PomaEDA: Automatic Exploratory Data Analysis PDF Report

In POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data

Description

This function automatically generates a PDF report with different exploratory plots and tables from an MSnSet object.

Usage

PomaEDA(
  data,
  imputation = "knn",
  normalization = "log_pareto",
  clean_outliers = TRUE,
  coeff_outliers = 1.5,
  username = "Username"
)

Arguments

data	A MSnSet object. First pData column must be the subject group/type.
imputation	Imputation method. Options are "none", "half_min", "median", "mean", "min" and "knn" (default). If "none", all missing values will be replaced by zero.
normalization	Normalization method. Options are "none", "auto_scaling", "level_scaling", "log_scaling", "log_transformation", "vast_scaling" and "log_pareto" (default).
clean_outliers	Logical. If it's set to TRUE, outliers will be removed from EDA.
coeff_outliers	This value corresponds to the classical 1.5 in Q3 + 1.5*IQR formula to detect outliers. By changing this value, the permissiveness in outlier detection will change.
username	This name will be included as a report subtitle.

Value

An exploratory data analysis PDF report.

Author(s)

Pol Castellano-Escuder

POMA documentation built on Nov. 8, 2020, 6:26 p.m.