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