Command line scripts wrapping functions in Monocle3
Create and activate a conda environment with r-base and python, then follow monocle3 installation guide.
Use provided install_monocle3.sh which uses conda to install most of the dependencies.
$ conda activate <monocle3-env>
$ Rscript -e 'devtools::install_github("ebi-gene-expression-group/monocle-scripts")'The installed executable script is at
$CONDA_PREFIX/<monocle3-env>/lib/R/library/MonocleScripts/exec/monocle3.
This test requires bats available in the same conda environment as monocle-scripts. Run:
$ wget 'https://github.com/ebi-gene-expression-group/monocle-scripts/raw/develop/monocle-scripts-post-install-tests.bats'
$ chmod +x monocle-scripts-post-install-tests.bats
$ ./monocle-scripts-post-install-tests.batsCurrently only covers steps introduced in Monocle3 documentation: Constructing single-cell trajectories
Usage: monocle3 [-h] <command> ...
Commands:
create Creation of Monocle 3 object from expression and metadata.
preprocess Normalisation, scaling, initial dimension reduction.
reduceDim Reduce dimensionality by UMAP.
partition Partition cells into groups.
learnGraph Learn trajectories.
orderCells Adjust the start of pseudo-time
diffExp Identify genes with varing expression along trajectories.
plotCells Visualise trajectories.
Options:
-h, --help Show this help message and exitUsage: monocle3 create [options] <output_object>
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--expression-matrix=STR
Expression matrix, genes as rows, cells as columns. Required input. Provide as TSV, CSV, RDS or MTX.
--cell-metadata=STR
Per-cell annotation, optional. Row names must match the column names of the expression matrix. Provide as TSV, CSV or RDS.
--gene-annotation=STR
Per-gene annotation, optional. Row names must match the row names of the expression matrix. Provide as TSV, CSV or RDS.
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 preprocess [options] <input_object> <output_object>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--method=STR
The initial dimension method to use, choose from {PCA, LSI}. [Default: PCA]
--num-dim=INT
The dimensionality of the reduced space. [Default: 50]
--norm-method=STR
Determines how to transform expression values prior to reducing dimensionality, choose from {log, size_only}. [Default: log]
--use-genes=STR
Manually subset the gene pool to these genes for dimensionality reduction, NULL to skip. [Default: NULL]
--residual-model-formula-str=STR
A string model formula specifying effects to subtract from the data, NULL to skip. [Default: NULL]
--pseudo-count=FLOAT
Amount to increase expression values before dimensionality reduction. [Default: 1]
--no-scaling
When this option is NOT set, scale each gene before running trajectory reconstruction.
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 reduceDim [options] <input_object> <output_object>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--max-components=INT
The dimensionality of the reduced space. [Default 2]
--reduction-method=STR
The algorithm to use for dimensionality reduction, choose from {UMAP, tSNE, PCA, LSI}. [Default: UMAP]
--preprocess-method=STR
The preprocessing method used on the data, choose from {PCA, LSI}. [Default: PCA]
--cores=CORES
The number of cores to be used for dimensionality reduction. [Default: 1]
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 partition [options] <input_object> <output_object>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--reduction-method=STR
The dimensionality reduction to base the clustering on, choose from {UMAP, tSNE, PCA, LSI}. [Default: UMAP]
--knn=INT
Number of nearest neighbours used for Louvain clustering. [Default: 20]
--weight
When this option is set, calculate the weight for each edge in the kNN graph.
--louvain-iter=INT
The number of iteration for Louvain clustering. [Default: 1]
--resolution=FLOAT
Resolution of clustering result, specifying the granularity of clusters. Not used by default and the standard igraph louvain clustering algorithm will be used.
--partition-qval=FLOAT
The q-value threshold used to determine the partition of cells. [Default: 0.05]
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 learnGraph [options] <input_object> <output_object>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--no-partition
When this option is set, learn a single tree structure for all the partitions. If not set, use the partitions calculated when clustering and identify disjoint graphs in each.
--no-close-loop
When this option is set, skip the additional run of loop closing after computing the principal graphs to identify potential loop structure in the data space.
--euclidean-distance-ratio=FLOAT
The maximal ratio between the euclidean distance of two tip nodes in the spanning tree inferred from SimplePPT algorithm and that of the maximum distance between any connecting points on the spanning tree allowed to be connected during the loop closure procedure. [Default: 1]
--geodesic-distance-ratio=FLOAT
The minimal ratio between the geodestic distance of two tip nodes in the spanning tree inferred from SimplePPT algorithm and that of the length of the diameter path on the spanning tree allowed to be connected during the loop closure procedure. (Both euclidean_distance_ratio and geodestic_distance_ratio need to be satisfied to introduce the edge for loop closure.)
