Package 'metaboprep'

Title: Metabolomics data preparation and processing pipeline
Description: Reads in raw Metabolon and Nightingale xls sheets and aids in data preparation of all metabolomics data sets.
Authors: Laura Corbin [aut], David Hughes [aut, cre]
Maintainer: David Hughes <[email protected]>
License: MIT + file LICENSE
Version: 1.0.1
Built: 2024-12-31 04:02:34 UTC
Source: https://github.com/remlapmot/metaboprep

Help Index

median batch normalization

Description

This function median normalizes multivariable data often processed in batches, such as metabolomic and proteomic data sets.

Usage

batch_normalization(
  wdata,
  feature_data_sheet = NULL,
  sample_data_sheet = NULL,
  feature_runmode_col = NULL,
  batch_ids = NULL
)

Arguments

wdata

the metabolite data frame samples in row, metabolites in columns
feature_data_sheet

a data frame containing the feature annotation data
sample_data_sheet

a data frame containing the sample annotation data
feature_runmode_col

a string identifying the column name in the feature_data_sheet that identifies the run mode for each feature (metabolites of proteins).
batch_ids

a string vector, with a length equal to the number of samples in the data set that identifies what batch each sample belongs to.

Value

returns the wdata object passed to the function median normalized given the batch information provided.

Examples

####################################
## with a vector of batch variables
####################################
## define the data set
d1 = sapply(1:10, function(x){ rnorm(25, 40, 2) })
d2 = sapply(1:10, function(x){ rnorm(25, 35, 2) })
ex_data = rbind(d1,d2)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## define the batch
batch = c( rep("A", 25), rep("B", 25)  )
## normalize by batch
norm_wdata = batch_normalization(wdata = ex_data, batch_ids = batch )

Cramer's V (phi)

Description

Calculates Cramer's V for a table of nominal variables; confidence intervals by bootstrap. Function taken from the rcompanion Rpackage.

Usage

cramerV(
  x,
  y = NULL,
  ci = FALSE,
  conf = 0.95,
  type = "perc",
  R = 1000,
  histogram = FALSE,
  digits = 4,
  bias.correct = FALSE,
  reportIncomplete = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x

Either a two-way table or a two-way matrix. Can also be a vector of observations for one dimension of a two-way table.
y

If x is a vector, y is the vector of observations for the second dimension of a two-way table.
ci

If TRUE, returns confidence intervals by bootstrap. May be slow.
conf

The level for the confidence interval.
type

The type of confidence interval to use. Can be any of "norm", "basic", "perc", or "bca". Passed to boot.ci.
R

The number of replications to use for bootstrap.
histogram

If TRUE, produces a histogram of bootstrapped values.
digits

The number of significant digits in the output.
bias.correct

If TRUE, a bias correction is applied.
reportIncomplete

If FALSE (the default), NA will be reported in cases where there are instances of the calculation of the statistic failing during the bootstrap procedure.
verbose

If TRUE, prints additional statistics.
...

Additional arguments passed to chisq.test.

Details

Cramer's V is used as a measure of association between two nominal variables, or as an effect size for a chi-square test of association. For a 2 x 2 table, the absolute value of the phi statistic is the same as Cramer's V.

Because V is always positive, if type="perc", the confidence interval will never cross zero. In this case, the confidence interval range should not be used for statistical inference. However, if type="norm", the confidence interval may cross zero.

When V is close to 0 or very large, or with small counts, the confidence intervals determined by this method may not be reliable, or the procedure may fail.

Value

A single statistic, Cramer's V. Or a small data frame consisting of Cramer's V, and the lower and upper confidence limits.

Author(s)

Salvatore Mangiafico, [email protected]

References

http://rcompanion.org/handbook/H_10.html

Examples

## Not run: 
### Example with table
data(Anderson)
fisher.test(Anderson)
cramerV(Anderson)

## End(Not run)

### Example with two vectors
Species = c(rep("Species1", 16), rep("Species2", 16))
Color   = c(rep(c("blue", "blue", "blue", "green"),4),
            rep(c("green", "green", "green", "blue"),4))
fisher.test(Species, Color)
cramerV(Species, Color)

Estimate power for a binary variable

Description

This function allows you estimate power for a binary variable given the sample size, effect size, significance threshold.

Usage

eval.power.binary(N, effect, alpha)

Arguments

N

a numeric vector of total study sample size, cases and controls will both be defined as N/2.
effect

a numeric vector of effect size
alpha

a numeric vector of significance thresholds

Value

a matrix of parameter inputs and an estimate(s) of power are returned as a matrix

Examples

eval.power.binary(N = 1000, effect = seq(0.01, 0.3, by = 0.01), alpha = 0.05)

Estimate power for a binary variable in an imbalanced design

Description

This function allows you estimate power for a binary variable given a defined number of case samples, control samples, effect size, and significance threshold.

