Verified Commit 0f213906 authored by Laurent Modolo's avatar Laurent Modolo
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update img path

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......@@ -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}
......
......@@ -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*
......
......@@ -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}
......
......@@ -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}
......
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