# getTransformedProps: Calculates and transforms cell type proportions In Oshlack/speckle: Statistical methods for analysing single cell RNA-seq data

 getTransformedProps R Documentation

## Calculates and transforms cell type proportions

### Description

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

### Usage

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

### Arguments

 `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".

### Details

This function is called by the `propeller` function and calculates cell type proportions and performs an arcsin-square root transformation.

### Value

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.

### Author(s)

Belinda Phipson

`propeller`

### Examples

```
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)

```

Oshlack/speckle documentation built on Oct. 16, 2022, 9:39 a.m.