add img to normalization.Rmd

2_normalization/normalization.Rmd

 ---
-title: "single-cell RNA-Seq data: Normalization"
+title: "single-cell RNA-Seq: Normalization"
 author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr)"
 date: "Friday 3 June 2022"
 output:
@@ -8,7 +8,7 @@ output:
     fig_caption: no
     highlight: tango
     latex_engine: xelatex
-    slide_level: 1
+    slide_level: 2
     theme: metropolis
   ioslides_presentation:
     highlight: tango
@@ -16,32 +16,263 @@ output:
     highlight: tango
 classoption: aspectratio=169  
 ---
-
 # Introduction
 
-## Programme
+## Introduction
+
+### Program
 
 1. Single-cell RNASeq data from 10X Sequencing (Friday 3 June 2022 - 14:00)
 2. Normalization and spurious effects (Wednesday 8 June 2022 - 14:00)
 3. Dimension reduction and data visualization (Monday 13 June 2022 - 15:00)
 4. Clustering and annotation (Thursday 23 June 2022 - 14:00)
 5. Pseudo-time and velocity inference (Thursday 30 June 2022 - 14:00)
-6. Differental expression analysis (Friday 8 July 2022 - 14:00)
+6. Differential expression analysis (Friday 8 July 2022 - 14:00)
 
