Mercurial > repos > kpbioteam > ewastools

comparison minfi_analysis.xml @ 82:369fef559cfc draft default tip

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"planemo upload for repository https://github.com/kpbioteam/ewas_galaxy commit 9363395728213b6d82e606c5513709c54af4df09"
author kpbioteam
date Sun, 23 Feb 2020 17:00:42 -0500
parents 8ab24a5229bd
children
comparison
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81:8ab24a5229bd 82:369fef559cfc
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41 41
42 if ('$optpp' == 'na' ) { 42 if ('$optpp' == 'na' ) {
43 GRSet <- mapToGenome(RSet) #mapping Ilumina methylation array data to the genome 43 GRSet <- mapToGenome(RSet) #mapping Ilumina methylation array data to the genome
44 } else if ('$optpp' == 'ppfun' ) { 44 } else if ('$optpp' == 'ppfun' ) {
45 GRSet <- preprocessFunnorm(RGSet) #optional - implements the functional normalization algorithm 45 GRSet <- preprocessFunnorm(RGSet) #optional - implements the functional normalisation algorithm
46 } else if ('$optpp' == 'ppq' ) { 46 } else if ('$optpp' == 'ppq' ) {
47 GRSet <- preprocessQuantile(RGSet, fixOutliers = TRUE, 47 GRSet <- preprocessQuantile(RGSet, fixOutliers = TRUE,
48 removeBadSamples = TRUE, badSampleCutoff = 10.5, 48 removeBadSamples = TRUE, badSampleCutoff = 10.5,
49 quantileNormalize = TRUE, stratified = TRUE, 49 quantileNormalize = TRUE, stratified = TRUE,
50 mergeManifest = FALSE, sex = NULL) #optional - implements stratified quantile normalization preprocessing 50 mergeManifest = FALSE, sex = NULL) #optional - implements stratified quantile normalisation preprocessing
51 } else if ('$optpp' == 'ppsnp' ) { 51 } else if ('$optpp' == 'ppsnp' ) {
52 snps <- getSnpInfo(GRSet) #optional - retrieve the chromosome and the position of each SNP 52 snps <- getSnpInfo(GRSet) #optional - retrieve the chromosome and the position of each SNP
53 write.table(snps, '$table') 53 write.table(snps, '$table')
54 GRSet <- dropLociWithSnps(GRSet, snps=c('SBE','CpG'), maf=0) #optional - drop the probes that contain either a SNP at the CpG interrogation or at the single nucleotide extensions 54 GRSet <- dropLociWithSnps(GRSet, snps=c('SBE','CpG'), maf=0) #optional - drop the probes that contain either an SNP at the CpG interrogation or at the single nucleotide extensions
55 } 55 }
56 pheno <- read.table('$phenotype_table',skip = 1) 56 pheno <- read.table('$phenotype_table',skip = 1)
57 group <- pheno\$V2 57 group <- pheno\$V2
58 pair <- factor(pheno\$V3) 58 pair <- factor(pheno\$V3)
59 59
152 152
153 .. class:: infomark 153 .. class:: infomark
154 154
155 **What it does** 155 **What it does**
156 156
157 The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP)and regions (DMR) with respect to a phenotype covariate. 157 The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including the highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP)and regions (DMR) with respect to a phenotype covariate.
158 158
159 ***Inputs*** 159 ***Inputs***
160 160
161 *Series of .IDAT files*: red and green .idat file for each sample on the chip intensity data. 161 *Series of .IDAT files*: matching red and green .idat file for each sample on the chip intensity data.
162 162
163 *(optional) Preprocessing Methods*: by this step probes can be stratified by region via quantile normalization or by extended implementation of functional normalisation recommended for cases where global changes are expected such as in cancer-normal comparisons. In addition unwanted probes containing either a SNP at the CpG interrogation or at the single nucleotide extension can be removed (recommended). 163 *(optional) Preprocessing Methods*: by this step probes can be stratified by region via quantile normalisation or by extended implementation of functional normalisation recommended for cases where global changes are expected such as in cancer-normal comparisons. In addition unwanted probes containing either an SNP at the CpG interrogation or at the single nucleotide extension can be removed (recommended).
164 164
165 *Phenotype Table*: table of compared samples and their characteristics, may be categorical (e.g. cancer vs. normal) or continuous (e.g. blood pressure). 165 *Phenotype Table*: table of compared samples and their characteristics, may be categorical (e.g. cancer vs. normal) or continuous (e.g. blood pressure).
166 166
167 ========== ============== =============== 167 ========== ============== ===============
168 Accession Sensitivity Treatment 168 Accession Sensitivity Treatment