Usage

eval.power.binary.imbalanced(N_case, N_control, effect, alpha)

Arguments

N_case

a numeric vector of sample size of cases
N_control

a numeric vector of sample size of controls
effect

a numeric vector of effect size
alpha

a numeric vector of significance thresholds

Value

a matrix of paramater inputs and power estimates are returned as a matrix

Examples

eval.power.binary.imbalanced( N_case = 1000, 
 N_control = 1000, 
 effect = 0.01, 
 alpha = 0.05 )

eval.power.binary.imbalanced( N_case = c(1000, 2000), 
 N_control = c(1000, 2000), 
 effect = 0.01, 
 alpha = 0.05 )

estimate power for continuous variable

Description

This function estimates power for a continuous variable given the sample size, effect size, significance threshold, and the degrees of freedom.

Usage

eval.power.cont(N, n_coeff, effect, alpha)

Arguments

N

Sample size
n_coeff

degrees of freedom for numerator
effect

effect size
alpha

significance level (Type 1 error)

Examples

eval.power.cont(N = 1000, n_coeff = 1, effect = 0.0025, alpha = 0.05)

feature plots to file

Description

This function to plots a scatter plot, a histogram, and a few summary statistics of each feature in a data frame to a pdf file

Usage

feature_plots(wdata, outdir = NULL, nsd = 5)

Arguments

wdata

a data frame of feature (ex: metabolite or protein) abundance levels
outdir

output directory path
nsd

number of SD from the mean to plot an outlier line on the scatter plot and histogram

Value

a ggplot2 object

Examples

ex_data = sapply(1:20, function(x){ rnorm(250, 40, 5) })
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
feature_plots(ex_data)

summary statistics for features

Description

This function allows you to 'describe' metabolite features using the describe() function from the psych package, as well as estimate variance, a dispersion index, the coeficent of variation, and shapiro's W-statistic.

Usage

feature.describe(wdata)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns

Value

a data frame of summary statistics for features (columns) of a matrix

Examples

ex_data = sapply(1:20, function(x){ rnorm(250, 40, 5) })
feature.describe(ex_data)

estimate feature missingness

Description

This function estimates feature missingess, with a step to exclude poor samples identified as those with a sample missingness greater than 50

Usage

feature.missingness(wdata)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns

Value

a data frame of percent missingness for each feature

Examples

ex_data = sapply(1:5, function(x){rnorm(10, 45, 2)})
ex_data[ sample(1:length(ex_data), 15) ] = NA
feature.missingness(wdata = ex_data )

outlier sample count for a features

Description

This function takes a matrix of data (samples in rows, features in columns) and counts the number of outlying samples each feature has.

Usage

feature.outliers(wdata, nsd = 5)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
nsd

the number of standard deviation from the mean outliers are identified at. Default value is 5.

Value

a data frame out sample outlier counts for each feature (column) in the matrix

Examples

ex_data = sapply(1:20, function(x){ rnorm(250, 40, 5) })
s = sample(1:length(ex_data), 200)
ex_data[s] = ex_data[s] + 40
## run the function
fout = feature.outliers(ex_data)

feature summary statistics

Description

This function estimates feature statistics for samples in a matrix of metabolite features.

Usage

feature.sum.stats(
  wdata,
  sammis = NA,
  tree_cut_height = 0.5,
  outlier_udist = 5,
  feature_names_2_exclude = NA
)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
sammis

a vector of sample missingness estimates, that is ordered to match the samples in the rows of your data matrix.
tree_cut_height

tree cut height is the height at which to cut the feature|metabolite dendrogram to identify "independent" features. tree_cut_height is 1-absolute(Spearman's Rho) for intra-cluster correlations.
outlier_udist

the interquartile range unit distance from the median to call a sample an outlier at a feature.
feature_names_2_exclude

A vector of feature|metabolite names to exclude from the tree building, independent feature identification process.

Value

a list object of length two, with (1) a data frame of summary statistics and (2) a hclust object

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
## define the data set
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 450)] = NA
## add in some technical error to two samples
m = apply(ex_data, 2, function(x){ mean(x, na.rm = TRUE) })
ex_data[c(1,10), ] = ex_data[1, ] + (m*0.00001) 
## run the function
fss = feature.sum.stats(ex_data)
## feature summary table
fss$table[1:5, ]
## plot the dendrogram
plot(fss$tree, hang = -1)

identify independent features

Description

This function identifies independent features using Spearman's Rho, and a dendrogram tree cut step. The feature returned as 'independent' within is k-cluster is the feature with the least missingness or chosen at random in case of missingness ties.