-# Introduction
+## Introduction
 
-## Programme
+### Program
 
 1. Single-cell RNASeq data from 10X Sequencing (Friday 3 June 2022 - 14:00)
 2. Normalization and spurious effects (Wednesday 8 June 2022 - 14:00)
- - Normalization vers transformation
- - Spurious effects
-  - library effect
-  - dropout effect
-  - batch effect
- - Variance decomposition
- - Residuals
+  - Quality control
+  - Normalization
+    - Variance stabilization
+    - Depth normalization
+    - The monotonicity of the normalization
+  - batch effects
+  - Heterogeneous data
 3. Dimension reduction and data visualization (Monday 13 June 2022 - 15:00)
 4. Clustering and annotation (Thursday 23 June 2022 - 14:00)
 5. Pseudo-time and velocity inference (Thursday 30 June 2022 - 14:00)
 6. Differential expression analysis (Friday 8 July 2022 - 14:00)
+
+
+# Quality control
+
+## Cell filtering
+
+\begin{center}
+\begin{columns}
+\column{0.5\textwidth}
+\begin{center}
+\begin{tikzpicture}
+  \fill
+      (0.5,3.5) node {\bf $\text{gene}_1$}
+   -- (0.5,2.5) node {\bf $\text{gene}_2$}
+   -- (0.5,1.5) node {\bf $\vdots$}
+   -- (0.5,0.5) node {\bf $\text{gene}_n$};
+  \fill
+      (1.5,4.5) node {\bf{$\text{cell}_1$}}
+   -- (1.5,3.5) node {mRNA}
+   -- (1.5,2.5) node {mRNA}
+   -- (1.5,1.5) node {$\vdots$}
+   -- (1.5,0.5) node {mRNA};
+  \fill
+      (2.5,4.5) node {\color{red}\bf{$\text{0 cell}_2$}}
+   -- (2.5,3.5) node {\color{red}mRNA}
+   -- (2.5,2.5) node {\color{red}mRNA}
+   -- (2.5,1.5) node {\color{red}$\vdots$}
+   -- (2.5,0.5) node {\color{red}mRNA};
+  \fill
+      (3.5,4.5) node {\bf{$\cdots$}}
+   -- (3.5,3.5) node {$\cdots$}
+   -- (3.5,2.5) node {$\cdots$}
+   -- (3.5,1.5) node {$\ddots$}
+   -- (3.5,0.5) node {$\cdots$};
+  \fill
+      (4.5,4.5) node {\bf{$\text{cell}_c$}}
+   -- (4.5,3.5) node {mRNA}
+   -- (4.5,2.5) node {mRNA}
+   -- (4.5,1.5) node {$\vdots$}
+   -- (4.5,0.5) node {mRNA};
+  \draw (1,0) grid (5,4);
+\end{tikzpicture}
+\end{center}
+
+\column{0.5\textwidth}
+
+{\large Some cells are not cells.}
+
+\begin{itemize}
+  \item matrix columns are defined by {\bf cell barcode sequences}
+  \item {\bf cell barcode sequences identify droplet} in the 10X protocol
+\end{itemize}
+
+\end{columns}
+\end{center}
+
+## Cell filtering
+
+\begin{center}
+\begin{columns}
+\column{0.5\textwidth}
+\begin{center}
+\begin{tikzpicture}
+  \fill
+      (0.5,3.5) node {\bf $\text{gene}_1$}
+   -- (0.5,2.5) node {\bf $\text{gene}_2$}
+   -- (0.5,1.5) node {\bf $\vdots$}
+   -- (0.5,0.5) node {\bf $\text{gene}_n$};
+  \fill
+      (1.5,4.5) node {\bf{$\text{bc}_1$}}
+   -- (1.5,3.5) node {mRNA}
+   -- (1.5,2.5) node {mRNA}
+   -- (1.5,1.5) node {$\vdots$}
+   -- (1.5,0.5) node {mRNA};
+  \fill
+      (2.5,4.5) node {\color{red}\bf{$\text{bc}_2$}}
+   -- (2.5,3.5) node {\color{red}mRNA}
+   -- (2.5,2.5) node {\color{red}mRNA}
+   -- (2.5,1.5) node {\color{red}$\vdots$}
+   -- (2.5,0.5) node {\color{red}mRNA};
+  \fill
+      (3.5,4.5) node {\bf{$\cdots$}}
+   -- (3.5,3.5) node {$\cdots$}
+   -- (3.5,2.5) node {$\cdots$}
+   -- (3.5,1.5) node {$\ddots$}
+   -- (3.5,0.5) node {$\cdots$};
+  \fill
+      (4.5,4.5) node {\bf{$\text{bc}_c$}}
+   -- (4.5,3.5) node {mRNA}
+   -- (4.5,2.5) node {mRNA}
+   -- (4.5,1.5) node {$\vdots$}
+   -- (4.5,0.5) node {mRNA};
+  \draw (1,0) grid (5,4);
+\end{tikzpicture}
+\end{center}
+
+\column{0.5\textwidth}
+
+{\large Some cells are not cells.}
+
+\begin{itemize}
+  \item {\bf v2} chemistry $\sim 737,000$ cell barcodes
+  \item {\bf v3} chemistry $\sim 3,500,000$ cell barcodes
+\end{itemize}
+
+\vspace{1em}
+
+To avoid cell barcode collision we need
+\[
+\text{\bf cell number} \ll \text{\bf cell barcode number}
+\]
+
+Most of the droplets will be empty
+
+\end{columns}
+\end{center}
+
+## Cell filtering
+
+\begin{center}
+\begin{columns}
+\column{0.35\textwidth}
+  Sequenced empty droplets:
+  \begin{itemize}
+    \item do not express many genes
+    \item looks like experimental noise
+  \end{itemize}
+
+  \vspace{1em}
+
+  The number of UMI per cell barcode
+
+\column{0.7\textwidth}
+  \vspace{1.5em}
+  \includegraphics[width=\textwidth]{img/cell_barcode_rank_vs_umi.png}
+\end{columns}
+\end{center}
+
+## Cell filtering
+
+\begin{center}
+\begin{columns}
+\column{0.35\textwidth}
+  We have {\bf two populations} of cell barcode:
+  \begin{itemize}
+    \item a {\bf low} total UMI counts one
+    \item a {\bf high} total UMI counts one
+  \end{itemize}
+
+\column{0.7\textwidth}
+  \vspace{1.5em}
+  \includegraphics[width=\textwidth]{img/cell_barcode_rank_vs_umi.png}
+\end{columns}
+\end{center}
+
+## Cell filtering
+
+\begin{center}
+\begin{columns}
+\column{0.5\textwidth}
+\begin{center}
+\begin{tikzpicture}
+  \fill
+      (0.5,3.5) node {\bf $\text{gene}_1$}
+   -- (0.5,2.5) node {\bf $\text{gene}_2$}
+   -- (0.5,1.5) node {\bf $\vdots$}
+   -- (0.5,0.5) node {\bf $\text{gene}_n$};
+  \fill
+      (1.5,4.5) node {\bf{$\text{cell}_1$}}
+   -- (1.5,3.5) node {mRNA}
+   -- (1.5,2.5) node {mRNA}
+   -- (1.5,1.5) node {$\vdots$}
+   -- (1.5,0.5) node {mRNA};
+  \fill
+      (2.5,4.5) node {\color{red}\bf{$\text{2 cells}_2$}}
+   -- (2.5,3.5) node {\color{red}mRNA}
+   -- (2.5,2.5) node {\color{red}mRNA}
+   -- (2.5,1.5) node {\color{red}$\vdots$}
+   -- (2.5,0.5) node {\color{red}mRNA};
+  \fill
+      (3.5,4.5) node {\bf{$\cdots$}}
+   -- (3.5,3.5) node {$\cdots$}
+   -- (3.5,2.5) node {$\cdots$}
+   -- (3.5,1.5) node {$\ddots$}
+   -- (3.5,0.5) node {$\cdots$};
+  \fill
+      (4.5,4.5) node {\bf{$\text{cell}_c$}}
+   -- (4.5,3.5) node {mRNA}
+   -- (4.5,2.5) node {mRNA}
+   -- (4.5,1.5) node {$\vdots$}
+   -- (4.5,0.5) node {mRNA};
+  \draw (1,0) grid (5,4);
+\end{tikzpicture}
+\end{center}
+
+\column{0.5\textwidth}
+
+{\large Some cells are many cells.}
+
+\begin{itemize}
+  \item not all tissues are easily dissociable
+  \item two cells glued together will share the same droplet
+\end{itemize}
+
+\vspace{1em}
+
+cell barcode corresponding to $n$-plet should be in monority the the preparation went well.
+
+\end{columns}
+\end{center}
+
+## Cell filtering
+
+apoptotic cells express MT genes
+
+# Normalization
+
+# Variance stabilization
+
+# Depth normalization
+# Monotonicity of the normalization
+# batch effects
+# heterogeneous data
+
+# Goals of Normalization
+
+- Variance stabilization
+- Depth normalization
+- Monotonicity of the normalization
+- Make different data set similar
\ No newline at end of file