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# Start the multiblock R package
library(multiblock)
#> Registered S3 method overwritten by 'plsVarSel':
#> method from
#> print.mvrVal pls
#> Registered S3 methods overwritten by 'multiblock':
#> method from
#> print.multiblock ade4
#> summary.multiblock ade4
#>
#> Attaching package: 'multiblock'
#> The following object is masked from 'package:stats':
#>
#> loadingsData are stored in many different file formats. The following three examples cover two types of CSV-files and generic flat files.
# Find directory extdata from the multiblock package
mbdir <- system.file('extdata/', package = "multiblock")
# Comma separated values, row names in first column
meta_data <- read.csv(paste0(mbdir, "/meta_data.csv"), row.names = 1)
# If working directory matches file location:
# meta_data <- read.csv('meta_data.csv', row.names = 1)
meta_data
#> temperature colour
#> John 38.0 blue
#> Julia 37.0 green
#> James 37.5 blue
#> Jacob 37.6 red
#> Jane 37.2 red
#> Johanna 37.9 green
# Semi-colon separated values (locales where the decimal point is comma),
# no row names
proteins <- read.csv2(paste0(mbdir, "/proteins.csv"))
proteins
#> prot1 prot2 prot3
#> 1 0.46532048 0.30183300 -1.4654414
#> 2 -1.79802081 -0.22812232 -0.4639203
#> 3 -1.92962434 -0.40513080 0.1767796
#> 4 0.87437138 0.79843798 0.1234731
#> 5 -0.62445278 -0.07975479 -1.1126332
#> 6 -0.07493721 1.09576027 1.2656596
# Blank space separated data without labels
genes <- read.table(paste0(mbdir, "/genes.dat"))
genes
#> V1 V2 V3
#> 1 0.39033106 -0.5720390 1.9147573
#> 2 0.55352785 0.0948703 -0.2239755
#> 3 0.09872346 -0.1029385 0.9047138
#> 4 -0.59213740 -0.6027739 0.6177083
#> 5 -0.02350148 0.3572809 -0.5168416
#> 6 0.76644845 1.2863428 1.8239298Before analysis, various types of pre-processing may be needed. Centring and standardising/scaling may be considered the most basic. In R, these operations are performed column-wise by default, leading to autoscaling. If these operations are performed on the rows, we perform the standard normal variate (SNV) instead.
# Column-centring
genes_centred <- scale(genes, scale=FALSE)
colMeans(genes_centred) # Check mean values
#> V1 V2 V3
#> 1.850372e-17 0.000000e+00 7.401487e-17
# Autoscaling
genes_scaled <- scale(genes)
apply(genes_scaled, 2, sd) # Check standard deviations
#> V1 V2 V3
#> 1 1 1
# SNV (transpose, autoscale, re-transpose)
genes_snv <- t(scale(t(genes)))
apply(genes_snv, 1, sd) # Check standard deviations
#> [1] 1 1 1 1 1 1Most analysis methods require continuous input data. The meta_data data.frame contains a character vector (a factor in older R versions) of categories. This package has a function dummycode for converting categorical data to various dummy formats.
# Default is sum coding
dummycode(meta_data$colour)
#> x1 x2
#> 1 1 0
#> 2 0 1
#> 3 1 0
#> 4 -1 -1
#> 5 -1 -1
#> 6 0 1
# Treatment coding
dummycode(meta_data$colour, "contr.treatment")
#> xgreen xred
#> 1 0 0
#> 2 1 0
#> 3 0 0
#> 4 0 1
#> 5 0 1
#> 6 1 0
# Full dummy-coding (rank deficient)
dummycode(meta_data$colour, drop = FALSE)
#> xblue xgreen xred
#> 1 1 0 0
#> 2 0 1 0
#> 3 1 0 0
#> 4 0 0 1
#> 5 0 0 1
#> 6 0 1 0
# Replace categorical with dummy-coded, use I() to index by common name
meta_data2 <- meta_data
meta_data2$colour <- I(dummycode(meta_data$colour, drop = FALSE))
meta_data2
#> temperature colour.xblue colour.xgreen colour.xred
#> John 38.0 1 0 0
#> Julia 37.0 0 1 0
#> James 37.5 1 0 0
#> Jacob 37.6 0 0 1
#> Jane 37.2 0 0 1
#> Johanna 37.9 0 1 0
meta_data2$colour
#> xblue xgreen xred
#> 1 1 0 0
#> 2 0 1 0
#> 3 1 0 0
#> 4 0 0 1
#> 5 0 0 1
#> 6 0 1 0A simple list of blocks can be created using the list() function. Naming of the blocks can be done directly or after creation.