Usage

feature.tree.independence(wdata)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns

Value

a data frame of 'k' cluster or group ids, and a 0/1 binary identifying if a feature was identified as and independent ('1') feature or not ('0').

Examples

cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.9, 0.9, 0.8)
cmat[2,] = c(0.9, 1, 0.7, 0.6)
cmat[3,] = c(0.9, 0.7, 1, 0.8)
cmat[4,] = c(0.8, 0.6, 0.8,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## run the function
df = feature.tree.independence(ex_data)

identify continuos trait effect sizes

Description

This function estimates an appropriate distribution of effect sizes to simulate in a continuous trait power analysis.

Usage

find.cont.effect.sizes.2.sim(mydata)

Arguments

mydata

Your metabolite data matrix, with samples in rows

Value

a vector of effect sizes

Examples

ex_data = sapply(1:10, function(x){ rnorm(250, 40, 5) })
find.cont.effect.sizes.2.sim(ex_data)

identify effect sizes

Description

This function estimates an appropriate distribution of effect sizes to simulate in a power analysis.

Usage

find.PA.effect.sizes.2.sim(mydata)

Arguments

mydata

Your metabolite data matrix, with samples in rows

Value

a vector of effect sizes

Examples

ex_data = sapply(1:10, function(x){ rnorm(250, 40, 5) })
find.PA.effect.sizes.2.sim(ex_data)

generate metaboprep summary html report

Description

This function generates the html report.

Usage

generate_report(
  full_path_2_Rdatafile = "ReportData.Rdata",
  dir_4_report = "./",
  path_2_Rmd_template = file.path(system.file("rmarkdown", package = "metaboprep"),
    "metaboprep_Report_v0.Rmd")
)

Arguments

full_path_2_Rdatafile

full path to the Rdatafile
dir_4_report

directory to place the report
path_2_Rmd_template

full path to the html report template

Value

Writes a html report to file

an html file written to file

greedy selection

Description

This function identifies features that have less than a minimum number of complete pairwise observations and removes one of them, in a greedy fashion. The need for this function is in instances where missingness is extreme between two features the number of paired observation between them may be to to be informative. Thus one, but not both should be removed from the analysis to avoid analytical error based on sample sizes.

Usage

greedy.pairwise.n.filter(wdata, minN = 50)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
minN

the minimum sample size (n) for pairwise comparisons

Value

a vector of feature names

Examples

set.seed(123)
ex_data = sapply(1:10, function(x){ rnorm(250, 40, 5) })
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[ sample(1:250, 200) ,1] = NA
ex_data[ sample(1:250, 200) ,2] = NA
ex_data[ sample(1:250, 200) ,3] = NA
## Estimate missingness and generate plots
greedy.pairwise.n.filter(ex_data)

identify outliers

Description

given a vector of data, identify those positions that are 'nsd' standard deviation units from the mean

Usage

id.outliers(x, nsd = 5)

Arguments

x

a vector of numerical values
nsd

the number of standard deviation from the mean outliers are identified at. Default value is 5.

Value

a vector indexing which samples are outliers

Examples

ex_data = rnbinom(500, mu = 40, size = 5)
id.outliers(ex_data, nsd = 2)

correlation matrix

Description

This function estimates a correlation matrix returning wither the correlation estimates or their p-values

Usage

make.cor.matrix(wdata, cor_method = "kendall", minN = 50, var2return = "cor")

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
cor_method

defaulted to "kendall" this is the correlation method to use in the function cor.test()
minN

sefaulted to 50, this is the minimum number of observations that must be available in pairs to perform analysis
var2return

sefaulted to "cor", other option is "pvalue" is a the flag indicating which estimate to return from the function.

Value

a matrix of correlation estimates or p-values

Examples

cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## return correlation estimates
cor_mat = make.cor.matrix(ex_data, var2return = "cor")
## return p-values
cor_mat = make.cor.matrix(ex_data, var2return = "pvalue")

generate a hclust dendrogram

Description

This estimates a dendrogram of feautres based on correlation coefficeint of your choice, and a clustering method of choice

Usage

make.tree(wdata, cor_method = "spearman", hclust_method = "complete")

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
cor_method

the correlation method used in the function cor(). Default is "spearman".
hclust_method

the dendrogram clustering method used in the construction of the tree. Default is "complete".

Value

an hclust object

Examples

cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## estiamte the dendrogram
tree = make.tree(ex_data)
## plot the dendrogram
plot(tree, hang = -1)

median impute missing data

Description

This function imputes features (columns) of a metabolome matrix to median estimates. Useful for PCA.

