RNA Seq Normalization

mhg-cipf edited this page Jan 28, 2015 · 3 revisions
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INPUT

  • Matrix of RNA-Seq raw counts, upload as a Data matrix Expression data type. The matrix should include a tag indicating the names of the columns.
  • Optionally, the length of the genes or transcripts can be also included, upload as ID list ranked data type. For more information in data types please visit Data types.


STEPS

  1. Examples: Prepared example data for testing the tool. For loading the example, click on Normalization example and then on the Launch job button at the bottom of the page.
  2. Select your data: Choose a data set of raw counts (integer numbers) among the data sets you have already upload to your personal user folder.
  3. Select gene length file: If desired, select your data file containing the length of the genetic features. Data type should have been upload as ID list ranked.
  4. Normalization method: Choose normalization method. Babelomics can choose the best normalization method for your particular data if you select the option Choose automatically the normalization method. For further information about normalization methods please see Preprocessing for RNA Seq.
  5. Job information: Give information about the job you are creating.
    • Select the output folder. Babelomics will create a new folder for the job inside the specified folder.
    • Choose job name and specify a description for the job if desired.
  6. Press the Launch job button.


OUTPUT

  • Job information: Gives information about the job.
  • Input parameters: Gives information about the parameters used as input.
  • RNA composition graphics: Only displayed when the length of the genetic features is available. RNA composition plots of the data set before and after the normalization process is applied. These plots give information about the RNA composition bias. For more information on the plots please refer to the NOISeq package user's guide
  • Boxplots of expression values: Boxplot displaying the distribution of the expression values before and after the normalization process is applied. Each box represents a sample and the expression values are represented in logarithmic scale.
  • Normalized data results: Matrix of the normalized expression values for each genetic feature and sample. It has the same dimension as the input data matrix. The header includes information about the normalization method applied. File normalized_results.txt include this information can be downloaded from here.