- Prerequisite : R (>=3.2.0, dtwclust package), Python 2.* (Scipy, numpy, matplotlib, sklearn, rpy2).
- TRACS takes six (+ three optional) arguments
- Example:
python TRACS.py -f expr.txt -tn 5 -rn 3 -tp 0,10,20,40,60 -o results -og hsa- Gene expression file (-f, --file)
- Gene expression file should be tab-delimited.
- If there are T time points for each biological replicate, columns should be arranges as
[T columns from first replicate] -> [T columns from second replicate] -> ... - Example:
Gene R1-T1 R1-T2 R1-T3 ... R2-T1 R2-T2 R2-T3 ...
Gene1 8 11 8 ... 19 10 11 ...
... 19 21 ... 11 10 11 ...
Gene2 14 20 ... 10 8 20 ...
-
The number of time points (-tn, --timenums) and replicates (-rn, --repnums)
-
A list of time points (-tp, --timepoints)
- A list of time points is the format of t1,t2,t3,...,tn
- Example:
-tn 0,5,10,15,20,25
-
Output directory (-o, --outdir)
- All the output files will be generated in the output directory
-
Organism of interest (-og, --organism)
- KEGG organism code (https://www.kegg.jp/kegg/catalog/org_list.html)
- Example: when your data is human gene expression data
-og hsa
-
Method for clustering (-m, --method) (optional)
- Select among KM (K-means clustering), AC (Agglomerative clustering), KS (K-Shape)
- Default m=KM
-
Start (-ks, --kstart) end (-ke, --kend) of the number of clusters, K (optional)
- TRACS will search for the optimal K in the range of [ks, ke].
- Default ks=1, ke=10