
COLE-TRAPNELL-LAB . GITHUB . IO {
}
Title:
Monocle
Description:
Monocle - A powerful software toolkit for single-cell analysis
Website Age:
12 years and 3 months (reg. 2013-03-08).
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Keywords {🔍}
cells, genes, monocle, cell, expression, data, gene, function, analysis, trajectory, singlecell, clustering, values, model, graph, counts, differential, ordering, single, rnaseq, pseudotime, branch, expressed, time, type, hsmm, set, celldataset, process, experiment, cluster, number, mrna, state, algorithm, object, progress, tpm, branches, experiments, functions, find, biological, transcript, plot, celltype, trajectories, embedding, based, reversed,
Topics {✒️}
$tpm_{ij} \propto \frac{y_{ij}}{\sum_{ begin{equation} \mathop{min}_{f_g begin{equation} \mathop{min}_{\mathbf{ mintree} \mathop{min}_{f_\mathcal{ ddrtree_opt} \mathop{min}_{\mathbf{ }}_b}\mathop{min}_{f_g analyzing single-cell rna-seq single-cell rna-seq experiments begin{equation} \label{eq begin{equation}\label{eq single-cell rna-seq analysis constructing single-cell trajectories performed paired-end rna-seq single-cell rna-seq process single-cell rna-seq experiment }} \mathop{min}_{\{\mathbf{ %\end{align} \end{equation} single cell rna-seq single-cell rna-seq }\quad\quad\quad\quad construct single-cell trajectories single-cell expression studies begin{equation} \hat{ highly protocol-dependent quantity single-cell mrna quantification reversed graph embedding accurate single-cell trajectories branch \end{equation} assumes } \int_{\epsilon}^{x_i^}x_{ independent y-axis ranges single-mrna genes divided high-mitogen growth medium variance-stabilization techniques introduced rna-seq experiments single cell studies low-mitogen differentiation medium single-cell mrna transcript branch-dependent gene expression mrna-count centered workflow rna-seq workflows sci-rna-seq rna-seq pipelines single-cell trajectory analysis early developmental stage single-cell trajectories identifying poor-quality libraries dimensional t-sne space linear function $f_\mathcal{ sample-sheet tells cuffnorm mapping function $f_\mathcal{
Questions {❓}
- A common question that arises when studying time-series gene expression studies is: "which genes follow similar kinetic trends"?
- But what about the "Unknown" cells?
- But what if you wish to focus on cycle-independent effects in your biological process?
- Choosing genes based on differential analysis of time points is often highly effective, but what if we don't have time series data?
- Perhaps the cells are clustering based on the media they're cultured in?
- What genes change as cells pass from the early developmental stage the top left of the tree through the branch?
- What is pseudotime?
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