Affymetrix
Differential expression analysis for Affymetrix data
Input data
Files:
- Affymetrix .cel files
- A design.csv file describing each sample (i.e. sample metadata), for example,
gsm,transfection,treatment
GSM606125,control,ethanol
GSM606126,control,ethanol
GSM606134,ERK1,ethanol
GSM606133,ERK1,ethanol
GSM606128,control,E2
GSM606127,control,E2
...
, where the sample identifiers must be in the first column.
Files are organized as follows:
my_project
|-- design.csv
|-- GSM606123.CEL.gz
|-- GSM606124.CEL.gz
|-- GSM606125.CEL.gz
|-- GSM606126.CEL.gz
|-- GSM606127.CEL.gz
|-- ...
Output files
- probeset_expression.tsv : Normalized gene expression matrix
- *_comparion.tsv : comparison results (one for each contrast) from running LIMMA package
- probeset_qc.tsv : Probeset QC status
For example,
output/
|-- control_E2-vs-ethanol_comparison.tsv
|-- ERK1_E2-vs-ethanol_comparison.tsv
|-- probeset_expression.tsv
`-- probeset_qc.tsv
Parameter settings
- factor_of_interest — Comparison column; optional
- reference_level — Baseline level; must be one of the values in factor_of_interest column
- grouping_factor — Subgroup analysis column; optional
- blocking_factor — Blocking/Pairing column, used for controlling blocking/pairing effects; optional
- summarization — Probeset summarization method, possible values, MAS5 (default) and RMA
Note: Currently we support adding either grouping_factor or blocking_factor (i.e. not both).
References
Please cite: