# Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

## Documentation for package ‘monocle3’ version 1.0.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

aggregate_gene_expression | Creates a matrix with aggregated expression values for arbitrary groups of genes
align_cds | Align cells from different groups within a cds
calc_principal_graph | Function to automatically learn the structure of data by either using L1-graph or the spanning-tree formulization
cell_data_set | The cell_data_set class
cell_data_set-class | The cell_data_set class
cell_data_set-methods | Methods for the cell_data_set class
choose_cells | Choose cells interactively to subset a cds
choose_graph_segments | Choose cells along the path of a principal graph
clear_cds_slots | Clear CDS slots
clusters | Generic to extract clusters from CDS object
clusters-method | Method to extract clusters from CDS object
cluster_cells | Cluster cells using Louvain/Leiden community detection
coefficient_table | Extracts a table of coefficients from a tibble containing model objects
combine_cds | Combine a list of cell_data_set objects
compare_models | Compares goodness of fit for two ways of fitting a set of genes' expression
detect_genes | Detects genes above minimum threshold.
estimate_size_factors | Function to calculate size factors for single-cell RNA-seq data
evaluate_fits | Evaluate the fits of model objects.
exprs | Generic to access cds count matrix
exprs-method | Method to access cds count matrix
fData | Generic to access cds rowData table
fData-method | Generic to access cds rowData table
fData<- | Generic to set cds rowData table
fData<--method | Method to set cds rowData table
find_gene_modules | Cluster genes into modules that are co-expressed across cells.
fit_models | Fits a model for each gene in a cell_data_set object.
generate_centers | Function to reproduce the behavior of eye function in matlab
generate_garnett_marker_file | Generate a Garnett marker file from top_markers output.
get_citations | Access citations for methods used during analysis.
get_genome_in_matrix_path | Get a genome from Cell Ranger output
graph_test | Test genes for differential expression based on the low dimensional embedding and the principal graph
learn_graph | Learn principal graph from the reduced dimension space using reversed graph embedding
load_a549 | Build a cell_data_set from the data stored in inst/extdata directory.
load_cellranger_data | Load data from the 10x Genomics Cell Ranger pipeline
load_mm_data | Load data from matrix market format files.
load_mtx_data | Load data from matrix market format
mc_es_apply | Multicore apply-like function for cell_data_set
model_predictions | Predict output of fitted models and return as a matrix
new_cell_data_set | Create a new cell_data_set object.
normalized_counts | Return a size-factor normalized and (optionally) log-transformed expression matrix
order_cells | Orders cells according to pseudotime.
partitions | Generic to extract partitions from CDS object
partitions-method | Method to extract partitions from CDS object
pData | Generic to access cds colData table
pData-method | Method to access cds colData table
pData<- | Generic to set cds colData table
pData<--method | Method to set cds colData table
plot_cells | Plots the cells along with their trajectories.
plot_cells_3d | Plot a dataset and trajectory in 3 dimensions
plot_genes_by_group | Create a dot plot to visualize the mean gene expression and percentage of expressed cells in each group of cells
plot_genes_in_pseudotime | Plots expression for one or more genes as a function of pseudotime
plot_genes_violin | Plot expression for one or more genes as a violin plot
plot_pc_variance_explained | Plots the percentage of variance explained by the each component based on PCA from the normalized expression data determined using preprocess_cds.
plot_percent_cells_positive | Plots the number of cells expressing one or more genes above a given value as a barplot
preprocess_cds | Preprocess a cds to prepare for trajectory inference
principal_graph | Generic to extract principal graph from CDS
principal_graph-method | Method to extract principal graph from CDS
principal_graph<- | Generic to set principal graph to CDS
principal_graph<--method | Generic to set principal graph to CDS
principal_graph_aux | Generic to extract principal graph auxiliary information from CDS
principal_graph_aux-method | Method to extract principal graph auxiliary information from CDS
principal_graph_aux<- | Generic to set principal graph auxiliary information into CDS
principal_graph_aux<--method | Method to set principal graph auxiliary information into CDS
pseudotime | Generic to extract pseudotime from CDS object
pseudotime-method | Method to extract pseudotime from CDS object
reduce_dimension | Compute a projection of a cell_data_set object into a lower dimensional space with non-linear dimension reduction methods
repmat | function to reproduce the behavior of repmat function in matlab to replicate and tile an matrix
size_factors | Get the size factors from a cds object.
size_factors<- | Set the size factor values in the cell_data_set
soft_assignment | Function to calculate the third term in the objective function
sparse_prcomp_irlba | Principal Components Analysis
top_markers | Identify the genes most specifically expressed in groups of cells