Classify New Data
Match the case of character vectors
CollapseSpeciesExpressionMatrix
Slim down a multi-species expression matrix, when only one species is primarily of interenst.
Gene expression markers for all identity classes
Find all markers for a node
Differential expression testing using Student's t-test
Find gene terms from Enrichr
Access cellular data
Find cells with highest scores for a given dimensional reduction technique
Run a custom distance function on an input data matrix
Find genes with highest scores for a given dimensional reduction technique
Old Dot plot visualization (pre-ggplot implementation)
Intuitive way of visualizing how gene expression changes across different identity classes (clusters).
The size of the dot encodes the percentage of cells within a class, while the color encodes the
AverageExpression level of 'expressing' cells (green is high).
Dot plot visualization
Quickly Pick Relevant Dimensions
Dimensional Reduction Cell Embeddings Accessor Function
Gene expression markers of identity classes
Return a subset of the Seurat object
Color tSNE Plot Based on Split
Create a custom color palette
Sets identity class information to be a combination of two object attributes
Convert Seurat objects to other classes and vice versa
Get cell centroids
Dimensional reduction heatmap
Get Cluster Assignments
Dimensional Reduction Accessor Function
Dimensional reduction plot
FastWhichCells
Identify cells matching certain criteria (limited to character values)
Gene expression heatmap
K-Means Clustering
Vizualization of multiple features
Perform spectral k-means clustering on single cells
Plot k-means clusters
Dark Theme
Initialize and setup the Seurat object
Calculate the mean of logged values
Likelihood ratio test for zero-inflated data
Calculate the standard deviation of logged values
Diffusion Maps Cell Embeddings Accessor Function
ICA Gene Loadings Accessor Function
Score cell cycle phases
ROC-based marker discovery
Plot Diffusion map
Feature Locator
Normalize raw data
Visualize 'features' on a dimensional reduction plot
GenesInCluster
Quickly Pick Relevant PCs
Independently shuffle values within each row of a matrix
Significant genes from a PCA
Calculate the variance to mean ratio of logged values
Gene expression heatmap
Cell-cell scatter plot
Calculate the variance of logged values
Perform spectral density clustering on single cells
Differential expression using DESeq2
Extract delimiter information from a string.
PCA Cell Embeddings Accessor Function
Principal component heatmap
PrintCalcVarExpRatioParams
Print Parameters Associated with CalcVarExpRatio
Gene expression markers of identity classes defined by a phylogenetic clade
Cluster Determination
Finds markers that are conserved between the two groups
Print Diffusion Map Calculation Parameters
Print SNN Construction Calculation Parameters
Find cells with highest PCA scores
Build mixture models of gene expression
Dimensional Reduction Gene Loadings Accessor Function
Identify variable genes
Print TSNE Calculation Parameters
Get identity of cells
Single cell ridge plot
Run Canonical Correlation Analysis (CCA) on multimodal data
Scatter plot of single cell data
Hover Locator
ICA Cell Embeddings Accessor Function
Find genes with highest ICA scores
Plot ICA map
Print the results of a ICA analysis
Perform Canonical Correlation Analysis
Print ICA Calculation Parameters
Run coinertia analysis on multimodal data
Read 10X hdf5 file
Accessor function for multimodal data
Determine statistical significance of PCA scores.
Poisson test for UMI-count based data
JackStraw Plot
Old R based implementation of ScaleData. Scales and centers the data
Switch identity class definition to another variable
Print AlignSubspace Calculation Parameters
Demultiplex samples based on data from cell 'hashing'
Infer spatial origins for single cells
Merge childen of a node
Quantitative refinement of spatial inferences
Perform Canonical Correlation Analysis with more than two groups
Run Principal Component Analysis on gene expression using IRLBA
Return a subset of columns for a matrix or data frame
Project Dimensional reduction onto full dataset
Plot CC bicor saturation plot
Project Principal Components Analysis onto full dataset
Assay Data Mutator Function
Return a subset of the Seurat object
A purple and yellow color palette
Merge Seurat Objects
Negative binomial test for UMI-count based data
Hashtag oligo heatmap
Set Cluster Assignments
Single cell violin plot
Visualize PCA genes
Independent component heatmap
Find cells with highest ICA scores
Differential expression using MAST
Set identity class information
Make object sparse
Normalize Assay Data
Return a subset of the Seurat object.
Apply a ceiling and floor to all values in a matrix
Load in data from 10X
Plot PCA map
PCA Gene Loadings Accessor Function
Print CCA Calculation Parameters
Negative binomial test for UMI-count based data (regularized version)
Print the results of a dimensional reduction analysis
A small example version of the PBMC dataset
Cell cycle genes
Print FindClusters Calculation Parameters
Run UMAP
Convert the cluster labels to a numeric representation
Run diffusion map
Sample UMI
Print the results of a PCA analysis
Print the calculation
Print PCA Calculation Parameters
Find genes with highest PCA scores
Remove data from a table
Deprecated function(s) in the Seurat package
Shuffle a vector
Rename cells
Plot phylogenetic tree
Visualize Dimensional Reduction genes
Run PHATE
Run Independent Component Analysis on gene expression
Rename one identity class to another
Split Dot plot visualization
Reorder identity classes
Splits object into a list of subsetted objects.
Run t-distributed Stochastic Neighbor Embedding
Save cluster assignments to a TSV file
Return a subset of rows for a matrix or data frame
Plot tSNE map
Visualize ICA genes
Scale and center the data.
Dimensional Reduction Mutator Function
Update old Seurat object to accomodate new features
Set identity class information
Differential expression testing using Tobit models
Identify cells matching certain criteria
Cluster Validation
Differential expression using Wilcoxon Rank Sum
Transfer identity class information (or meta data) from one object to another
Specific Cluster Validation
The Seurat Class
View variable genes
Identify potential genes associated with batch effects
A black and white color palette
Assess Internal Nodes
Assess Cluster Split
SNN Graph Construction
Calculate module scores for gene expression programs in single cells
Add samples into existing Seurat object.
Calculate an alignment score
Calculate the ratio of variance explained by ICA or PCA to CCA
Calculate smoothed expression values
Align subspaces using dynamic time warping (DTW)
Phylogenetic Analysis of Identity Classes
Build Random Forest Classifier
Augments ggplot2 scatterplot with a PNG image.
Probability of detection by identity class
Averaged gene expression by identity class
Average PCA scores by identity class
Calculate imputed expression values
Add Metadata