update img path

1_scrnaseq_data/scrnaseq_data.Rmd

@@ -181,7 +181,7 @@ We want to estimate the number of each mRNA molecule in each cell.
 ## Unique Molecular Identifier (UMI)
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/umi_and_pcr.png}
+\includegraphics[width=\textwidth]{img/umi_and_pcr.png}
 \end{center}
 
 [*](https://www.genomescan.nl/wp-content/uploads/2019/10/graphic_UMIs_1.png.webp)
@@ -190,7 +190,7 @@ We want to estimate the number of each mRNA molecule in each cell.
 ## Unique Molecular Identifier (UMI)
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/umi_vs_read.png}
+\includegraphics[width=\textwidth]{img/umi_vs_read.png}
 \end{center}
 
 ## single-cell RNA Sequencing
@@ -444,7 +444,7 @@ Phred & Probability of incorrect base call & Base call accuracy \\
 ## Fastq preparation
 
 \begin{center}
-\includegraphics[width=0.8\textwidth]{./img/barcode_umi_sequence_split_fastq.png}
+\includegraphics[width=0.8\textwidth]{img/barcode_umi_sequence_split_fastq.png}
 \end{center}
 
 ## Cell barcode correction
@@ -457,7 +457,7 @@ Phred & Probability of incorrect base call & Base call accuracy \\
 \end{block}
 \column{0.5\textwidth}
 \vspace{2em}
-\includegraphics[width=0.7\textwidth]{./img/hash_function.png}
+\includegraphics[width=0.7\textwidth]{img/hash_function.png}
 \end{columns}
 \end{center}
 
@@ -466,7 +466,7 @@ Phred & Probability of incorrect base call & Base call accuracy \\
 \begin{center}
 \begin{columns}
 \column{0.5\textwidth}
-\includegraphics[width=0.9\textwidth]{./img/whitelist_hamming_dist.png}
+\includegraphics[width=0.9\textwidth]{img/whitelist_hamming_dist.png}
 \column{0.5\textwidth}
 
 The list of cell barcode is known:
@@ -483,7 +483,7 @@ We can take into account the sequence quality
 
 ## mRNA identification
 
-We need to **align** the cDNA sequence part of the read to a reference genome. \includegraphics[width=0.2\textwidth]{./img/mapping_read.png}
+We need to **align** the cDNA sequence part of the read to a reference genome. \includegraphics[width=0.2\textwidth]{img/mapping_read.png}
 
 \begin{block}{Smith–Waterman algorithm}
 Local sequence alignment, which aims at determining similar regions between two sequences.
@@ -514,7 +514,7 @@ $s(a_i,b_j) = \begin{cases}+3, \quad a_i=b_j \\ -3, \quad a_i\ne b_j\end{cases}$
 
 ## mRNA identification
 
-We need to **align** the cDNA sequence part of the read to a reference genome. \includegraphics[width=0.2\textwidth]{./img/mapping_read.png}
+We need to **align** the cDNA sequence part of the read to a reference genome. \includegraphics[width=0.2\textwidth]{img/mapping_read.png}
 
 \begin{block}{Smith–Waterman algorithm}
 Local sequence alignment, which aims at determining similar regions between two sequences.
@@ -579,7 +579,7 @@ H_{i-1,j-1} + s(a_i,b_j), \\
 \]
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/Smith-Waterman-Algorithm-Example-Step1.png}
+\includegraphics[width=\textwidth]{img/Smith-Waterman-Algorithm-Example-Step1.png}
 \end{center}
 
 ## mRNA identification
@@ -589,7 +589,7 @@ H_{i-1,j-1} + s(a_i,b_j), \\
 \vspace{-3.5em}
 
 \begin{center}
-\includegraphics[width=0.4\textwidth]{./img/Smith-Waterman-Algorithm-Example-Step2.png}
+\includegraphics[width=0.4\textwidth]{img/Smith-Waterman-Algorithm-Example-Step2.png}
 \end{center}
 
