Methods Procedure for the classification of cancer is shown as follows. First, a classifier is trained on a subset (training set) of gene expression dataset. Then, the mature classifier is used for unknown subset (test set) and predicting each observation’s class. The detailed information about classification procedure is shown in Figure 1. Figure 1 Framework for the procedure of classification. Datasets Six publicly available microarray datasets [8–14] were used to test the above described methods and we call them 2-class lung cancer, PLX4032 datasheet colon, prostate, multi-class lung cancer, SRBCT and brain following the naming there. Due to the fact that microarray-based
studies may report findings that are not reproducible, after reviewing literature we selected these above public datasets with the consideration of our research topic and cross-comparison with other similar studies. The main features of these datasets are summarized in Table 1. Table 1 Characteristics of the six microarray datasets used Dataset No. of Tozasertib cost samples Classes (No. of samples) No. of genes Original ref. Website Two-class lung cancer 181 MPM(31),
adenocarcinoma(150) 12533 [8] http://www.chestsurg.org Colon 62 normal(22), tumor(40) 2000 [9] http://microarray.princeton.edu/oncology/affydata/index.html Prostate 102 normal(50), tumor(52) 6033 [10] http://microarray.princeton.edu/oncology/affydata/index.html Multi-class lung cancer 68(66)a adenocarcinoma(37), combined(1), normal(5), small cell(4), squamous cell(10), fetal(1), large EPZ015938 supplier cell(4), lymph node(6) 3171 [11, 12] http://www.genome.wi.mit.edu/mpr/lung/ SRBCT 88(83)b Burkitt lymphoma (29), Ewing sarcoma (11), neuroblastoma (18), rhabdomyosarcoma medroxyprogesterone (25), non-SRBCTs(5) 2308 [13] http://research.nhgri.nih.gov/microarray/Supplement/ Brain 42(38)c medulloblastomas(10), CNS AT/RTs(5), rhabdoid renal and extrarenal rhabdoid tumours(5), supratentorial PNETs(8), non-embryonal brain tumours (malignant glioma) (10), normal human cerebella(4)
5597 [14] http://research.nhgri.nih.gov/microarray/Supplement/ Note: Some samples were removed for keeping adequate number of each type. a. One combined and one fetal cancer samples were removed, and real sample size is 66; b. Five non-SRBCT samples were removed, and real sample size is 83; c. Four normal tissue samples were removed, and real sample size is 38. Data pre-processing To avoid the noise of the dataset, pre-processing was necessary in the analysis. Absolute transformation was first performed on the original data. The data was transformed to have a mean of 0 and standard deviation of 1 after logarithmic transformation and normalization. When the original data had already experienced the above transformation, it entered next step directly. Algorithms for feature gene selection Notation Let xij be the expression level of gene j in the sample i, and yi be the cancer type for sample i, j = 1,…,p and response yi∈1,…,K. Denote Y = (y1,…