Man pages for paodan/studySeu
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 |