--no-prune-graph
When this option is set, perform an additional run of graph pruning to remove smaller insignificant branches.
--minimal-branch-len=INT
The minimal length of the diameter path for a branch to be preserved during graph pruning procedure. [Default: 10]
--orthogonal-proj-tip
When this option is set, perform orthogonal projection for cells corresponding to the tip principal points.
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 orderCells [options] <input_object> <output_object>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_object>:
Output object, can be SingleCellExperiment(sce), Seurat object(seurat), or CellDataSet V3(cds3). Only cds3 is supported currently.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-object-format=STR
Format of output object. [Default: cds3]
-I, --introspective
Print introspective information of the output object.
--root-pr-nodes=STR
The starting principal points. We learn a principal graph that passes
through the middle of the data points and use it to represent the
developmental process. Exclusive with --root-cells.
--root-cells=STR
The starting cells. Each cell corresponds to a principal point and multiple cells can correspond to the same principal point. Exclusive with --root-pr-nodes.
--cell-phenotype=STR
The cell phenotype (column in pdata) used to identify root principal nodes.
--root-type=STR
The value of the phenotype specified by "--cell-pheontype" that defines cells root principal nodes.
--reduction-method=STR
The dimensionality reduction that was used for clustering, choose from {UMAP, tSNE, PCA, LSI}. [Default: UMAP]
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 diffExp [options] <input_object> <output_table>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_table>:
Output table file name.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-table-format=STR
Format of output table, choose from {tsv, csv}. [Default: tsv]
-I, --introspective
Print introspective information of the output table.
--neighbor-graph=STR
What neighbor graph to use, "principal_graph" recommended for trajectory analysis, choose from {principal_graph, knn}. [Default: knn]
--reduction-method=STR
The dimensionality reduction to base the clustering on, choose from {UMAP}. [Default: UMAP]
--knn=KNN
Number of nearest neighbors used for building the kNN graph which is passed to knn2nb function during the Moran's I (Geary's C) test procedure.
--method=METHOD
A character string specifying the method for detecting significant genes showing correlated expression along the principal graph embedded in the low dimensional space, choose from {Moran_I}. [Default: Moran_I]
--alternative=ALTERNATIVE
A character string specifying the alternative hypothesis, choose from {greater, less, two.sided}. [Default: greater]
--cores=CORES
The number of cores to be used while testing each gene for differential expression. [Default: 1]
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exitUsage: monocle3 plotCells [options] <input_object> <output_plot>
<input_object>:
Input object, can be SingleCellExperiment(sce), Seurat object(seurat), CellDataSet V2(cds2) or V3(cds3). Only cds3 is supported currently.
<output_plot>:
Output plot file name.
Options:
-f STR, --input-object-format=STR
Format of input object. [Default: cds3]
-F STR, --output-plot-format=STR
Format of output plot, choose from {png, pdf}. [Default: png]
--xdim=XDIM
The column of reducedDimS(cds) to plot on the horizontal axis. [Default: 1]
--ydim=YDIM
The column of reducedDimS(cds) to plot on the vertical axis. [Default: 2]
--reduction-method=STR
The dimensionality reduction for plotting, choose from {UMAP, tSNE, PCA, LSI}. [Default: UMAP]
--color-cells-by=COLOR-CELLS-BY
The cell attribute (e.g. the column of pData(cds)) to map to each cell's color, or one of {clusters, partitions, pseudotime}. [Default: pseudotime]
--genes=STR
A list of gene IDs/short names to plot, one per panel.
--norm-method=STR
Determines how to transform expression values for plotting, choose from {log, size_only}. [Default: log]
--cell-size=CELL-SIZE
The size of the point for each cell. [Default: 1.5]
--alpha=ALPHA
The alpha aesthetics for the original cell points, useful to highlight the learned principal graph.
--label-cell-groups
If set, display the cell group names directly on the plot. Otherwise include a color legend on the side of the plot.
--no-trajectory-graph
When this option is set, skip displaying the trajectory graph inferred by learn_graph().
--label-groups-by-cluster
If set, and setting --color-cells-by to something other than cluster, label the cells of each cluster independently. Can result in duplicate labels being present in the manifold.
--label-leaves
If set, label the leaves of the principal graph.
--label-roots
If set, label the roots of the principal graph.
--label-branch-points
If set, label the branch points of the principal graph.
-v, --verbose
Emit verbose output.
-h, --help
Show this help message and exit