README.md

In hughesevoanth/moosefun: Functions by Moose

Random useful functions for Moose's work

Authors: David Hughes

Date started: 31st Jan. 2020

About:

This repo contains a variety of helpful functions that I come back to time and time again. Rather than adding them into a variety of different packages I will try to maintain this catch-all that I and my colleagues can source when necessary.

Installation instructions of the moosefun package

1. insure that the devtools library is installed on your local machine
     > ifelse("devtools" %in% rownames(installed.packages()), 
     NA, 
     install.packages("devtools"))

2. install moosefun
    > devtools::install_github("hughesevoanth/moosefun")

iPVs functions have moved to their own package

Date moved: March 6th 2020
For functions to aid in the identification of principal variables in inter-correlated data sets please follow the path below.

https://github.com/hughesevoanth/iPVs

coverting rsid to SNPID and SNPID to rsid

the useful functions are: rsid_2_chrbp() & chrbp_2_rsid()

• Do note that this function is only good for a handful (1 to a few hundred) of SNPs that need an id conversion.
• The reason for this is because for each ID to convert a query is sent over the web and a built in sleep step needed to be implemented to avoid query crashes, which can still occur.
• If you can I would avoid trying to run these functions for more than ~100 SNPs at a time.
• If you need to convert thousands of IDs look into using ANNOVAR (https://annovar.openbioinformatics.org/en/latest/) or some other tool.

EXAMPLE:

#########################
## install biomaRt
#########################
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("biomaRt")

#########################
##  load libraries
#########################
library(moosefun)
library(biomaRt)

#########################
## convert rsids to snpids
#########################
## read in the SNP list
snps = c("rs4987657","rs4987667","rs4987682")
## Extract mapping cooridinates
map = rsid_2_chrbp(snps)
o = order(map$chr, map$bp)
map = map[o,]

#########################
## convert snpids to rsids
#########################
## read in the SNP list
snps = c("7:142569596:A_G","7:142572908:T_C","7:142574913:A_G")

## NOTE: the function splits snpids on ":" and only uses the first two 
## strings as chr and bp, all else is ignored

## Extract mapping cooridinates
map2 = chrbp_2_rsid(snps)
o = order(map2$chr, map2$bp)
map2 = map2[o,]

--

DESCRIPTION OF OTHER FUNCTIONS

ztransform()

• taken from the GenABEL package to perform z-transformations. This function is necessary to run my edited version of the rank normal transformation function.

rntransform()

• taken from the GenABEL package to perform rank normal transformations, but edited to randomly split tied values.

moose_biplot()

• a function to plot a biplot or a PCA with a loadings plot on top of it.
• however, the uniqueness here is that we are not plotting the loading from variables used in the construction of the PCA.
• rather, we are passing a novel set of variables | traits | phenotypes that will be correlated to the PC-axis (1 and 2) and then plotted.

• as an example:

  1. generate a prcomp object:

     pca = prcomp( iris[, 1:4] )
     

  2. or you can generate a probabilistic pca (pcaRes) object

     pca = ppca( as.matrix( iris[, 1:4] ), nPCs = 4)
     

  3. run the function

     moose_biplot(PCA = pca, dataframe_of_phenotypes = iris[, 1:4], 
      plot_top_N_phenotypes = 3, 
      grouping1 = iris$Species, grouping1NAME = "species",
      grouping2 = iris$Species, grouping2NAME =  "species",
      scalearrows = FALSE )
     

  4. the dataframe_of_phenotypes can be any matrix of quantitative trait with the same number of row as passed to the prcomp() or ppca() functions.

  5. grouping1 dictates the color scheme and the ellipses to be drawn Currently limited to 9 groups
  6. grouping2 dictates the plot shapes to be used. Currently limited to 5 groups
  7. scalearrows allows you to scale the largest correlated trait to a rho of 1, and all other arrows in correspoinding manner. This can be done to aid in visualization. Note: if scalearrows is set to TRUE, the relative length of the arrows remain informative.

hughesevoanth/moosefun documentation built on Aug. 22, 2022, 7:04 a.m.