 We fill all the elements
@@ -609,7 +609,7 @@ score of 0
 \vspace{-12.5em}
 
 \begin{center}
-\includegraphics[width=0.4\textwidth]{./img/Smith-Waterman-Algorithm-Example-Step3.png}
+\includegraphics[width=0.4\textwidth]{img/Smith-Waterman-Algorithm-Example-Step3.png}
 \end{center}
 
 
@@ -776,7 +776,7 @@ Do we keep reads mapping to introns ?
 Do we keep reads mapping after the annotated 3' UTR ?
 
 \begin{center}
-\includegraphics[width=0.65\textwidth]{./img/undetected_read_intergenic_mapping.png}
+\includegraphics[width=0.65\textwidth]{img/undetected_read_intergenic_mapping.png}
 \end{center}
 
 ## mRNA identification
@@ -789,7 +789,7 @@ Do we keep reads mapping after the annotated 3' UTR ?
 \begin{columns}
 \column{0.65\textwidth}
 \begin{center}
-\includegraphics[width=\textwidth]{./img/discared_reads.png
+\includegraphics[width=\textwidth]{img/discared_reads.png
 }
 \end{center}
 \column{0.35\textwidth}

2_normalization/normalization.Rmd

@@ -395,7 +395,7 @@ With a transcription rate $\lambda_g(t)$ the observed mRNA count follow a Poisso
 $P(X = x)$ for $\mathcal{P}(\lambda_g)$
 
 \begin{center}
-\includegraphics[width=0.6\textwidth]{./img/poisson.png}
+\includegraphics[width=0.6\textwidth]{img/poisson.png}
 \end{center}
 
 The expectation of $X$, $E(X)$ is equal to it's variance $Var(X)$ (both are equal to $\lambda_g$)
@@ -467,7 +467,7 @@ In bulk RNASeq, we have $\sim 3$ observation per gene the task is more difficult
 $P(X = x)$ for $\mathcal{P}(\mu)$
 
 \begin{center}
-\includegraphics[width=0.6\textwidth]{./img/poisson.png}
+\includegraphics[width=0.6\textwidth]{img/poisson.png}
 \end{center}
 
 \begin{center}
@@ -513,7 +513,7 @@ with $Var(X) = \lambda + \alpha \lambda^2$
 
 \column{0.5\textwidth}
 \vspace{1em}
-\includegraphics[width=0.9\textwidth]{./img/mu_vs_var.png}
+\includegraphics[width=0.9\textwidth]{img/mu_vs_var.png}
 
 \end{columns}
 \end{center}
@@ -523,7 +523,7 @@ with $Var(X) = \lambda + \alpha \lambda^2$
 $P(X = x)$ for $\mathcal{P}(\lambda)$
 
 \begin{center}
-\includegraphics[width=0.8\textwidth]{./img/poisson.png}
+\includegraphics[width=0.8\textwidth]{img/poisson.png}
 \end{center}
 
 
@@ -532,7 +532,7 @@ $P(X = x)$ for $\mathcal{P}(\lambda)$
 $P(X = x)$ for $\mathcal{NB}(\lambda, \alpha = 10)$
 
 \begin{center}
-\includegraphics[width=0.8\textwidth]{./img/NB_sigma_10.png}
+\includegraphics[width=0.8\textwidth]{img/NB_sigma_10.png}
 \end{center}
 
 
@@ -541,7 +541,7 @@ $P(X = x)$ for $\mathcal{NB}(\lambda, \alpha = 10)$
 $P(X = x)$ for $\mathcal{NB}(\lambda, \alpha = 2)$
 
 \begin{center}
-\includegraphics[width=0.8\textwidth]{./img/NB_sigma_2.png}
+\includegraphics[width=0.8\textwidth]{img/NB_sigma_2.png}
 \end{center}
 
