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+## 1. Metabolome
+
+The raw data from mass spectrometer was imported into commercial software Progenesis QI (version 2.2, hereinafter referred to as QI) for peak picking (https://www.nonlinear.com/progenesis/qi/), to obtain information of metabolites such as mass over charge, retention time and ion area. The QI workflow consists of the following steps: peak alignment, peak picking, and peak identification.
+
+The metabolite identification was performed by Progenesis QI by searching against HMDB (v5.0), METLIN (v3.7.1) and KEGG (v96.0) databases. 
+
+Pre-processing of peak data was performed using metaX (https://www.bioconductor.org/packages/3.2/bioc/html/metaX.html), the steps include: 
+
+- Filtering out low quality ions (first removed ions in QC sample that contain over 50% missing value, then removed ions in actual samples that contain over 80% missing value)
+- Using k-nearest neighbor (KNN) method for filling the missing values
+- Using probabilistic quotient normalization (PQN) method for data normalization
+- Using QC-RSC (Quality control-based robust LOESS signal correction) method to alleviate the effects of peak area attenuation
+- Filtering out ions in all QC samples which are RSD > 30% (the ions with RSD > 30% are fluctuate greatly in the experiment and will not be included in downstream statistical analysis)
+
+Taken the analysis of positive ion mode as example:
+
+```R
+library(metaX)
+para <- new("metaXpara")
+pfile <- "m_pos.csv" ## Output from QI, raw peak file with metabolite information
+sfile <- "s_pos.list" ## Output from QI, sample list file
+idres <- "i_pos.csv" ## Output from QI, ion intensity file
+para@outdir <- "metaX_result_pos"
+para@prefix <- "pos"
+para@sampleListFile <- sfile
+para@ratioPairs <- "COPD:Healthy"
+para <- importDataFromQI(para, file=pfile)
+plsdaPara <- new("plsDAPara")
+plsdaPara@scale = "pareto"
+plsdaPara@cpu = 4
+plsdaPara@kfold = 3
+#plsdaPara@do = FALSE
+res <- doQCRLSC(para, cpu=1)
+missValueImputeMethod(para)<-"KNN"
+p <- metaXpipe(para, plsdaPara=plsdaPara, missValueRatioQC=0.5, missValueRatioSample=0.8, cvFilter=0.3, idres=idres, qcsc=0, scale="pareto", remveOutlier=FALSE, nor.method="pqn", t=1, nor.order = 1, pclean = FALSE, doROC=FALSE)
+save(p, file="pos.rda")
+sessionInfo()
+```
+
+The processed metabolome data are uploaded as metabolome.txt
+
+The detailed information for each metabolite, including KEGG/HMDB/METLIN/PubChem/ChEBI IDs, SMILES structure, class and pathway is uploaded as compound_information.txt
+
+## 2. Sputum and serum proteome
+
+A panel of 280 proteins were measured using custom Quantibody Human Antibody Array (test procedure no. SOP-TF-QAH-001, SOP-TF-QAH-003 microarray) from RayBiotech (https://www.raybiotech.com/inflammation-protein-arrays/).
+
+The processed sputum and serum proteome data are uploaded as sputum_proteome.txt and serum_proteome.txt
+
+The detailed information of the 280 proteins is uploaded as protein_information.txt