduce the whole scRNAseq analysis with Seurat. # Dataset test -The dataset for this analysis will be single cell RNAseq from chick embryos -from [de Lima *et al*](https://doi.org/10.1038/s41467-021-24157-x). We are -going to analyse only single cells of E10 stage of embryos. You can download -the dataset at the GEO accession [GSE166981](https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/geo/query/acc.cgi?acc=GSE166981). +The dataset for this analysis will be single cell RNAseq from zebrafish embryos +from [Metikala *et al*](https://doi.org/10.1371/journal.pone.0254024). You can download +the dataset at the GEO accession [GSE152982](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152982). ??? question "Do you need help to find the data ?" ??? tip "First tip : " Look to supplementary file.... ??? tip "Second tip : " To see what's inside the tar archive you can click on `(custom)` - ??? tip "Still no clue ? :" - You need the three files with *"E10"* in the filename. You need to check the three files and then click on download. You will have the archive `GSE166981_RAW.tar` in your downloads. Once you download the data, all you have to do is import it onto the Rstudio server. From 78d1e3a5ae71b6bc82f3fe41863f89b6e6035cb1 Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 17:52:59 +0200 Subject: [PATCH 19/21] add program and exo for week1 --- docs/scRNAseq_basics/01_IOCsc_week1.md | 37 ++++++++++++++++++++++---- 1 file changed, 32 insertions(+), 5 deletions(-) diff --git a/docs/scRNAseq_basics/01_IOCsc_week1.md b/docs/scRNAseq_basics/01_IOCsc_week1.md index 6c319e2a..6d772bb1 100644 --- a/docs/scRNAseq_basics/01_IOCsc_week1.md +++ b/docs/scRNAseq_basics/01_IOCsc_week1.md @@ -1,18 +1,45 @@ -## Data Preporcessing +## Data Preprocessing -[Reference of preprocessing.](preprocessing.md) +The preprocessing is the most important part of a single cell analysis because you +can skew your result if you filter too much **or too little** and you must really +understand what's going on these steps. -## Normalization +Please go read the following pages to learn more about it : [Preprocessing.](preprocessing.md). -## Identification of Highly Variable Genes (HGV) +The preprocesing is composed of : +- Filtering of low quality barcodes +- Barcode Normalization +- Selection of most variable features --- ![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** -[Exercices](preprocessing_exo.md) +Now it's your time to shine ! We are going to put into pratice what we +have just seen. By using a more complicated use case, you are going to +reproduce the whole scRNAseq analysis with Seurat. +# Dataset test + +The dataset for this analysis will be single cell RNAseq from zebrafish embryos +from [Metikala *et al*](https://doi.org/10.1371/journal.pone.0254024). You need +to continue your RMD/QMD from last week. + +# Render your RMD/QMD + +To complete this week you'll need to : + +- [x] 1. Filtering the low quality barcodes **and explain each cutoff** +- [x] 2. Normalize the data +- [x] 3. Identify the most variable genes + +!!! warning "IMPORTANT" + Please note you **must** be explicative in your cutoff choices and detailled + each step of your thoughts. + In general, try to explain in your own words, each step of your analysis ! + +Add your RMD/QMD in your trello card. **Thank you for your attention and see you next week :clap: :clap: :clap:** From 4d4e63358589eb6c19a176779e96d62801ee7a4a Mon Sep 17 00:00:00 2001 From: bellenger-l Date: Tue, 23 Apr 2024 17:53:28 +0200 Subject: [PATCH 20/21] small fix week0 --- docs/scRNAseq_basics/00_IOCsc_week0.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md index 10072bf2..f0d664c8 100644 --- a/docs/scRNAseq_basics/00_IOCsc_week0.md +++ b/docs/scRNAseq_basics/00_IOCsc_week0.md @@ -40,17 +40,17 @@ Make sure to tweak parameters to fit this new dataset. - [Short presentation](https://docs.google.com/presentation/d/1ck41d_0a6bMEreTfeeES67RExEc2pp_OXQvqbJ3ZdhU/edit?usp=sharing) of the use of the R package, comparing it with the Ensembl interface -# Render your script +# Render your RMD/QMD To complete this week you'll need to : -- [x] 1. Retrieve the chick dataset +- [x] 1. Retrieve the zebrafish dataset - [x] 2. Import data in Rstudio - [x] 3. Import data in your global environment - [x] 4. Create a Seurat Object -- [x] 5. Create an annotation table of chick genes using `biomaRt`. +- [x] 5. Create an annotation table of zebrafish genes using `biomaRt`. -Add your script in your trello card. +Add your RMD/QMD in your trello card. **Thank you for your attention and see you next week :clap: :clap: :clap:** From 023f75f7da5c504da14b65a73c7b53aa5627fc86 Mon Sep 17 00:00:00 2001 From: Lijiao Ning Date: Tue, 23 Apr 2024 21:12:09 +0200 Subject: [PATCH 21/21] rephrase and reorder some codes --- docs/scRNAseq_basics/00_IOCsc_week0.md | 4 +-- docs/scRNAseq_basics/01_IOCsc_week1.md | 10 ++++---- docs/scRNAseq_basics/preprocessing.md | 34 +++++++++++++------------- 3 files changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md index f0d664c8..a966ce60 100644 --- a/docs/scRNAseq_basics/00_IOCsc_week0.md +++ b/docs/scRNAseq_basics/00_IOCsc_week0.md @@ -50,8 +50,8 @@ To complete this week you'll need to : - [x] 4. Create a Seurat Object - [x] 5. Create an annotation table of zebrafish genes using `biomaRt`. -Add your RMD/QMD in your trello card. +Add your RMD/QMD in your Trello card. **Thank you for your attention and see you next week :clap: :clap: :clap:** ----- +---- \ No newline at end of file diff --git a/docs/scRNAseq_basics/01_IOCsc_week1.md b/docs/scRNAseq_basics/01_IOCsc_week1.md index 6d772bb1..6886534f 100644 --- a/docs/scRNAseq_basics/01_IOCsc_week1.md +++ b/docs/scRNAseq_basics/01_IOCsc_week1.md @@ -4,14 +4,14 @@ The preprocessing is the most important part of a single cell analysis because y can skew your result if you filter too much **or too little** and you must really understand what's going on these steps. -Please go read the following pages to learn more about it : [Preprocessing.](preprocessing.md). - -The preprocesing is composed of : +The preprocesing is composed of: - Filtering of low quality barcodes -- Barcode Normalization +- Barcode normalization - Selection of most variable features +Please go read the [preprocessing](preprocessing.md) pages to learn more about it. + --- ![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!** @@ -39,7 +39,7 @@ To complete this week you'll need to : each step of your thoughts. In general, try to explain in your own words, each step of your analysis ! -Add your RMD/QMD in your trello card. +Add your RMD/QMD in your Trello card. **Thank you for your attention and see you next week :clap: :clap: :clap:** diff --git a/docs/scRNAseq_basics/preprocessing.md b/docs/scRNAseq_basics/preprocessing.md index dc0c37ce..46f5c82f 100644 --- a/docs/scRNAseq_basics/preprocessing.md +++ b/docs/scRNAseq_basics/preprocessing.md @@ -2,7 +2,7 @@ The pre-processing steps are used to clean the data in order not to distort the results of downstream analyses (clustering analysis, markers, differential -expression analysis). +expression analysis, *etc.*). ## Filter out low quality cells @@ -36,7 +36,7 @@ A cell is generally considered to be in apoptosis when the transcriptome detects more than 20% of the genes in the MT genome. Some are more stringent in lowering this threshold to 10%. -``` r +```r ## Retrieve genes from the MT genome using biomart genes_MT <- annotated_hg19$ensembl_gene_id[annotated_hg19$chromosome_name == "MT"] @@ -55,7 +55,7 @@ VlnPlot(object = pbmc_small, -``` r +```r ## Graphical representation of QC ggplot(pbmc_small@meta.data, aes(y = nCount_RNA, @@ -69,8 +69,9 @@ ggplot(pbmc_small@meta.data, high = "red", mid = "yellow", midpoint = 20) + - ggtitle("QC plot", "Number of detected genes in function of number of UMI")+ - labs(y = "Number of UMI per cell", x = "Number of detected genes by cell") + labs(x = "Number of detected genes by cell", + y = "Number of UMI per cell", + title = "QC plot", "Number of detected genes in function of number of UMI") ``` @@ -87,7 +88,7 @@ We can also use a histogram representation. I recommend the three types of figures because depending on the dataset, the best method to identify outliers is different. -``` r +```r hist(pbmc_small$nCount_RNA, breaks = 100, xlab = "Number of UMI per cell", @@ -98,7 +99,7 @@ abline(v = 10000, col = "red") -``` r +```r hist(pbmc_small$nFeature_RNA, breaks = 100, xlab = "Number of detected genes by cell", @@ -128,14 +129,12 @@ We will remove all the cells : - that detect less than 300 genes or more than 2300. - whose percentage of expressed genes of the MT genome exceeds 10% -``` r +```r ## Filtering SeuratObject pbmc_small <- subset(pbmc_small, percent_mito < 10 & - nCount_RNA > 650 & - nCount_RNA < 10000 & - nFeature_RNA > 300 & - nFeature_RNA < 2300) + (nCount_RNA > 650 & nCount_RNA < 10000) & + (nFeature_RNA > 300 & nFeature_RNA < 2300)) ## Plot ggplot(pbmc_small@meta.data, @@ -148,13 +147,14 @@ ggplot(pbmc_small@meta.data, high = "red", mid = "yellow", midpoint = 20) + - ggtitle("QC plot after filtering", "Number of detected genes in function of number of UMI")+ - labs(y = "Number of UMI per cell", x = "Number of detected genes by cell") + labs(x = "Number of detected genes by cell", + y = "Number of UMI per cell", + title = "QC plot after filtering", "Number of detected genes in function of number of UMI") ``` -``` r +```r ## Update object in R console pbmc_small ``` @@ -197,7 +197,7 @@ median of the library size (= total number of UMIs per cell, = `nCount_RNA` in `meta.data`). If the scale factor is equal to 1e6 then we would get log2(CPM+1). *CPM : Count Per Million*. -``` r +```r ## Inter-cell normalization pbmc_small <- NormalizeData(pbmc_small, #SeuratObject assay = "RNA", #Assay to use @@ -230,7 +230,7 @@ relation between the expression mean and the variance of each gene. With the `nfeatures` parameter we retrieve the 2000 most variable genes according to the vst method. -``` r +```r pbmc_small <- FindVariableFeatures(pbmc_small, #SeuratObject selection.method = "vst", #Method nfeatures = 2000) #Top HVG (Highly Variable Gene), default value