As a list

The most basic way to import data from perseus into r is as a list whose elements can then be used in any sort of workflow.

library(PerseusR)

dataFolder <- system.file('extdata', package = 'PerseusR')
dataFiles <- list.files(dataFolder, 
                        pattern = "matrix[[:digit:]]*.txt",
                        full.names=TRUE)
dataFile <- dataFiles[[1]]

default_output <- read.perseus.default(dataFile)
class(default_output)
#> [1] "list"
print(default_output)
#> $main
#>         Column_1   Column_2   Column_3
#> Row.1 -1.8605740 -0.3910594  0.2870352
#> Row.2 -1.0443650  1.4728070 -0.5056471
#> Row.3        NaN -0.4742951  0.8499980
#> Row.4  0.4721563  0.6996973 -0.6752246
#> Row.5  0.7463229 -0.1144230  2.2125310
#> 
#> $annotCols
#>       GO_Process  Name
#> Row.1            Row 1
#> Row.2            Row 2
#> Row.3            Row 3
#> Row.4            Row 4
#> Row.5            Row 5
#> 
#> $annotRows
#>          Grouping
#> Column_1   Group1
#> Column_2   Group2
#> Column_3   Group3
#> 
#> $description
#> [1] ""     ""     ""     ""     "Name"
#> 
#> $imputeData
#>   Column_1 Column_2 Column_3
#> 1    False    False    False
#> 2    False    False    False
#> 3    False    False    False
#> 4    False    False    False
#> 5    False    False    False
#> 
#> $qualityData
#>   Column_1 Column_2 Column_3
#> 1        0        0        0
#> 2        0        0        0
#> 3        0        0        0
#> 4        0        0        0
#> 5        0        0        0

As a MatrixData object

The custom MatrixData object is the most faithfull representation of a perseus matrix in R and is an internal class used by this package to validate whether the fields will be compatible with perseus in additional workflows.

dm_out <- read.perseus.as.matrixData(dataFile)
class(dm_out)
#> [1] "matrixData"
#> attr(,"package")
#> [1] "PerseusR"
print(dm_out)
#> An object of class "matrixData"
#> Slot "main":
#>         Column_1   Column_2   Column_3
#> Row.1 -1.8605740 -0.3910594  0.2870352
#> Row.2 -1.0443650  1.4728070 -0.5056471
#> Row.3        NaN -0.4742951  0.8499980
#> Row.4  0.4721563  0.6996973 -0.6752246
#> Row.5  0.7463229 -0.1144230  2.2125310
#> 
#> Slot "annotCols":
#>       GO_Process  Name
#> Row.1            Row 1
#> Row.2            Row 2
#> Row.3            Row 3
#> Row.4            Row 4
#> Row.5            Row 5
#> 
#> Slot "annotRows":
#>          Grouping
#> Column_1   Group1
#> Column_2   Group2
#> Column_3   Group3
#> 
#> Slot "description":
#> [1] ""     ""     ""     ""     "Name"
#> 
#> Slot "imputeData":
#>   Column_1 Column_2 Column_3
#> 1    False    False    False
#> 2    False    False    False
#> 3    False    False    False
#> 4    False    False    False
#> 5    False    False    False
#> 
#> Slot "qualityData":
#>   Column_1 Column_2 Column_3
#> 1        0        0        0
#> 2        0        0        0
#> 3        0        0        0
#> 4        0        0        0
#> 5        0        0        0

The data matrix object contains the slots that are supported by perseus and can be accessed by the methods of the object (not all of them must be present)

main(dm_out)
#>         Column_1   Column_2   Column_3
#> Row.1 -1.8605740 -0.3910594  0.2870352
#> Row.2 -1.0443650  1.4728070 -0.5056471
#> Row.3        NaN -0.4742951  0.8499980
#> Row.4  0.4721563  0.6996973 -0.6752246
#> Row.5  0.7463229 -0.1144230  2.2125310
annotRows(dm_out)
#>          Grouping
#> Column_1   Group1
#> Column_2   Group2
#> Column_3   Group3
annotCols(dm_out)
#>       GO_Process  Name
#> Row.1            Row 1
#> Row.2            Row 2
#> Row.3            Row 3
#> Row.4            Row 4
#> Row.5            Row 5
PerseusR::description(dm_out) # Biobase has a descr function as well...
#> [1] ""     ""     ""     ""     "Name"

As an expressionSet object (for bioconductor usage)