# Direct approach
blocks1 <- list(meta = meta_data2, proteins = proteins, genes = genes)
# Two-step approach
blocks2 <- list(meta_data2, proteins, genes)
names(blocks2) <- c('meta', 'proteins', 'genes')
# Same result
identical(blocks1, blocks2)
#> [1] TRUE
# Access by name or number
blocks1[['meta']]
#> temperature colour.xblue colour.xgreen colour.xred
#> John 38.0 1 0 0
#> Julia 37.0 0 1 0
#> James 37.5 1 0 0
#> Jacob 37.6 0 0 1
#> Jane 37.2 0 0 1
#> Johanna 37.9 0 1 0
blocks2[[1]]
#> temperature colour.xblue colour.xgreen colour.xred
#> John 38.0 1 0 0
#> Julia 37.0 0 1 0
#> James 37.5 1 0 0
#> Jacob 37.6 0 0 1
#> Jane 37.2 0 0 1
#> Johanna 37.9 0 1 0A data.frame is a convenient storage format for data in R and can handle many types of variables, e.g. numeric, logical, character, factor or matrices. The latter is useful for analyses of data with shared sample mode.
# Construct block data.frame from list
df1 <- block.data.frame(blocks1)
# Construct block data.frame from data.frame:
# First merge blocks into data.frame
my_data <- cbind(meta_data2, proteins, genes)
# Then construct block data.frame using named
# list of indexes
df2 <- block.data.frame(my_data, block_inds =
list(meta = 1:2, proteins = 3:5, genes = 6:8))
# Same result
identical(df1,df2)
#> [1] TRUE
# Access by name or number
df1[[2]]
#> prot1 prot2 prot3
#> John 0.46532048 0.30183300 -1.4654414
#> Julia -1.79802081 -0.22812232 -0.4639203
#> James -1.92962434 -0.40513080 0.1767796
#> Jacob 0.87437138 0.79843798 0.1234731
#> Jane -0.62445278 -0.07975479 -1.1126332
#> Johanna -0.07493721 1.09576027 1.2656596
df2[['proteins']]
#> prot1 prot2 prot3
#> John 0.46532048 0.30183300 -1.4654414
#> Julia -1.79802081 -0.22812232 -0.4639203
#> James -1.92962434 -0.40513080 0.1767796
#> Jacob 0.87437138 0.79843798 0.1234731
#> Jane -0.62445278 -0.07975479 -1.1126332
#> Johanna -0.07493721 1.09576027 1.2656596
df1[c(1,3)]
#> meta.temperature meta.colour.xblue meta.colour.xgreen meta.colour.xred
#> John 38.0 1.0 0.0 0.0
#> Julia 37.0 0.0 1.0 0.0
#> James 37.5 1.0 0.0 0.0
#> Jacob 37.6 0.0 0.0 1.0
#> Jane 37.2 0.0 0.0 1.0
#> Johanna 37.9 0.0 1.0 0.0
#> genes.V1 genes.V2 genes.V3
#> John 0.39033106 -0.57203901 1.91475732
#> Julia 0.55352785 0.09487030 -0.22397553
#> James 0.09872346 -0.10293847 0.90471382
#> Jacob -0.59213740 -0.60277392 0.61770832
#> Jane -0.02350148 0.35728086 -0.51684156
#> Johanna 0.76644845 1.28634283 1.82392983
df1[-2]
#> meta.temperature meta.colour.xblue meta.colour.xgreen meta.colour.xred
#> John 38.0 1.0 0.0 0.0
#> Julia 37.0 0.0 1.0 0.0
#> James 37.5 1.0 0.0 0.0
#> Jacob 37.6 0.0 0.0 1.0
#> Jane 37.2 0.0 0.0 1.0
#> Johanna 37.9 0.0 1.0 0.0
#> genes.V1 genes.V2 genes.V3
#> John 0.39033106 -0.57203901 1.91475732
#> Julia 0.55352785 0.09487030 -0.22397553
#> James 0.09872346 -0.10293847 0.90471382
#> Jacob -0.59213740 -0.60277392 0.61770832
#> Jane -0.02350148 0.35728086 -0.51684156
#> Johanna 0.76644845 1.28634283 1.82392983
df2[c('proteins','genes')]
#> proteins.prot1 proteins.prot2 proteins.prot3 genes.V1 genes.V2
#> John 0.46532048 0.30183300 -1.46544136 0.39033106 -0.57203901
#> Julia -1.79802081 -0.22812232 -0.46392027 0.55352785 0.09487030
#> James -1.92962434 -0.40513080 0.17677963 0.09872346 -0.10293847
#> Jacob 0.87437138 0.79843798 0.12347308 -0.59213740 -0.60277392
#> Jane -0.62445278 -0.07975479 -1.11263323 -0.02350148 0.35728086
#> Johanna -0.07493721 1.09576027 1.26565956 0.76644845 1.28634283
#> genes.V3
#> John 1.91475732
#> Julia -0.22397553
#> James 0.90471382
#> Jacob 0.61770832
#> Jane -0.51684156
#> Johanna 1.82392983
# Use with formula interface (see other vignettes)
# sopls(meta ~ proteins + genes, data = df1)
# Use with single list interface (see other vignettes)
# mfa(df1[c(1,3)], ncomp = 3)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.