def readInData(
cls, phenotype_fname, input_fname, eigen_vector_fname, phenotype_method_id_ls, test_type=1, report=0
):
"""
2010-2-25
call removeUnPhenotypedSNPData() to shrink the snp dataset by removing un-phenotyped ecotypes
2009-3-20
refactored out of run(), easy for MpiAssociation.py to call
"""
header, strain_acc_list, category_list, data_matrix = read_data(input_fname)
snpData = SNPData(
header=header, strain_acc_list=strain_acc_list, category_list=category_list, data_matrix=data_matrix
)
header_phen, strain_acc_list_phen, category_list_phen, data_matrix_phen = read_data(
phenotype_fname, turn_into_integer=0
)
snpData = cls.removeUnPhenotypedSNPData(
snpData, header_phen, strain_acc_list_phen, data_matrix_phen, phenotype_method_id_ls
)
newSnpData, allele2index_ls = snpData.convertSNPAllele2Index(
report
) # 0 (NA) or -2 (untouched) is all converted to -2 as 0 is used to denote allele
newSnpData.header = snpData.header
data_matrix_phen = cls.get_phenotype_matrix_in_data_matrix_order(
strain_acc_list, strain_acc_list_phen, data_matrix_phen
)
phenData = SNPData(header=header_phen, strain_acc_list=snpData.strain_acc_list, data_matrix=data_matrix_phen)
if eigen_vector_fname:
PC_data = cls.getPCFromFile(eigen_vector_fname)
PC_matrix = PC_data.PC_matrix
else:
if test_type == 4: # eigen_vector_fname not given for this test_type. calcualte PCs.
import pca_module
T, P, explained_var = pca_module.PCA_svd(newSnpData.data_matrix, standardize=False)
PC_matrix = T
else:
PC_matrix = None
del snpData
if phenotype_method_id_ls:
which_phenotype_ls = PlotGroupOfSNPs.findOutWhichPhenotypeColumn(phenData, Set(phenotype_method_id_ls))
else: # if not available, take all phenotypes
which_phenotype_ls = range(len(phenData.col_id_ls))
pdata = PassingData(
snpData=newSnpData,
phenData=phenData,
PC_matrix=PC_matrix,
which_phenotype_ls=which_phenotype_ls,
phenotype_method_id_ls=phenotype_method_id_ls,
)
return pdata
def run(self):
if self.debug:
import pdb
pdb.set_trace()
db = Stock_250kDB.Stock_250kDB(drivername=self.drivername, username=self.db_user,
password=self.db_passwd, hostname=self.hostname, database=self.dbname, schema=self.schema)
db.setup(create_tables=False)
session = db.session
snpData = SNPData(input_fname=self.input_fname, turn_into_integer=1, turn_into_array=1, ignore_2nd_column=1)
if self.eigen_vector_fname and self.eigen_value_fname:
eigen_value_ls = self.getEigenValueFromFile(self.eigen_value_fname)
eigen_value_ls = numpy.array(eigen_value_ls)
explained_var = eigen_value_ls/numpy.sum(eigen_value_ls)
PC_data = self.getPCFromFile(self.eigen_vector_fname)
PC_matrix = PC_data.PC_matrix
else:
max_no_of_snps = 10000
if len(snpData.col_id_ls)>max_no_of_snps: #2008-12-01 randomly pick max_no_of_snps SNPs
picked_col_index_ls = random.sample(range(len(snpData.col_id_ls)), max_no_of_snps)
new_col_id_ls = [snpData.col_id_ls[i] for i in picked_col_index_ls]
newSnpData = SNPData(row_id_ls=snpData.row_id_ls, col_id_ls=new_col_id_ls, strain_acc_list=snpData.strain_acc_list,\
category_list=snpData.category_list)
newSnpData.data_matrix = snpData.data_matrix[:, picked_col_index_ls]
snpData = newSnpData
snpData, allele_index2allele_ls = snpData.convertSNPAllele2Index()
explained_var = None
PC_matrix = None
header_phen, strain_acc_list_phen, category_list_phen, data_matrix_phen = read_data(self.phenotype_fname, turn_into_integer=0)
phenData = SNPData(header=header_phen, strain_acc_list=snpData.strain_acc_list, data_matrix=data_matrix_phen) #row label is that of the SNP matrix, because the phenotype matrix is gonna be re-ordered in that way
phenData.data_matrix = Kruskal_Wallis.get_phenotype_matrix_in_data_matrix_order(snpData.row_id_ls, strain_acc_list_phen, phenData.data_matrix) #tricky, using strain_acc_list_phen
phenotype_col_index = self.findOutWhichPhenotypeColumn(phenData, Set([self.phenotype_method_id]))[0]
ecotype_info = getEcotypeInfo(db, self.country_order_type)
#the offset below decides where the label of strains/snps should start in axe_snp_matrix
#2008-11-14 only for PlotGroupOfSNPs.py. you can set it to 1 cuz we dont' draw axe_snp_matrix here.