If you feel more confortable with the bioconductor expression set class, you can also import the data as such.

require(Biobase)
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#> 
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
#>     parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#>     as.data.frame, basename, cbind, colMeans, colSums, colnames,
#>     dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
#>     intersect, is.unsorted, lapply, lengths, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
#>     rowMeans, rowSums, rownames, sapply, setdiff, sort, table,
#>     tapply, union, unique, unsplit, which, which.max, which.min
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: 'Biobase'
#> The following objects are masked from 'package:PerseusR':
#> 
#>     description, description<-
eSet_out <- read.perseus.as.ExpressionSet(dataFile)
class(eSet_out)
#> [1] "ExpressionSet"
#> attr(,"package")
#> [1] "Biobase"
print(eSet_out)
#> ExpressionSet (storageMode: lockedEnvironment)
#> assayData: 5 features, 3 samples 
#>   element names: exprs 
#> protocolData: none
#> phenoData
#>   sampleNames: Column_1 Column_2 Column_3
#>   varLabels: Grouping
#>   varMetadata: labelDescription
#> featureData
#>   featureNames: Row.1 Row.2 ... Row.5 (5 total)
#>   fvarLabels: GO_Process Name
#>   fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation:     Name

eSet_out@annotation
#> [1] ""     ""     ""     ""     "Name"

and the equivalentes with the perseus functions would go like this:

1. The exprs slot in the expressionSet objects would be equivalent to the main data frame in perseus.
2. The featureData would be equivalent to the annotationCols.
3. The phenoData would be aquivalent to the annotationRows.
4. The Annotation would be equivalent to the descr.

MatrixData

This function would not provide much insight on this kind of object because it is run when constructing it; Therefore if the data is not compatible it would return an error when trying to create it

df <- matrixData(
    main = data.frame(a = 1:3, b = 6:8),
    annotCols = data.frame(c = c('a','b','c')),
    annotRows = data.frame(x = factor(c('1','1'))))

MatrixDataCheck(df)
#> [1] TRUE

my_error <- try({
  matrixData(
    main = data.frame(a = 1:3, b = 6:8, c = 1:3),
    annotCols = data.frame(c = c('a','b','c')),
    annotRows = data.frame(x = factor(c('1','1'))))
})

print(my_error)
#> [1] "Error in MatrixDataCheck.default(main = mainDF, annotationRows = annotationRows,  : \n  Size of annotation rows not matching: 3 main columns, but 2 annotations\n"
#> attr(,"class")
#> [1] "try-error"
#> attr(,"condition")
#> <simpleError in MatrixDataCheck.default(main = mainDF, annotationRows = annotationRows,     annotationCols = annotationCols, descriptions = descriptions,     imputeData = imputeData, qualityData = qualityData, all_colnames = all_colnames): Size of annotation rows not matching: 3 main columns, but 2 annotations>

lists

In this case this functions would be usefull, since the object is not inherently compatible.

my_list <- list(main = data.frame(a = 1:3, b = 6:8),
    annotCols = data.frame(c = c('a','b','c')),
    annotRows = data.frame(x = factor(c('1','1'))))

MatrixDataCheck(my_list)
#> [1] TRUE

my_list <- list(main = data.frame(a = 1:3, b = 6:8, c = 1:3),
    annotCols = data.frame(c = c('a','b','c')),
    annotRows = data.frame(x = factor(c('1','1'))))

my_error <- try({
  MatrixDataCheck(my_list)
})
print(my_error)
#> [1] "Error in MatrixDataCheck.default(main = object$main, annotationRows = object$annotRows,  : \n  Size of annotation rows not matching: 3 main columns, but 2 annotations\n"
#> attr(,"class")
#> [1] "try-error"
#> attr(,"condition")
#> <simpleError in MatrixDataCheck.default(main = object$main, annotationRows = object$annotRows,     annotationCols = object$annotCols, descriptions = object$descriptions,     imputeData = object$imputeData, qualityData = object$qualityData,     all_colnames = all_colnames): Size of annotation rows not matching: 3 main columns, but 2 annotations>

expressionSet objects

Single matrix Expression Sets will usually be compatible, since most of the restriction in matrix data objects also apply to expression set objects (regarding the dimensions and classes of the objects in each of the slots)

eSet <- Biobase::ExpressionSet(matrix(1:10, ncol = 2))