Usage

median_impute(wdata)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns

Value

the matrix passed to the function but with NA's imputed to each columns median value.

Examples

ex_data = sapply(1:100, function(x){ rnorm(250, 40, 5) })
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 500)] = NA
## Estimate missingness and generate plots
imp_data = median_impute(ex_data)

batch effect on numeric matrix

Description

This function estimates the effects of a categorical (batch) variable on a matrix of features in univariate linear models.

Usage

met2batch(wdata, batch)

Arguments

wdata

the numeric data matrix with samples in row, features in columns
batch

a single vector containing a vector based batch variable

Value

a list object of length two with (1) a data frame of summary statistics on (a) the number of tested features ,(b) the mean batch effect across all features ,(c) the mean batch effect across all associated (BH FDR<0.05) features ,(d) the number of associated (BH FDR<0.05) features.

Examples

d1 = sapply(1:10, function(x){ rnorm(50, 30, 5) })
d2 = sapply(1:10, function(x){ rnorm(50, 40, 5) })
ex_data = rbind(d1, d2)
d3 = sapply(1:10, function(x){ rnorm(100, 40, 5) })
ex_data = cbind(ex_data, d3)
lot = c( rep("A",50), rep("B",50) )
ex = met2batch(wdata = ex_data, batch = lot)

missingness summary plots

Description

This function sumarizes sample and feature missingness in tables and in a summary plot.

Usage

missingness.sum(mydata)

Arguments

mydata

metabolite data matrix, with samples in rows and metabolite features in columns.

Value

a list object of length four with (1) a vector of sample missingess,(2) a vector of feature missingness ,(3) a table summarizing missingness ,(4) a list of ggplot2 plots for sample and feature histogram distribution and summary tables

Examples

ex_data = sapply(1:100, function(x){ rnorm(250, 40, 5) })
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 500)] = NA
## Estimate missingness and generate plots
ms = missingness.sum(ex_data)
## plots
ggpubr::ggarrange(plotlist = ms$plotsout, ncol = 2, nrow = 2)

multivariate analysis

Description

This function performs a multivariate analysis over a dependent|response and numerous independent|explanatory variable

Usage

multivariate.anova(dep, indep_df)

Arguments

dep

a vector of the dependent variable
indep_df

a data frame of the independent variable

Value

ggplot2 table figure of

Examples

cmat = matrix(1, 3, 3 )
cmat[1,] = c(1, 0.5, 0.3)
cmat[2,] = c(0.5, 1, 0.25)
cmat[3,] = c(0.3, 0.25, 1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 250, mu = c(5, 45, 25), Sigma = cmat )
colnames(ex_data) = c("outcome","age","bmi")
multivariate.anova(dep = ex_data[,1], indep_df = ex_data[, 2:3])

Nightingale Health metabolomics annotation data set

Description

A dataset containing annotation data on Nightingale Health NMR metabolites (lipids), compiled from public resources with additional annotation made here. Some metabolites are repeated in the ng_anno data frame as we have observed that Nightingale Health has changed the spellings a few times. Our code and this data frame attempts to capture all possible spellings that we have observed. We note that as Nightingale Health continues to add new metabolites, updates names, modifies names, or changes spellings the automation of the annotation may fail for some features|metabolites.

Usage

ng_anno

Format

A data frame with 291 rows and 7 variables:

metabolite

metabolite id

raw.label

metabolite name and units

class

metabolite annotation class

subclass

metabolite annotation subclass

label

metabolite name and units

label.no.units

metabolite name without units

derived_features

a binary yes|no indicating if the metabolite variable is a variable derived of two or more other features in the data set

...

Source

http://nightingalehealth.com/

identify outlier sample indexes in a matrix

Description

Given a matrix of data this function returns a matrix of 0|1, of the same structure with 1 values indicating outliers. It is an expansion of the function id.outliers(), applied to columns of a matrix.

Usage

outlier.matrix(data, nsd = 5, meansd = FALSE)

Arguments

data

a matrix of numerical values, samples in row, features in columns
nsd

the unit distance in SD or IQR from the mean or median estimate, respectively outliers are identified at. Default value is 5.
meansd

set to TRUE if you would like to estimate outliers using a mean and SD method; set to FALSE if you would like to estimate medians and inter quartile ranges. The default is FALSE.