snp_id_label_y_offset = 0.95
StrainID2PCAPosInfo = self.getStrainID2PCAPosInfo(snpData, pca_range=[0,1], snp_id_label_y_offset=snp_id_label_y_offset, explained_var=explained_var, T=PC_matrix)
axe_y_offset1 = 0.03
axe_height1 = 0.45 #height of axe_chromosome, twice height of axe_map_phenotype_legend
axe_y_offset2 = axe_y_offset1+axe_height1
axe_height2 = 0.5 #height of axe_strain_pca, axe_snp_matrix, axe_map
axe_y_offset3 = axe_y_offset2+axe_height2
axe_x_offset1 = 0.05
axe_width1 = 0.8 #width of axe_strain_pca
axe_x_offset2 = axe_x_offset1 + 0.02 + axe_width1
axe_width2 = 0.05 #width of axe_chromosome, axe_snp_matrix, axe_snp_pca
axe_x_offset3 = axe_x_offset2 + axe_width2
axe_width3 = 0.02 #width of axe_phenotype
phenotype_method = Stock_250kDB.PhenotypeMethod.get(self.phenotype_method_id)
phenotype_cmap = mpl.cm.jet
max_phenotype = numpy.nanmax(phenData.data_matrix[:,phenotype_col_index]) #nanmax ignores the nan elements
min_phenotype = numpy.nanmin(phenData.data_matrix[:,phenotype_col_index]) #nanmin ignores the nan elements
phenotype_gap = max_phenotype - min_phenotype
phenotype_jitter = phenotype_gap/10.
phenotype_norm = mpl.colors.Normalize(vmin=min_phenotype-phenotype_jitter, vmax=max_phenotype+phenotype_jitter)
axe_map_phenotype_legend = pylab.axes([axe_x_offset2, axe_y_offset1, axe_width2, 0.3], frameon=False)
cb = mpl.colorbar.ColorbarBase(axe_map_phenotype_legend, cmap=phenotype_cmap,
norm=phenotype_norm,
orientation='vertical')
cb.set_label('Legend Of Phenotype %s %s'%(phenotype_method.id, phenotype_method.short_name))
axe_strain_map = pylab.axes([axe_x_offset1, axe_y_offset2, axe_width1, axe_height2], frameon=False)
axe_strain_pca = pylab.axes([axe_x_offset1, axe_y_offset1, axe_width1, axe_height1], frameon=False)
axe_strain_map_pca_cover = pylab.axes([axe_x_offset1, axe_y_offset1, axe_width1, axe_height1+axe_height2], frameon=False, \
sharex=axe_strain_pca) #cover both axe_strain_map and axe_strain_pca
axe_strain_map_pca_cover.set_yticks([])
axe_strain_pca_xlim = [-0.05,1.05]
axe_strain_pca_ylim = [0, 1.05]
axe_strain_pca.set_xlim(axe_strain_pca_xlim)
axe_strain_pca.set_ylim(axe_strain_pca_ylim)
axe_strain_map_pca_cover_ylim = [0, (axe_height1+axe_height2)/axe_height1] #set it accordingly
axe_strain_map_pca_cover.set_ylim(axe_strain_map_pca_cover_ylim)
axe_strain_pca.grid(True, alpha=0.3)
axe_strain_pca.set_xticks([])
axe_strain_pca.set_yticks([])
axe_strain_pca_legend = None #no pca legend
self.drawStrainPCA(axe_strain_pca, axe_strain_map, axe_strain_map_pca_cover, axe_strain_pca_legend, StrainID2PCAPosInfo, \
ecotype_info, phenData, \
phenotype_col_index, phenotype_cmap, phenotype_norm, rightmost_x_value=axe_strain_pca_xlim[1],\
strain_color_type=2, pca2map_line_color=None, ecotype_width_on_map=10,\
draw_lines_to_axe_snp_matrix = False, strain_size_on_axe_strain_pca=14, pic_area=self.pic_area,\
map_pca_line_alpha=0.2, map_pca_linewidth=0.2) #customize a couple of things
axe_strain_pca.set_xlim(axe_strain_pca_xlim)
axe_strain_pca.set_ylim(axe_strain_pca_ylim)
axe_strain_map_pca_cover.set_ylim(axe_strain_map_pca_cover_ylim)
png_output_fname = '%s.png'%self.output_fname_prefix
pylab.savefig(png_output_fname, dpi=400)
pylab.savefig('%s.svg'%self.output_fname_prefix)
self.plotLatLonPhenVsPC(ecotype_info, StrainID2PCAPosInfo, phenData, phenotype_col_index, phenotype_cmap, phenotype_norm,
self.output_fname_prefix, commit=self.commit)
