View source: R/getTransformedProps.R

getTransformedProps | R Documentation |

Calculates cell types proportions based on clusters/cell types and sample information and performs a variance stabilising transformation on the proportions.

getTransformedProps(clusters = clusters, sample = sample, transform = NULL)

`clusters` |
a factor specifying the cluster or cell type for every cell. |

`sample` |
a factor specifying the biological replicate for every cell. |

`transform` |
a character scalar specifying which transformation of the proportions to perform. Possible values include "asin" or "logit". Defaults to "asin". |

This function is called by the `propeller`

function and calculates cell
type proportions and performs an arcsin-square root transformation.

outputs a list object with the following components

`Counts ` |
A matrix of cell type counts with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples. |

`TransformedProps ` |
A matrix of transformed cell type proportions with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples. |

`Proportions ` |
A matrix of cell type proportions with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples. |

Belinda Phipson

`propeller`

library(speckle) library(ggplot2) library(limma) # Make up some data # True cell type proportions for 4 samples p_s1 <- c(0.5,0.3,0.2) p_s2 <- c(0.6,0.3,0.1) p_s3 <- c(0.3,0.4,0.3) p_s4 <- c(0.4,0.3,0.3) # Total numbers of cells per sample numcells <- c(1000,1500,900,1200) # Generate cell-level vector for sample info biorep <- rep(c("s1","s2","s3","s4"),numcells) length(biorep) # Numbers of cells for each of 3 clusters per sample n_s1 <- p_s1*numcells[1] n_s2 <- p_s2*numcells[2] n_s3 <- p_s3*numcells[3] n_s4 <- p_s4*numcells[4] cl_s1 <- rep(c("c0","c1","c2"),n_s1) cl_s2 <- rep(c("c0","c1","c2"),n_s2) cl_s3 <- rep(c("c0","c1","c2"),n_s3) cl_s4 <- rep(c("c0","c1","c2"),n_s4) # Generate cell-level vector for cluster info clust <- c(cl_s1,cl_s2,cl_s3,cl_s4) length(clust) getTransformedProps(clusters = clust, sample = biorep)

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