Seurat : R toolkit for single cell genomics

addImputedScore | Calculate imputed expression values |

addMetaData | Add Metadata |

addSmoothedScore | Calculate smoothed expression values |

average.expression | Averaged gene expression by identity class |

average.pca | Average PCA scores by identity class |

batch.gene | Identify potential genes associated with batch effects |

buildClusterTree | Phylogenetic Analysis of Identity Classes |

BuildSNN | SNN Graph Construction |

cellPlot | Cell-cell scatter plot |

cluster.alpha | Probability of detection by identity class |

DBclust_dimension | Perform spectral density clustering on single cells |

diffExp.test | Likelihood ratio test for zero-inflated data |

diff.t.test | Differential expression testing using Student's t-test |

dim.plot | Dimensional reduction plot |

doHeatMap | Gene expression heatmap |

doKMeans | K-Means Clustering |

dot.plot | Dot plot visualization |

feature.heatmap | Vizualization of multiple features |

feature.plot | Visualize 'features' on a dimensional reduction plot |

fetch.data | Access cellular data |

find_all_markers | Gene expression markers for all identity classes |

FindClusters | Cluster Determination |

find.markers | Gene expression markers of identity classes |

find.markers.node | Gene expression markers of identity classes defined by a... |

fit.gene.k | Build mixture models of gene expression |

genePlot | Scatter plot of single cell data |

get.centroids | Get cell centroids |

ica | Run Independent Component Analysis on gene expression |

ica.plot | Plot ICA map |

icHeatmap | Independent component heatmap |

icTopGenes | Find genes with highest ICA scores |

initial.mapping | Infer spatial origins for single cells |

jackStraw | Determine statistical significance of PCA scores. |

jackStrawPlot | JackStraw Plot |

Kclust_dimension | Perform spectral k-means clustering on single cells |

marker.test | ROC-based marker discovery |

mean.var.plot | Identify variable genes |

pca | Run Principal Component Analysis on gene expression |

pca.plot | Plot PCA map |

pca.sig.genes | Significant genes from a PCA |

pcHeatmap | Principal component heatmap |

pcTopCells | Find cells with highest PCA scores |

pcTopGenes | Find genes with highest PCA scores |

plotClusterTree | Plot phylogenetic tree |

plotNoiseModel | Visualize expression/dropout curve |

print.pca | Print the results of a PCA analysis |

project.pca | Project Principal Components Analysis onto full dataset |

refined.mapping | Quantitative refinement of spatial inferences |

RegressOut | Regress out technical effects and cell cycle |

rename.ident | Rename one identity class to another |

reorder.ident | Reorder identity classes |

run_diffusion | Run t-distributed Stochastic Neighbor Embedding |

run_tsne | Run t-distributed Stochastic Neighbor Embedding |

ScaleData | Scale and center the data |

set.all.ident | Switch identity class definition to another variable |

set.ident | Set identity class information |

setup | Setup Seurat object |

seurat | The Seurat Class |

subsetCells | Return a subset of the Seurat object |

subsetData | Return a subset of the Seurat object |

tobit.test | Differential expression testing using Tobit models |

tsne.plot | Plot tSNE map |

ValidateClusters | Cluster Validation |

ValidateSpecificClusters | Specific Cluster Validation |

viz.ica | Visualize ICA genes |

viz.pca | Visualize PCA genes |

vlnPlot | Single cell violin plot |

which.cells | Identify matching cells |

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