Value

a matrix of 0 (not a sample outlier) and 1 (outlier)

Examples

ex_data = sapply(1:25, function(x){ rnorm(250, 40, 5) })
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some technical error to two samples
m = apply(ex_data, 2, function(x){ mean(x, na.rm = TRUE) })
ex_data[c(1,50), ] = ex_data[1, ] + (m*4) 
Omat = outlier.matrix(ex_data)
## how many outliers identified
sum(Omat)

feature summary plots

Description

This function plots the distribution of and identifiy outliers for every metabolite (column) in a data frame.

Usage

outlier.summary(dtst, pdf_filename = "./feature_distributions.pdf", nsd = 5)

Arguments

dtst

numeric data frame
pdf_filename

name of the pdf out file
nsd

number of SD to consider as outliers, 5 is default

Value

print summary figures for each column of data in the data frame to a pdf file.

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
## define the data set
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## run the function
outlier.summary(ex_data)

identify outliers

Description

This function identifies outliers from a vector of data at SD units from the mean.

Usage

outliers(x, nsd = 3)

Arguments

x

a numerical vector of data
nsd

the number of SD units from the mean to be used as an outlier cutoff.

Value

a list object of length three. (1) a vector of sample indexes indicating the outliers, (2) the lower outlier cuttoff value, (3) the upper outlier cuttoff value.

Examples

ex_data = rnbinom(500, mu = 40, size = 5)
outliers(ex_data)

principal component analysis

Description

This function performs two principal component analysis. In the first, missing data is imputed to the median. In the second a probablistic PCA is run to account for the missingness. Subsequent to the derivation of the PC, the median imputed PC data is used to identify the number of informative or "significant" PC by (1) an acceleration analysis, and (2) a parrallel analysis. Finally the number of sample outliers are determined at 3, 4, and 5 standard deviations from the mean on the top PCs as determined by the acceleration factor analysis.

Usage

pc.and.outliers(metabolitedata, indfeature_names, outliers = TRUE)

Arguments

metabolitedata

the metabolite data matrix. samples in row, metabolites in columns
indfeature_names

a vector of independent feature names | column names.
outliers

defaulted to TRUE, a TRUE|FALSE binary flagging if you would like outliers identified.

Value

a list object of length five, with (1) a data frame of PC loadings, (2) a vector of variance explained estimates for each PC, (3) an estimate of the number of informative or top PCs determined by the acceleration factor analysis, (4) an estimate of the number of informative or top PCs determined by parrallel analysis, (5) a data frame of the probablisitic PC loadings

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
## define the data set
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 450)] = NA
## add in some technical error to two samples
m = apply(ex_data, 2, function(x){ mean(x, na.rm = TRUE) })
ex_data[c(1,10), ] = ex_data[1, ] + (m*0.00001) 
## run the PCA
ex_out = pc.and.outliers(ex_data, indfeature_names = sample(colnames(ex_data), 15) )

PCA factor analysis and annotation enrichment

Description

This function performs (1) a factor analysis on numeric data and PC loadings derived from said data, then subsequently (3) performs a hypergeometrix enrichment test to ask if a certain class of variables are enriched on a particular PC.

Usage

pca.factor.analysis(
  metabolitedata,
  pcloadings,
  sigthreshold = 0.3,
  feature_anno = feature_data$SUPER_PATHWAY
)

Arguments

metabolitedata

a matrix or data frame of metabolite data
pcloadings

a matrix or data frame of pc loadings you wish to test
sigthreshold

Spearman's rho correlation threshold to declare an association between the numeric variable and a PC loading
feature_anno

a vector of variable annotations to perform the hypergeomtric enrichment on

Value

a list object of length 2: (1) a list object of enrichment_tables, and (2) the Spearman's correlation matrix between features and the PC loadings

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## annotation
met_anno = c( rep("A", 10), rep("B", 10), rep("C", 8) )
## PCA
pca = prcomp(ex_data, center = TRUE, scale = TRUE)
## run pca.factor.analysis()
ex_out = pca.factor.analysis(metabolitedata = ex_data, 
                             pcloadings = pca$x[,1:5], 
                             sigthreshold = 0.3, 
                             feature_anno = met_anno )

pca pairs plot

Description

This function generates an upper triangle PCA plot for all pairs of PC loadings provided.

