## ---- eval=FALSE---------------------------------------------------------
# # Load MetaboAnalystR
# library(MetaboAnalystR)
#
## ---- eval=FALSE---------------------------------------------------------
# # Create objects for storing processed data
# mSet <- InitDataObjects("conc", "stat", FALSE)
#
# # Read in the data and fill in the dataSet list
#
# mSet <- Read.TextData(mSet, "http://www.metaboanalyst.ca/resources/data/human_cachexia.csv", "rowu", "disc")
#
# # To view messages from the data import and processing
# mSet$msgSet$read.msg
#
# # Example of messages
# [1] "Samples are in rows and features in columns"
# [2] "The uploaded file is in comma separated values (.csv) format."
# [3] "The uploaded data file contains 77 (samples) by 63 (compounds) data matrix."
## ---- eval=FALSE---------------------------------------------------------
# mSet <- InitDataObjects("pktable", "stat", FALSE)
#
# mSet <- Read.TextData(mSet, "http://www.metaboanalyst.ca/resources/data/NMRpeaklistskidney.csv", "rowu", "disc")
## ---- eval=FALSE---------------------------------------------------------
# # Create an object for storing processed data
# mSet <- InitDataObjects("mspeak", "stat", FALSE)
#
# # Unzips the uploaded zip file/s, removes it and saves it as "upload"
# UnzipUploadedFile("lcms_3col_peaks.zip", "upload", F)
#
# # Read peak lists/intensity files
# mSet <- Read.PeakList(mSet, "upload")
## ---- eval=FALSE---------------------------------------------------------
# # Perform grouping of peaks
# mSet <- GroupPeakList(mSet, 0.025, 30.0)
#
# # Form peak groups
# mSet <- SetPeakList.GroupValues(mSet)
#
# # View message resulting from peak grouping (Optional, though for your benefit)
# mSet$dataSet$proc.msg
#
## ---- eval=FALSE---------------------------------------------------------
#
# # Run the sanity check, it will return a series of messages if the data is suitable for subsequent analyses.
# mSet <- SanityCheckData(mSet)
#
# # [1] "Successfully passed sanity check!"
# # [2] "Samples are not paired."
# # [3] "2 groups were detected in samples."
# # [4] "Only English letters, numbers, underscore, hyphen and forward slash (/) are allowed."
# # [5] "<font color=\"orange\">Other special characters or punctuations (if any) will be stripped off.</font>"
# # [6] "All data values are numeric."
# # [7] "A total of 0 (0%) missing values were detected."
# # [8] "<u>By default, these values will be replaced by a small value.</u>"
# # [9] "Click <b>Skip</b> button if you accept the default practice"
# # [10] "Or click <b>Missing value imputation</b> to use other methods"
## ---- eval=FALSE---------------------------------------------------------
# # Replace missing/zero values with a minimum positive value
# mSet <- ReplaceMin(mSet)
#
# # View messages collected during ReplaceMin()
# mSet$msgSet$replace.msg
#
# # Example of message for replacing values
# [1] "Zero or missing variables were replaced with a small value: 0.395"
#
## ---- eval=FALSE---------------------------------------------------------
# # STEP 1: Remove features containing a user-defined % cut-off of missing values
# mSet <- RemoveMissingPercent(mSet, percent=0.5)
#
# # STEP 2: Remove variables with missing values
# mSet <- ImputeVar(mSet, method="exclude")
#
# ######### Alternative Step 2: Replace missing values with KNN imputed values
# mSet <- ImputeVar(mSet, method="knn")
## ---- eval=FALSE---------------------------------------------------------
# # Check if the sample size is too small, returns a 0 if the data passes the check
# mSet<-IsSmallSmplSize(mSet)
# [1] 0
## ---- eval=FALSE---------------------------------------------------------
# ### OPTION 1) Perform Probabilistic Quotient Normalization based upon a reference sample
# mSet<-PreparePrenormData(mSet)
# mSet<-Normalization(mSet, "ProbNormF", "NULL", "NULL", "PIF_178", ratio=FALSE, ratioNum=20)
#
# ### OPTION 2) Normalize by reference feature
# mSet<-PreparePrenormData(mSet)
# mSet<-Normalization(mSet, "CompNorm", "NULL", "NULL", "1,6-Anhydro-beta-D-glucose", ratio=FALSE, ratioNum=20)
#
# ### OPTION 3) Perform quantile normalization, log transformation, and mean-center scaling
# mSet<-PreparePrenormData(mSet)
# mSet<-Normalization(mSet, "QuantileNorm", "LogNorm", "MeanCenter", ref=NULL, ratio=FALSE, ratioNum=20)
## ---- eval=FALSE---------------------------------------------------------
# # View feature normalization
# mSet<-PlotNormSummary(mSet, "feature_norm", format="png", dpi=300, width=0)
#
# # View sample normalization
# mSet<-PlotSampleNormSummary(mSet, "sample_norm", format="pdf", width=NA)
#
## ---- eval=FALSE---------------------------------------------------------
# # Filter variables based on the median absolute deviation
# mSet <- FilterVariable(mSet, "mad", "F", 15)
#
# # Filter variables using QC-samples and a RDS threshold of 25
# mSet <- FilterVariable(mSet, "none", "T", 25)
## ---- eval=FALSE---------------------------------------------------------
# # Remove a sample from the data set, in this case sample "PIF_178"
# mSet <- UpdateSampleItems(mSet, "PIF_178")
#
# # Remove a feature from the data set
# mSet <- UpdateFeatureItems(mSet, "2-Aminobutyrate")
#
# # Remove a group from the data set, in this case remove the "control" samples
# mSet <- UpdateGroupItems(mSet, "control")
## ---- eval=FALSE---------------------------------------------------------
# # Create Biomarker Sweave report
# PreparePDFReport(mSet, "User Name")
#
# # To save all files created during your session
# SaveTransformedData(mSet)
#
