The result from Monocle 2 here also shows two main branches. Also, as expected, the stem cells are at the very beginning of the trajectory. The pseudotime values are inverted. Monocle 2 can also be used to find genes differentially expressed along the pseudotime trajectory and clusters of such genes.
Here Monocle 2 will first project the data to 2 dimensions with DDRTree, and then do trajectory inference ( orderCells ). See what the trajectory looks like. This projection is DDRTree.
Monocle is an R package developed for analysing single cell gene expression data. Specifically, the package provides functionality for clustering and classifying single cells, conducting differential expression analyses, and constructing and investigating inferred developmental trajectories.
The result from Monocle 2 here also shows two main branches. Also, as expected, the stem cells are at the very beginning of the trajectory. The pseudotime values are inverted.
Instead of tracking changes in expression as a function of time, Monocle tracks changes as a function of progress along the trajectory, which we term pseudotime''. Pseudotime is an abstract unit of progress: it's simply the distance between a cell and the start of the trajectory, measured along the shortest path.
Monocle introduced the concept of pseudotime, which is a measure of how far a cell has moved through biological progress.
monocle (biotools:monocle) ID Verified This tool performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process.
The “pseudotime” is defined as the positioning of cells along the trajectory that quantifies the relative activity or progression of the underlying biological process.
DPT (Diffusion pseudotime) is a tool to estimate the temporal order of differentiating cell in single-cell RNA-seq (scRNA-seq) data. The DPT algorithm using diffusion-like random walks to estimate transitions between cells.
The objective of pseudotime analysis is to take a collection of high-dimensional molecular data from a cross-sectional cohort of individuals and to map these on to a series of one-dimensional quantities, called pseudotimes.
For those with different visual requirements a prescription monocle can be used to correct the vision. They are handy and are popular among chefs who need help seeing recipes as they can be easily clipped on to aprons.
Pseudo-time is a mathematical time function that accounts for the high compressibility of gas. When pseudo-time is used, a significant improvement is achieved in the accuracy of long-term gas rate forecasting or in the analysis of gas production data.
It identifies groups of individuals following similar progressions of some phenomenon over time and estimates the effects of covariates not only on trajectory shape, but also group membership.
What is ATAC-Seq? The assay for transposase-accessible chromatin with sequencing (ATAC-Seq) is a popular method for determining chromatin accessibility across the genome. By sequencing regions of open chromatin, ATAC-Seq can help you uncover how chromatin packaging and other factors affect gene expression.
Clearly groups the cells from oocyte/zygote to blastocyst with a split of the blastocyst cells. But Monocle does not know which the root state is for the tree, we need to define the root.
In general, I find that the trajectories may shift quite a bit depending on the gene set. In a best case scenario, the signal is robust and you get the same results regardless of gene set. Hence, it is always wise to critically view your results and check how robust the signal is.
According to their Vignette: The expression value matrix must have the same number of columns as the phenoData has rows, and it must have the same number of rows as the featureData data frame has rows . Row names of the phenoData object should match the column names of the expression matrix.
The result from Monocle 2 here also shows two main branches. Also, as expected, the stem cells are at the very beginning of the trajectory . The pseudotime values are inverted. Monocle 2 can also be used to find genes differentially expressed along the pseudotime trajectory and clusters of such genes.
Monocle 2 only infers one trajectory for the entire dataset, so non-neuronal cells like endothelial cells and erythrocytes may be mistaken as highly differentiated cells from the neuronal lineage. So we will remove cell types not of the neural or glial lineages. Cell types are also helpful to orient the trajectory; neuronal progenitor cells must come before neurons. Here cell type inference is done programatically with SingleR, which compares gene expression profiles of individual cells to bulk RNA-seq data of purified known cell types.
A whitelist that contains all the barcodes known to be present in the kit is provided by 10x and comes with CellRanger. A CellRanger installation is required, though we will not run CellRanger here.
Does anyone know if there is a way to achieve a pseudotime heatmap in Monocle3? I am looking for a result similar to what Monocle2's "plot_pseudotime_heatmap ()" function generated.
Hi, unfortunately I did not find a solution in Monocle3. I ended up using Monocle2 for heatmaps.
I have a same issue, Please update! How can I make a pseudotime heatmap in Monocle3 and gene differentiation dynamics.
After realizing that monocle3 no longer supports this function, I adapted a solution based on the code published by Tambalo et al., (A single cell transcriptome atlas of the developing zebrafish hindbrain). My solution is posted at the bottom of the link below. The example shows clustering by both k means as well as hierarchical clustering.
According to Monocle3 team's response to # this issue , branched heatmaps are no longer supported in Monocle3. But how did these authors do branched heatmaps by using Monocle3 in this science paper?
Monocle provides functionality for clustering genes according to their pseudotime value. The clustering analysis and plotting are done in one step using the plot_pseudotime_heatmap () function; to save the clustering results, use return_heatmap = TRUE.
The trajectory analysis consists of three stages: 1 Choose genes that define progress 2 Reduce the dimensionality of the data 3 Order cells in pseudotime
Because the full model has more information about each cell, it will do a better job of predicting the expression of the gene in each cell. The question Monocle must answer for each gene is how much better the full model’s prediction is than the reduced model’s.