Usage

pcapairs_bymoose(myloadings, varexp, pcol = "dodgerblue")

Arguments

myloadings

a matrix or data frame of PC loadings (only those you would like to plot).
varexp

a vector of the the variance explained by each PC
pcol

plot colors for the dots background

Value

a base R plot

Examples

ex_data = sapply(1:5, function(x){rnorm( 50, 0, 2) })
## add in some extreme values to a sample
ex_data[1,] = ex_data[1,] + sample( c(8, -8), ncol(ex_data), replace = TRUE )
ex_data[2,] = ex_data[2,] + sample( c(3, -3), ncol(ex_data), replace = TRUE )
## plot
pcapairs_bymoose(myloadings = ex_data, 
    varexp = rep(1/ncol(ex_data), ncol(ex_data)),
    pcol = "tomato" )

perform metabolomics quality control

Description

This function is a wrapper function that performs the key quality controls steps on a metabolomics data set

Usage

perform.metabolite.qc(
  wdata,
  fmis = 0.2,
  smis = 0.2,
  tpa_out_SD = 5,
  outlier_udist = 5,
  outlier_treatment = "leave_be",
  winsorize_quantile = 1,
  tree_cut_height = 0.5,
  PC_out_SD = 5,
  feature_colnames_2_exclude = NA,
  derived_colnames_2_exclude = NA
)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
fmis

defaulted at 0.2, this defines the feature missingness cutoff
smis

defaulted at 0.2, this defines the sample missingness cutoff
tpa_out_SD

defaulted at 5, this defines the number of standard deviation from the mean in which samples will be excluded for total peak area. Pass NA to this paramater to exclude this step.
outlier_udist

defaulted at 5, this defines the number of interquartile range units from the median to define a value as an outlier
outlier_treatment

defaulted to "leave_be". Choices are "leave_be", "winsorize", or "turn_NA", which defines how to treat outlier values prior to estimating principal components. "leave_be" will do nothing to ourlier values. "turn_NA" will turn outliers into NA and thus be median imputed for the purpose of the PCA. "winsorize" will turn NA values into the "winsorize_quantile" of all remaining (non-outlying) values at a feature.
winsorize_quantile

the quantile (0-1) to winzorise outlier values to, if "outlier_treatment" parameter set to "winsorize". Defaulted to 1, or the maximum value of all remaining (non-outlying) values at a feature.
tree_cut_height

The height at which to cut the feature|metabolite dendrogram to identify "independent" features. tree_cut_height is 1-absolute(Spearman's Rho) for intra-cluster correlations.
PC_out_SD

defaulted at '5', this defines the number of standard deviation from the mean in which samples will be excluded for principle components. NA is NOT an excepted paramater.
feature_colnames_2_exclude

names of columns to be excluded from all analysis, such as for Xenobiotics. Pass NA to this parameter to exclude this step.
derived_colnames_2_exclude

names of columns to exclude from all sample QC steps, including independent feature identification, which is used to generate the sample PCA.

Value

a list object of: (1) "wdata" qc'd data matrix, (2) "featuresumstats" a list with a (1:"table") data frame of feature summary statistics and a (2:"tree") hclust object (3) "pca" a list with a (1:"pcs") data frame of the top 10 PCs and a binary for outliers on the top 2 PCs at 3,4, and 5 SD from the mean, a (2:"varexp") vector of the variance explained for each PC, an estimate of the number of 'significant' PCs as determined by (3:"accelerationfactor") an acceleration factor and (4:"nsig_parrallel") a parrallel analysis, and finally (5:"prob_pca") the top 10 PCs derived by a probabilistic PC analysis. (4) "exclusion_data" a matrix of exclusion summary statistics

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
## define the data set
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 450)] = NA
## add in some technical error to two samples
m = apply(ex_data, 2, function(x){ mean(x, na.rm = TRUE) })
ex_data[c(1,10), ] = ex_data[1, ] + (m*0.00001) 

## run the quality control
example_qc = perform.metabolite.qc(ex_data)

## the filtered data is found in 
dim(example_qc$wdata)
example_qc$wdata[1:5, 1:5]
## a data frame of summary statistics
head( example_qc$featuresumstats$table )
## a hclust dendrogram can be plotted
plot( example_qc$featuresumstats$tree , hang = -1)
abline(h = 0.5, col = "red", lwd = 1.5)
## (median imputed) PCs for all samples can be plotted
pcol = c("blue","red")[ as.factor( example_qc$pca$pcs[, "PC1_5_SD_outlier"] )]
plot(example_qc$pca$pcs[,"PC1"], example_qc$pca$pcs[,"PC2"], 
     pch = 21, cex = 1.5, bg = pcol, 
     xlab = paste0( "PC1: Var Exp = " , round(example_qc$pca$varexp[1], d = 4)*100, "%" ) , 
     ylab = paste0( "PC2: Var Exp = " , round(example_qc$pca$varexp[2], d = 4)*100, "%" ) )
## A Scree plot can be generated by
plot( x = 1:length(example_qc$pca$varexp), 
      y = example_qc$pca$varexp, 
      type = "b", pch = 21, cex = 2, bg = "blue",
      xlab = "PC", ylab = "Variance Explained", main = "Scree Plot")
abline(v = example_qc$pca$accelerationfactor, col = "red", lwd = 2)
abline(v = example_qc$pca$nsig_parrallel, col = "green", lwd = 2)
## Probablistic PCs for all samples can be plotted
plot(example_qc$pca$prob_pca[,1], example_qc$pca$prob_pca[,2], 
     pch = 21, cex = 1.5, bg = pcol, 
     xlab = "PC1", 
     ylab = "PC2" )
## A summary of the exclusion statistics
example_qc$exclusion_data

