Upload

Upload a CSV or tab-separated file of your RNA-Seq data. One row per gene, and one column of read counts per replicate.

Upload your CSV file. See below for the format description.

CSV File Format

You may upload a CSV of read counts per gene strong OR a CSV of pre-analysed gene data.

Uploading read counts per gene

After uploading your counts file, you'll be directed to a page to specify the columns. Ensure the checkbox strong Analyze server side is checked.

The requirements for the CSV file:

  • Must be in CSV {or tab-separated} format
  • Must have a single header row defining with a unique name for each column
  • Must have 2 or more replicates per condition, and 2 or more conditions
  • May optionally have information columns to be displayed in the gene table
  • May optionally have an EC Number column to display genes on Kegg pathways
Example CSV File
          Gene ID, name, control rep1, control rep2, treatment A rep1, treatment A rep2, EC Number
          gene001, flavodoxin, 60, 40, 200, 220, 3.1.-.-
          gene002, p53, 0, 4, 20, 30,
          gene003, potassium uptake protein, 600, 633, 200, 220, 2.7.8.-
Uploading pre-analysed data

After uploading your analysis file, you'll be directed to a page to specify the columns. Ensure the checkbox strong Analyze server side is not checked.

The requirements for CSV file:

  • Must be in CSV {or tab-separated} format
  • Must have a single header row defining with a unique name for each column
  • Must have 1 or more columns for log-fold-change
  • Must have 1 column for False Discovery Rate {or an equivalent}
  • Must have 1 column for log average expression {for the 'A' in an MA plot}
  • May optionally have information columns to be displayed in the gene table
  • May optionally have an EC Number column to display genes on Kegg pathways
Example CSV File
          Gene ID, name, treatment log-fold-change, FDR, log average expression, EC Number
          gene001, flavodoxin, 0.1, 0.65, 8.23, 3.1.-.-
          gene002, p53, -1.5, 0.0001, 10.4,
          gene003, potassium uptake protein, -1.2, 0.023, 5.32, 2.7.8.-

One possible way to produce such a CSV file is by performing your differential analysis using R with LIMMA and saving the results as follows: 

          > # Save our analysis object 'efit' to a CSV file
          > class(efit)
          [1] "MArrayLM"
          attr{,"package"}
          [1] "limma"
          > colnames(efit)
          [1] "GppX" "luxS" "cdhR"
          > write.csv(topTable{efit, number=Inf}, 'dge.csv', row.names=F)
          > quit()
          bash$