Description Usage Arguments Value Examples

Function to perform a dimensionality reduction upon the original gene expression matrix data through a method of choice, either linear or no-linear, among the following: Principal Component Analysis (PCA), Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (tSNE), classical Multidimensional Scaling (MDS) and non-metric Multidimensional Scaling.

1 2 | ```
sc_DimensionalityReductionObj(SincellObject, method="PCA", dim=2,
MDS.distance="spearman", bins=c(-Inf,0,1,2,Inf),tsne.perplexity=1,tsne.theta=0.25)
``` |

`SincellObject` |
A SincellObject named list as created by function sc_InitializingSincellObject with a named member "expressionmatrix" containing a numeric matrix that represents a gene expression matrix gathering the expression levels of each single-cell in the experiment (displayed by columns) for each detected gene (displayed by rows). |

`method` |
Dimensionality reduction algorithm to be used. Options are: Principal Component Analysis (method="PCA"), Independent Component Analysis (method="ICA"; using fastICA() function in fastICA package), t-Distributed Stochastic Neighbor Embedding (method="tSNE"; using Rtsne() function in Rtsne package with parameters tsne.perplexity=1 and tsne.theta=0.25), classical Multidimensional Scaling (method="classical-MDS"; using the cmdscale() function) and non-metric Multidimensional Scaling (method="nonmetric-MDS";using the isoMDS() function in MASS package). if method="PCA" is chosen, the proportion of variance explained by each of the principal axes is plotted. We note that Sincell makes use of the Rtsne implementation of the Barnes-Hut algorithm, which approximates the likelihood. The user should be aware that this is a less accurate version of t-SNE than e.g. the one used as basis of viSNE (Amir,E.D. et al. 2013, Nat Biotechnol 31, 545<e2><80><93>552). |

`dim` |
Number of dimensions in low-dimensional space to be retained. Default is dim=2. |

`MDS.distance` |
Distance method to be used if method="classical-MDS" or method="nonmetric-MDS" is selected. The available distances are the Euclidean distance (method="euclidean"), Manhattan distance (also called L1 distance, method="L1"), distance based on Pearson (method="pearson") or Spearman (method="spearman") correlation coefficients, and distance based on Mutual Information (method="MI"). Intervals used to assess Mutual Information are indicated in the parameter <e2><80><9c>bins<e2><80><9d> (see below). |

`bins` |
Intervals used to discretize the data in the case that Mutual Information distance (MDS.distance="MI") is selected. |

`tsne.perplexity` |
perplexity parameter for tSNE algorithm. We refer the reader to the Frequently Asked Questions in http://homepage.tudelft.nl/19j49/t-SNE.html |

`tsne.theta` |
tradeoff between speed and accuracy. We refer the reader to the Frequently Asked Questions in http://homepage.tudelft.nl/19j49/t-SNE.html |

A SincellObject named list whose members are: expressionmatrix=SincellObject[["expressionmatrix"]], cellsLowDimensionalSpace=cellsLowDimensionalSpace, cell2celldist=distance,method=method, dim=dim, MDS.distance=MDS.distance, bins=bins, where cellsLowDimensionalSpace contains the coordinates of each cell (by columns) in each low dimensional axis (by rows), and "cell2celldist" contains the numeric matrix representing the cell-to-cell distance matrix assessed in low dimensional space

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
## Generate some random data
Data <- matrix(abs(rnorm(3000, sd=2)),ncol=10,nrow=300)
## Initializing SincellObject named list
mySincellObject <- sc_InitializingSincellObject(Data)
## To access the gene expression matrix
expressionmatrix<-mySincellObject[["expressionmatrix"]]
## Dimensionality reduction
# Principal Component Analysis (PCA)
mySincellObject <- sc_DimensionalityReductionObj(mySincellObject, method="PCA",dim=2)
# Independent Component Analysis (ICA)
mySincellObject <- sc_DimensionalityReductionObj(mySincellObject, method="ICA",dim=2)
# t-Distributed Stochastic Neighbor Embedding (t-SNE)
mySincellObject <- sc_DimensionalityReductionObj(mySincellObject, method="tSNE",dim=2)
# Classic Multidimensional Scaling (classic-MDS).
mySincellObject <- sc_DimensionalityReductionObj(mySincellObject, method="classical-MDS",dim=2)
# Non-metric Multidimensional Scaling (nonmetric-MDS).
mySincellObject <- sc_DimensionalityReductionObj(mySincellObject, method="nonmetric-MDS",dim=2)
## To access the coordinates of cells (by columns) in low dimensional space (axes by rows)
cellsLowDimensionalSpace<-mySincellObject[["cellsLowDimensionalSpace"]]
## To access the cell-to-cell distance matrix assessed on low dimensional space
cell2celldist<-mySincellObject[["cell2celldist"]]
``` |

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