All functions
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`[`(<DPT>,<index>,<index>,<logicalOrMissing>) `[`(<DPT>,<index>,<missing>,<logicalOrMissing>) `[`(<DPT>,<missing>,<index>,<logicalOrMissing>) `[`(<DPT>,<missing>,<missing>,<logicalOrMissing>) `[[`(<DPT>,<index>,<index>) nrow(<DPT>) ncol(<DPT>) dim(<DPT>)
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DPT Matrix methods |
branch_divide() tips() dataset(<DPT>) `dataset<-`(<DPT>)
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DPT methods |
DPT()
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Diffusion Pseudo Time |
eigenvalues(<DiffusionMap>) `eigenvalues<-`(<DiffusionMap>) eigenvectors(<DiffusionMap>) `eigenvectors<-`(<DiffusionMap>) sigmas(<DiffusionMap>) `sigmas<-`(<DiffusionMap>) dataset(<DiffusionMap>) `dataset<-`(<DiffusionMap>) distance(<DiffusionMap>) `distance<-`(<DiffusionMap>) optimal_sigma(<DiffusionMap>)
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DiffusionMap accession methods |
DiffusionMap()
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Create a diffusion map of cells |
print(<DiffusionMap>) show(<DiffusionMap>)
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DiffusionMap methods |
as.ExpressionSet() read.ExpressionSet()
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Convert object to ExpressionSet or read it from a file |
print(<GeneRelevance>) show(<GeneRelevance>) featureNames(<GeneRelevance>) `featureNames<-`(<GeneRelevance>,<characterOrFactor>) dataset(<GeneRelevance>) `dataset<-`(<GeneRelevance>) distance(<GeneRelevance>) `distance<-`(<GeneRelevance>)
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Gene Relevance methods |
plot_differential_map() plot_gene_relevance() plot_gene_relevance_rank() plot(<GeneRelevance>,<character>) plot(<GeneRelevance>,<numeric>) plot(<GeneRelevance>,<missing>)
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Plot gene relevance or differential map |
gene_relevance()
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Gene relevances for entire data set |
Sigmas() optimal_sigma(<Sigmas>) print(<Sigmas>) show(<Sigmas>)
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Sigmas Object |
as.data.frame(<DiffusionMap>) fortify.DiffusionMap() as.data.frame(<DPT>) fortify.DPT() as.matrix(<DPT>)
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Coercion methods |
colorlegend()
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Color legend |
cube_helix() scale_colour_cube_helix() scale_color_cube_helix() scale_fill_cube_helix()
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Sequential color palette using the cube helix system |
eigenvalues() `eigenvalues<-`() eigenvectors() `eigenvectors<-`() sigmas() `sigmas<-`() dataset() `dataset<-`() distance() `distance<-`() optimal_sigma()
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destiny generics |
destiny
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Create and plot diffusion maps |
dm_predict()
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Predict new data points using an existing DiffusionMap. The resulting matrix can be used in the plot method for the DiffusionMap |
eig_decomp()
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Fast eigen decomposition using eigs |
names(<DiffusionMap>) names(<DPT>) `[[`(<DiffusionMap>,<character>,<missing>) `[[`(<DPT>,<character>,<missing>) `$`(<DiffusionMap>) `$`(<DPT>)
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Extraction methods |
find_dm_k()
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Find a suitable k |
find_sigmas()
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Calculate the average dimensionality for m different gaussian kernel widths (\(\sigma\)). |
find_tips()
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Find tips in a DiffusionMap object |
guo guo_norm
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Guo at al. mouse embryonic stem cell qPCR data |
find_knn()
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kNN search |
l_which()
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Logical which |
plot.DPT() plot(<DPT>,<numeric>) plot(<DPT>,<missing>)
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Plot DPT |
plot.DiffusionMap() plot(<DiffusionMap>,<numeric>) plot(<DiffusionMap>,<missing>)
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3D or 2D plot of diffusion map |
plot(<Sigmas>,<missing>)
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Plot Sigmas object |
projection_dist()
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Projection distance |
random_root()
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Find a random root cell index |
updateObject(<DiffusionMap>) updateObject(<Sigmas>) updateObject(<GeneRelevance>)
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Update old destiny objects to a newer version. |