read in Metabolon (v1) metabolomics data

Description

This function reads in a Metabolon (v1 format) raw data excel file, writes the (1) metabolite, (2) sample annotation, and (3) feature annotation data to flat text files. It also returns a list object of the same data.

Usage

read.in.metabolon(file2process, data_dir, projectname)

Arguments

file2process

the name of the xls file to process
data_dir

the full path to the directory holding your Metabolon excel file
projectname

a name for your project

Value

a list object of (1) metabolite, (2) sample annotation, and (3) feature annotation data

Examples

# read.in.metabolon(file2process = "Metabolon_data_release.xls", 
#  data_dir = "/File/sits/here/", 
#  projectname = "My Amazing Project")

read in Nightingale Health metabolomics data

Description

This function reads in a Nightingale raw data excel file, writes the (1) metabolite, (2) sample annotation, and (3) feature annotation data to flat text files. It also returns a list object of the same data.

Usage

read.in.nightingale(file2process, data_dir, projectname)

Arguments

file2process

the name of the xls file to process
data_dir

the full path to the directory holding your Nightingale excel file
projectname

a name for your project

Value

a list object of (1) metabolite, (2) sample annotation, and (3) feature annotation data

Examples

# read.in.nightingale(file2process = "NH_data_release.xls", 
#  data_dir = "/File/sits/here/", 
#  projectname = "My Amazing Project")

rank normal tranformation

Description

This function rank normal transforms a vector of data. The procedure is built off of that provided in the GenABEL pacakge.

Usage

rntransform(y, split_ties = TRUE)

Arguments

y

a numeric vector which will be rank normal transformed
split_ties

a binary string of TRUE (default) or FALSE indicating if tied values, of the same rank, should be randomly split giving them unique ranks.

Value

returns a numeric vector, with the same length as y, of rank normal transformed values

Examples

## simulate a negative binomial distribution of values
nb_data = rnbinom(500, mu = 40, size = 100)
## rank normal transform those values
rnt_data = rntransform( nb_data , split_ties = TRUE )

continuous trait power analysis plot

Description

This function (1) identifies an informative distribution of effect and power estimates given your datas total sample size and (2) returns a summary plot.

Usage

run.cont.power.make.plot(mydata)

Arguments

mydata

Your metabolite data matrix, with samples in rows

Value

a ggplot2 object

Examples

ex_data = matrix(NA, 1000, 2)
run.cont.power.make.plot( ex_data )

binary trait imbalanced design power analysis plot

Description

This function (1) estimates an informative distribution of effect and power estimates given your datas total sample size, over a distribution of imbalanced sample sizes and (2) returns a summary plot.

Usage

run.pa.imbalanced.power.make.plot(mydata)

Arguments

mydata

a numeric data matrix with samples in rows and features in columns

Value

a ggplot2 object

Examples

ex_data = matrix(NA, 1000, 2)
run.pa.imbalanced.power.make.plot( ex_data )

sample exlusions on missingness and total peak area

Description

This function provides missingnes and tpa estimates along with exlcusion at 3, 4, and 5 SD from the mean.

Usage

sam.missingness.exclusion(mydata, sdata, fdata)

Arguments

mydata

metabolite data
sdata

sample data
fdata

feature data

Value

a data frame of missingness and TPA exclusions

Examples

# sam.missingness.exclusion()

estimate sample missingness

Description

This function estimates sample missingness in a matrix of data and provides an option to exclude certain columns or features from the analysis, such as xenobiotics (with high missingness rates) in metabolomics data sets.

Usage

sample.missingness(wdata, excludethesefeatures = NA)

Arguments

wdata

a numeric matrix with samples in row and features in columns
excludethesefeatures

a vector of feature names (i.e. column names) to exclude from missingness estimates

Value

A data frame of missingness estimates for each sample. If a vector of feature names was also passed to the function a second column of missingness estimates will also be returned providing missingness estimates for each sample to the exclusion of those features provided.