MatrixDataCheck(eSet)
#> [1] TRUE

MatrixData

# Here you can use any tipe of conection, similar to th base write... functions
# The usage should be fairly similar to the write.table function

tmp.file <- tempfile(fileext="txt")
write.perseus(dm_out, tmp.file)
#> NULL

# which would output somethint like this

cat(readLines(tmp.file), sep = '\n')
#> Column_1 Column_2    Column_3    GO_Process  Name
#> #!{Description}              Name
#> #!{Type}E    E   E   C   T
#> #!{C:Grouping}Group1 Group2  Group3      
#> -1.860574    -0.3910594  0.2870352       Row 1
#> -1.044365    1.472807    -0.5056471      Row 2
#> NaN  -0.4742951  0.849998        Row 3
#> 0.4721563    0.6996973   -0.6752246      Row 4
#> 0.7463229    -0.114423   2.212531        Row 5

data.frame

Data frames are converted in such manner that numeric columns are transfered as the main DF and non numerics as the annotation cols of the perseus DF

my_df <- data.frame(Con1 = 1:3, Con2 = 4:6, An1 = letters[1:3])

tmp.file <- tempfile(fileext="txt")
write.perseus(my_df, con = tmp.file)
#> NULL

cat(readLines(tmp.file), sep = '\n')
#> Con1 Con2    An1
#> #!{Type}E    E   C
#> 1    4   a
#> 2    5   b
#> 3    6   c

matrix

Numeric matrices can be outputed as well :D

my_matrix <- matrix(1:10, ncol = 2, 
                    dimnames = list(letters[11:15], letters[1:2]))
tmp.file <- tempfile(fileext="txt")
write.perseus(my_matrix, con = tmp.file)
#> NULL

cat(readLines(tmp.file), sep = '\n')
#> a    b   Names
#> #!{Type}E    E   C
#> 1    6   k
#> 2    7   l
#> 3    8   m
#> 4    9   n
#> 5    10  o

list

Lists are a little trickier, currently it looks for named elements that match the arguments, so it would use the elements named: main annotCols annotRows and descr

my_list <- list(main = data.frame(A = 1:5, B = 6:10),
                annotRows = data.frame(is_control = c(TRUE, FALSE)),
                annotCols = data.frame(Names = letters[1:5]),
                descr = c('something',
                          'something else',
                          'yet another thing'))
tmp.file <- tempfile(fileext="txt")
write.perseus(my_list, con = tmp.file)
#> NULL
cat(readLines(tmp.file), sep = '\n')
#> A    B   Names
#> #!{Description}something something else  yet another thing
#> #!{Type}E    E   C
#> #!{C:is_control}TRUE FALSE   
#> 1    6   a
#> 2    7   b
#> 3    8   c
#> 4    9   d
#> 5    10  e

expressionSet objects

eSet <- Biobase::ExpressionSet(matrix(1:10, ncol = 2))
tmp.file <- tempfile(fileext="txt")
write.perseus(eSet, con = tmp.file)
#> NULL
cat(readLines(tmp.file), sep = '\n')
#> X1   X2
#> #!{Type}E    E
#> 1    6
#> 2    7
#> 3    8
#> 4    9
#> 5    10

Adding elements to the output

Since not all data typer support allthe elements, one can specify them to the function and they will be passed over to the output.

my_matrix <- matrix(1:10, ncol = 2, 
                    dimnames = list(letters[11:15], letters[1:2]))
my_annotations_rows <- data.frame(My_Names = letters[1:2])
my_annotations_cols <- data.frame(My_Genes = letters[11:15])
tmp.file <- tempfile(fileext="txt")
write.perseus(my_matrix, con = tmp.file, 
              annotCols = my_annotations_cols,
              annotRows = my_annotations_rows)
#> NULL

cat(readLines(tmp.file), sep = '\n')
#> a    b   My_Genes
#> #!{Type}E    E   C
#> #!{C:My_Names}a  b   
#> 1    6   k
#> 2    7   l
#> 3    8   m
#> 4    9   n
#> 5    10  o

Removing elements form the output

Elements can be set to NULL to remove that section from the output

my_matrix <- matrix(1:10, ncol = 2, 
                    dimnames = list(letters[11:15], letters[1:2]))
tmp.file <- tempfile(fileext="txt")
write.perseus(my_matrix, con = tmp.file, 
              annotCols = NULL)
#> NULL

cat(readLines(tmp.file), sep = '\n')
#> a    b   Names
#> #!{Type}E    E   C
#> 1    6   k
#> 2    7   l
#> 3    8   m
#> 4    9   n
#> 5    10  o