def spellman_yeast_cdc15(data_set='<STR_LIT>'):
if not data_available(data_set):<EOL><INDENT>download_data(data_set)<EOL><DEDENT>from pandas import read_csv<EOL>dir_path = os.path.join(data_path, data_set)<EOL>filename = os.path.join(dir_path, '<STR_LIT>')<EOL>Y = read_csv(filename, header=<NUM_LIT:0>, index_col=<NUM_LIT:0>, sep='<STR_LIT:\t>')<EOL>t = np.asarray([<NUM_LIT:10>, <NUM_LIT:30>, <NUM_LIT:50>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT:100>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT:200>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>])<EOL>times = ['<STR_LIT>'+str(time) for time in t]<EOL>Y = Y[times].T<EOL>t = t[:, None]<EOL>return data_details_return({'<STR_LIT:Y>' : Y, '<STR_LIT:t>': t, '<STR_LIT:info>': '<STR_LIT>'}, data_set)<EOL>
These are the gene expression levels from the CDC-15 experiment of Spellman et al (1998).