Examples

## simulate some data
set.seed(1110)
ex_data = sapply(1:5, function(x){ rnorm(10, 40, 5) })
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add some missingness to the data
ex_data[ sample(1:50, 10) ] = NA
## estimate missingness
mis_est = sample.missingness(ex_data)
mis_est_v2 = sample.missingness(ex_data, excludethesefeatures = "var5")

outlier features count for samples

Description

This function takes a matrix of numeric data and counts the number of outlying features each sample has.

Usage

sample.outliers(wdata, nsd = 5)

Arguments

wdata

a metabolite data matrix with samples in row, metabolites in columns
nsd

the number of standard deviation from the mean outliers are identified at. The default value is 5.

Value

a data frame of outiler counts for each sample

Examples

d = sapply(1:5, function(x){ rnorm(50, 50, 15) })
sample.outliers(d, nsd = 2)

summary statistics for samples

Description

This function estimates summary statistics for samples in a matrix of numeric features. This includes missingness, total peak area, and a count of the number of outlying features for a sample.

Usage

sample.sum.stats(wdata, feature_names_2_exclude = NA, outlier_udist = 5)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
feature_names_2_exclude

a vector of feature|column names to exclude from missingness estimates
outlier_udist

the interquartile range unit distance from the median to call a sample an outlier at a feature.

Value

a data frame of summary statistics

Examples

## simulate some data
set.seed(1110)
ex_data = sapply(1:5, function(x){ rnorm(10, 40, 5) })
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add some missingness to the data
ex_data[ sample(1:50, 10) ] = NA
## run estimate sample summary statistics
sample.sum.stats(ex_data)

estimates total peak abundance

Description

This function estimates total peak abundance|area for numeric data in a matrix, for (1) all features and (2) all features with complete data.

Usage

total.peak.area(wdata, feature_names_2_exclude = NA, ztransform = TRUE)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
feature_names_2_exclude

A vector of feature|metabolite names to exclude from the tree building, independent feature identification process.
ztransform

should the feature data be z-transformed and absolute value minimum, mean shifted prior to summing the feature values. TRUE or FALSE.

Value

a data frame of estimates for (1) total peak abundance and (2) total peak abundance at complete features for each samples

Examples

set.seed(1110)
ex_data = sapply(1:5, function(x){ rnorm(10, 40, 5) })
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
ex_data[ sample(1:50, 4) ] = NA
tpa_est = total.peak.area(ex_data)

identify independent features in a numeric matrix

Description

This function identifies independent features using Spearman's rho correlation distances, and a dendrogram tree cut step.

Usage

tree_and_independent_features(
  wdata,
  minimum_samplesize = 50,
  tree_cut_height = 0.5,
  feature_names_2_exclude = NA
)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns
minimum_samplesize

the metabolite data matrix. samples in row, metabolites in columns
tree_cut_height

the tree cut height. A value of 0.2 (1-Spearman's rho) is equivalent to saying that features with a rho >= 0.8 are NOT independent.
feature_names_2_exclude

A vector of feature|metabolite names to exclude from this analysis. This might be features heavily present|absent like Xenobiotics or variables derived from two or more variable already in the dataset.

Value

a list object of (1) an hclust object, (2) independent features, (3) a data frame of feature ids, k-cluster identifiers, and a binary identifier of independent features

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.7, 0.4, 0.2)
cmat[2,] = c(0.7, 1, 0.2, 0.05)
cmat[3,] = c(0.4, 0.2, 1, 0.375)
cmat[4,] = c(0.2, 0.05, 0.375,1)

## simulate the data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 500, mu = c(5, 45, 25, 15), Sigma = cmat )
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))

## run function to identify independent variables at a tree cut height
## of 0.5 which is equivalent to clustering variables with a Spearman's
## rho > 0.5 or (1 - tree_cut_height)
ind = tree_and_independent_features(ex_data, tree_cut_height = 0.5)

ggplot2 violin plot

Description

This function performs univariate linear analysis of a dependent and an independent variable and generates a viloin or box plot to illustrate the associated structure.

Usage

variable.by.factor(
  dep,
  indep,
  dep_name = "dependent",
  indep_name = "independent",
  orderfactor = TRUE,
  violin = TRUE
)

Arguments

dep

a vector of the dependent variable
indep

a vector of the independent variable
dep_name

name of the dependent variable
indep_name

name of the independent variable
orderfactor

order factors alphebetically
violin

box plot or violin plot. violin = TRUE is default

Value

a ggplot2 object

Examples

x = c( rnorm(20, 10, 2), rnorm(20, 20, 2) )
y = as.factor( c( rep("A", 20), rep("B", 20)  ) )
variable.by.factor(dep = x , indep = y, dep_name = "expression", indep_name = "species" )