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Swiss knife of tabular data
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README.md

bio-table

Build Status

bio-table is the swiss knife of tabular data. Tables of data are often used in bioinformatics, especially in conjunction with Excel spreadsheets and DB queries. This biogem contains support for reading tables, writing tables, and manipulation of rows and columns, both using a command line interface and through a Ruby library. If you don't like R dataframes, maybe you like this. Also, because bio-table is command line driven, and can use STDIN and STDOUT, it easily fits in a pipe-line setup.

Quick example, say we want to filter out rows that contain certain p-values listed in the 4th column:

    bio-table test/data/input/table1.csv --num-filter "values[3] <= 0.05"

bio-table should be lazy. And be good for big data, bio-table is designed so that most important functions do not load the data in memory. The library supports a functional style of programming, but you don't need to know Ruby to use the command line interface (CLI).

Features:

  • Support for converting TAB and CSV files
  • Filter on data
  • Transform table and data by column or row
  • Recalculate data
  • Diff between tables, selecting on specific column values
  • Merge tables side by side on column value/rowname
  • Split/reduce tables by column
  • Read from STDIN, write to STDOUT
  • Convert table to RDF
  • Convert table to JSON (nyi)
  • etc. etc.

and bio-table is pretty fast. To convert a 3Mb file of 18670 rows takes 0.87 second. Adding a filter makes it parse at 0.95 second on my 3.2 GHz desktop (with preloaded disk cache).

Note: this software is under active development, though what is documented here should just work.

Installation

    gem install bio-table

The command line interface (CLI)

Transforming a table

Tables can be transformed through the command line. To transform a comma separated file to a tab delimited one

    bio-table test/data/input/table1.csv --in-format csv --format tab > test1.tab

Tab is actually the general default. Still, if the file name ends in csv, it will assume CSV. To convert the table back

    bio-table test1.tab --format csv > table1.csv

To filter out rows that contain certain values

    bio-table test/data/input/table1.csv --num-filter "values[3] <= 0.05" > test1a.tab

The filter ignores the header row, and the row names. If you need either, use the switches --with-header and --with-rownames. With math, list all rows

    bio-table test/data/input/table1.csv --num-filter "values[3]-values[6] >= 0.05" > test1a.tab

or, list all rows that have a least a field with values >= 1000.0

    bio-table test/data/input/table1.csv --num-filter "values.max >= 1000.0" > test1a.tab

Produce all rows that have at least 3 values above 3.0 and 1 one value above 10.0:

    bio-table test/data/input/table1.csv --num-filter "values.max >= 10.0 and values.count{|x| x>=3.0} > 3"

How is that for expressiveness? Looks like Ruby to me.

The --num-filter will convert fields lazily to numerical values (only valid numbers are converted). If there are NA (nil) values in the table, you may wish to remove them, like this

    bio-table test/data/input/table1.csv --num-filter "values[0..12].compact.max >= 1000.0" > test1a.tab

which takes the first 13 fields and compact removes the nil values.

Also string comparisons and regular expressions can be used. E.g. filter on rownames and a row field both containing 'BGT'

    # not yet implemented
    bio-table test/data/input/table1.csv --filter "rowname =~ /BGT/ and field[1] =~ /BGT/" > test1a.tab

To reorder/reduce table columns by name

    bio-table test/data/input/table1.csv --columns AJ,B6,Axb1,Axb4,AXB13,Axb15,Axb19 > test1a.tab

or use their index numbers (the first column is zero)

    bio-table test/data/input/table1.csv --columns 0,1,8,2,4,6 > test1a.tab

To filter for columns using a regular expression

    bio-table table1.csv --column-filter 'colname !~ /infected/i'

will drop all columns with names containing 'infected', ignoring case.

Finally we can rewrite the content of a table using rowname and fields again

    bio-table table1.csv --rewrite 'rowname.upcase!; field[1]=nil if field[2].to_f<0.25'

where we rewrite the rowname in capitals, and set the second field to empty if the third field is below 0.25.

Sorting a table

To sort a table on column 4 and 2

    # not yet implemented
    bio-table test/data/input/table1.csv --sort 4,2 > test1a.tab

Note: not all is implemented (just yet). Please check bio-table --help first.

Combining/merging tables

You can combine/concat two or more tables by passing in multiple file names

    bio-table test/data/input/table1.csv test/data/input/table2.csv

this will append table2 to table1, assuming they have the same headers (you can use the --columns switch!)

To combine tables side by side use the --merge switch:

    bio-table --merge table1.csv table2.csv

all rownames will be matched (i.e. the input table order do not need to be sorted). For non-matching rownames the fields will be filled with NA's, unless you add a filter, e.g.

    bio-table --merge table1.csv table2.csv --num-filter "values.compact.size == values.size"

Splitting a table

Splitting a table by column is possible by named or indexed columns, see the --columns switch.

Diffing and overlapping tables

With two tables it may be interesting to see the differences, or overlap, based on shared columns. The bio-table diff command shows the difference between two tables using the row names (i.e. those rows with rownames that appear in table2, but not in table1)

    bio-table --diff 0 table1.csv table2.csv 

bio-table --diff is different from the standard Unix diff tool. The latter shows insertions and deletions. bio-table --diff shows what is in one file, and not in the other (insertions). To see deletions, reverse the file order, i.e. switch the file names

    bio-table --diff 0 table1.csv table2.csv 

To diff on something else

    bio-table --diff 0,3 table2.csv table1.csv 

creates a key using columns 0 and 3 (0 is the rownames column).

Similarly

    bio-table --overlap 2 table1.csv table2.csv

finds the overlapping rows, based on the content of column 2.

Different parsers

more soon

Using STDIN

bio-table can read data from STDIN, by simply assuming that the data piped in is the first input file

    cat test1.tab | bio-table table1.csv --num-filter "values[3] <= 0.05" > test1a.tab

will filter both files test1.tab and test1.csv and output to test1a.tab.

Output table to RDF

bio-table can write a table into turtle RDF triples (part of the semantic web!), so you can put the data directly into a triple-store.

    bio-table --format rdf table1.csv

The table header is stored with predicate :colname using the header values both as subject and label, with the :index:

  :header3 rdf:label "Header3" ; a :colname; :index 4 .

Rows are stored with rowname as subject and label, followed by the columns referring to the header triples, and the values. E.g.

   :row13475701 rdf:label "row13475701" ; a :rowname ; ; :Id "row13475701" ; :header1 "1" ; :header2 "0" ; :header3 "3" .

To unify identifier names you may want to transform ids:

    bio-table --format rdf --transform-ids "downcase" table1.csv

Another interesting option is --blank-nodes. This causes rows to be written as blank nodes, and allows for duplicate row names. E.g.

   :row13475701 [ rdf:label "row13475701" ; a :rowname ; ; :Id "row13475701" ; :header1 "1" ; :header2 "0" ; :header3 "3" ] .

The bio-rdf gem actually uses this bio-table biogem to parse data into a triple store and query the data through SPARQL. For examples see the features, e.g. the genotype to RDF feature.

bio-table API (for Ruby programmers)

    require 'bio-table'
    include BioTable

Reading, transforming, and writing a table

Note: the Ruby API below is a work in progress.

Tables are two dimensional matrixes, which can be read from a file

    t = Table.read_file('test/data/input/table1.csv')
    p t.header              # print the header array
    p t.name[0],t[0]        # print the row name and row row
    p t[0][0]               # print the top corner field

The table reader has quite a few options for defining field separator, which column to use for names etc. More interestingly you can pass a function to limit the amount of row read into memory:

    t = Table.read_file('test/data/input/table1.csv',
      :by_row => { | row | row[0..3] } )

will create a table of the column name +row[0]+ and 2 table fields. You can use the same idea to reformat and reorder table columns when reading data into the table. E.g.

    t = Table.read_file('test/data/input/table1.csv',
      :by_row => { | row | [row.rowname, row[0..3], row[6].to_i].flatten } )

When a header can not be transformed, it may fail. You can test for the header with row.header?, but in this case you can pass in a :by_header, which will have :by_row only call on actual table rows.

    t = Table.read_file('test/data/input/table1.csv',
      :by_header => { | header | ["Row name", header[0..3], header[6]].flatten } )
      :by_row => { | row | [row.rowname, row[0..3], row[6].to_i].flatten } )

When by_row returns nil or false, the table row is skipped. One way to transform a file, and not loading it in memory, is

    f = File.new('test.tab','w')
    t = Table.read_file('test/data/input/table1.csv', 
      :by_row => { | row | 
        TableRow::write(f,[row.rowname,row[0..3],row[6].to_i].flatten, :separator => "\t") 
        nil   # don't create a table in memory, effectively a filter
      })

Another function is :filter which only acts on rows, but can not transform them.

To write a full table from memory to file use

    t.write_file('test1a.csv')

again columns can be reordered/transformed using a function. Another option is by passing in an list of column numbers or header names, so only those get written, e.g.

    t.write_file('test1a.csv', columns: [0,1,2,4,6,8])
    t.write_file('test1b.csv', columns: ["AJ","B6","Axb1","Axb4","AXB13","Axb15","Axb19"] )

other options are available for excluding row names (rownames: false), etc.

To sort a table file, the current routine is to load the file in memory and sort according to table columns. In the near future we aim to have a low-memory version, by reading only the sorting columns in memory, and indexing them before writing output. That means reading a file twice, but being able to handle much larger data.

In above examples we loaded the whole table in memory. It is also possible to execute functions without using RAM by using the emit function. This is what the bio-table CLI does to convert a CSV table to tab delimited:

ARGV.each do | fn |
  f = File.open(fn)
  writer = BioTable::TableWriter::Writer.new(format: :tab)
  BioTable::TableLoader.emit(f, in_format: :csv).each do |row,type| 
    writer.write(TableRow.new(row[0],row[1..-1]),type)
  end
end

Essentially you can pass in any object that has the each method to iterate through rows as String (f's each method reads in a line at a time). The emit function yields the parsed row object as a simple array of fields (each field a String). The type is used to distinguish the header row.

Loading a numerical matrix

Coming soon

More...

The API doc is online. For more code examples see the test files in the source tree.

Project home page

Information on the source tree, documentation, examples, issues and how to contribute, see

http://github.com/pjotrp/bioruby-table

The BioRuby community is on IRC server: irc.freenode.org, channel: #bioruby.

Cite

If you use this software, please cite one of

Biogems.info

This Biogem is published at #bio-table

Copyright

Copyright (c) 2012 Pjotr Prins. See LICENSE.txt for further details.

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