 ## Counts distributions
@@ -549,7 +549,7 @@ $P(X = x)$ for $\mathcal{NB}(\lambda, \alpha = 2)$
 $P(X = x)$ for $\mathcal{NB}(\lambda, \alpha = 1)$
 
 \begin{center}
-\includegraphics[width=0.8\textwidth]{./img/NB_sigma_1.png}
+\includegraphics[width=0.8\textwidth]{img/NB_sigma_1.png}
 \end{center}
 
 ## Variance of count data
@@ -867,7 +867,7 @@ Sanity will estimate $log\left(\alpha_{gi}\right) \sim \mathcal{N}\left(\mu, \si
 ## Goals of Normalization
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/normalization_marker_gene.png}
+\includegraphics[width=\textwidth]{img/normalization_marker_gene.png}
 \end{center}
 
 
@@ -876,7 +876,7 @@ Sanity will estimate $log\left(\alpha_{gi}\right) \sim \mathcal{N}\left(\mu, \si
 ## batch effects
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/batch_effect.png}
+\includegraphics[width=\textwidth]{img/batch_effect.png}
 \end{center}
 
 **batch** effects appear when you mix data from different:
@@ -906,7 +906,7 @@ We can run Sanity on each separate batch and combine the results.
 ## Harmony
 
 \begin{center}
-\includegraphics[width=\textwidth]{./img/harmony.png}
+\includegraphics[width=\textwidth]{img/harmony.png}
 \end{center}
 
 # single-cell RNA-Seq dimension reduction *Monday 13 June 2022*

3_dimension_reduction/dimension_reduction.Rmd

@@ -275,7 +275,7 @@ Dimension reduction is mandatory for any analysis (clustering, visualization, in
 \vspace{-2em}
 
 \begin{center}
-  \includegraphics[height=4cm]{./img/matrix_factorization.png}
+  \includegraphics[height=4cm]{img/matrix_factorization.png}
 \end{center}
 
 \begin{center}
@@ -495,7 +495,7 @@ x_{2,i} \\
 \vspace{1em}
 
 \begin{center}
-  \includegraphics[height=4cm]{./img/sparce_matrix_factorization.png}
+  \includegraphics[height=4cm]{img/sparce_matrix_factorization.png}
 \end{center}
 
 
@@ -504,7 +504,7 @@ x_{2,i} \\
 ### Non-negative matrix factorization
 \begin{center}
 \href{https://doi.org/10.1093/bioinformatics/btz177}{
-  \includegraphics[width=\textwidth]{./img/count_matrix_factorization.png}
+  \includegraphics[width=\textwidth]{img/count_matrix_factorization.png}
 }
 \end{center}
 \vspace{-2em}

5_pseudo_time/pseudo_time.Rmd

@@ -137,6 +137,7 @@ one RNA-Seq experiment constitutes a {\bf time series}, with each {\bf cell repr
 
 ## Monocle 1
 
+
 \begin{center}
   \href{http://www.nature.com/doifinder/10.1038/nbt.2859}{
     \includegraphics[width=\textwidth]{img/monocle_1.png}
@@ -146,6 +147,10 @@ one RNA-Seq experiment constitutes a {\bf time series}, with each {\bf cell repr
 
 ## Monocle 1
 
+### Find the longest path
+
+\vspace{-2em}
+
 \begin{center}
   \href{http://www.nature.com/doifinder/10.1038/nbt.2859}{
     \includegraphics[width=0.6\textwidth]{img/monocle_2.png}
@@ -210,10 +215,10 @@ traveling salesman problem (TSP)
 \end{center}
 \column{0.4\textwidth}
 \begin{itemize}
-  \item a. The linear regression line
-  \item b. The principal-component 
-  \item c. The smooth regression curve
-  \item d. The principal curve minimizes
+  \item linear regression line
+  \item principal-component 
+  \item smooth regression curve
+  \item principal curve minimizes
 \end{itemize}
 \end{columns}
 \end{center}