Description Usage Arguments Value Examples

View source: R/transform_RNAseq.R

Application of common transformations for RNA-seq data prior to fitting a normal mixture model

1 2 | ```
transform_RNAseq(y, norm = "TMM", transformation = "arcsin",
geneLength = NA, meanFilterCutoff = NULL, verbose = TRUE)
``` |

`y` |
( |

`norm` |
The type of estimator to be used to normalize for differences in
library size: “ |

`transformation` |
Transformation type to be used: “ |

`geneLength` |
Vector of length equal to the number of rows in “ |

`meanFilterCutoff` |
Value used to filter low mean normalized counts |

`verbose` |
If |

`tcounts ` |
Transformed counts |

`normCounts ` |
Normalized counts |

`snorm ` |
Per-sample normalization factors divided by mean normalization factor |

`ellnorm ` |
Per-sample normalization factors |

1 2 3 4 5 6 7 8 9 | ```
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
conds <- rep(c("A","B","C","D"), each=2)
## Arcsin transformation, TMM normalization
arcsin <- transform_RNAseq(countmat, norm="TMM", transformation="arcsin")$tcounts
## Logit transformation, TMM normalization
logit <- transform_RNAseq(countmat, norm="TMM", transformation="logit")$tcounts
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.