Random Forest

Input

Two datasets, one for training and one for testing

Example training dataset:

id	S01	S02	S03	S04	S05	S06	S07	S08	S09	S10	S11	S12	S13
class	Nor	Nor	CL	CL	CL	CL	Nor	Nor	CL	Nor	Nor	CL	CL
ENSG00000000003	2.33759	2.52662	2.7183	2.51855	3.04828	1.9738	2.24389	2.38385	2.57132	1.30435	1.4402	1.10014	1.58609
ENSG00000000419	4.20574	3.7589	4.60355	4.33196	3.99043	3.36449	3.95675	3.37951	3.60308	3.9614	3.61078	3.40717	3.35534
ENSG00000000457	1.15773	1.44836	1.30756	1.64957	1.22521	1.10778	1.65392	1.3595	1.614	1.55099	1.19662	0.790419	0.80516
ENSG00000000460	2.10237	1.50082	2.36666	2.43947	2.50501	2.28542	2.7399	1.68931	2.43887	2.77064	2.4063	0.930404	1.14447
…	…	…	…	…	…	…	…	…	…	…	…	…	…

Example testing dataset:

id	T01	T02	T03	T04	T05	T06	T07	T08	T09	T10	T11
ENSG00000000003	2.33759	2.52662	2.7183	2.51855	3.04828	1.9738	2.24389	2.38385	2.57132	1.30435	1.10014
ENSG00000000419	4.20574	3.7589	4.60355	4.33196	3.99043	3.36449	3.95675	3.37951	3.60308	3.9614	3.40717
ENSG00000000457	1.15773	1.44836	1.30756	1.64957	1.22521	1.10778	1.65392	1.3595	1.614	1.55099	0.790419
ENSG00000000460	2.10237	1.50082	2.36666	2.43947	2.50501	2.28542	2.7399	1.68931	2.43887	2.77064	0.930404
ENSG00000000971	1.38253	0.373766	2.67851	2.51908	1.58319	2.63903	1.50035	1.08973	1.92864	0	0
…	…	…	…	…	…	…	…	…	…	…	…

Code

R Code to run Random Forest

Output for 100 trees

Result for TestSet

Sample	PredictedClass
T01	Nor
T02	Nor
T03	CL
T04	CL
T05	CL
T06	CL
T07	Nor
T08	Nor
T09	CL
T10	Nor
T11	CL

Predict_stat

Confusion matrix:

	CL	Nor	class.error
CL	7	0	0
Nor	0	6	0

Plot Results