Question: Which file can be parsed into caArray? What is the benefit of file parsing?
Topic: caArray Usage
Release: caArray 2.2.0 and above
Date entered: 03/30/2009
Files Recognized by caArray
caArray has the ability to upload the array design file or experiment data from many array providers, even if it doesn't have a parser available yet. Those files will be imported into caArray without being validated or parsed. Even if a file is not parsed, the user will still be able to download the file (through the user interface as well as through the programmatic API), and will be able to associate the file to samples, extracts, and hybridizations. This feature allows data to be shared and help the system identify which new parsers are need developed in the future. For more information on how files are processed by caArray, review caArray 017 - Meaning of the caArray Status of Importing - Imported versus Imported Not Parsed.
The following table, File types from caArray, (from Chapter 7 in the caArray User Guide) summarizes the file types that caArray currently supports with full validation and parsing as well as those that can be imported without validation and parsing. The user's guide also summarizes the array design file types.
Files That Can Be Imported into caArray
Imported after validation and parsing
Imported without validation and parsing
Raw/processed data files;
Array Design files;
For information about array design file types, see the table in Managing Array Designs.
These cannot be validated nor imported. Files of unknown file type or simply reference files fall under this category. For more information, see #Supplemental Files.
Benefit of File Parsing
For the data that are parsed into caArray, an analytical service (like geWorkbench) can pull the data out using the programmatic API and perform analysis on it or plot graphs from it etc. Another example is web Genome, a caArray client, which pulls parsed data from caArray experiments and plots log ratio values against the chromosome location. With parsed data, a client can ask for quantitative types (columns) of data of interest, instead of having to retrieve the entire contents of the data file.
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