Upload
We recommend you
Upload a CSV or tab-separated file of your RNA-Seq data. One row per gene, and one column of read counts per replicate.
to track and control uploaded data sets.
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$