def process(line_sources):
"""
@param line_sources: sources of line iterables
"""
# get the headers and data from all of the input sources
header_data_pairs = [hud.decode(lines) for lines in line_sources]
header_list, data_list = zip(*header_data_pairs)
# get the header to index map for each input source
h_to_i_list = [Util.inverse_map(x) for x in header_list]
# get the intersection of headers in all lists
header_sets = [set(x) for x in header_list]
header_intersection = set.intersection(*header_sets)
# get the ordered list of all headers
unique_headers = list(
iterutils.unique_everseen(itertools.chain.from_iterable(header_list)))
# get the ordered list of headers present in every input source
out_headers = [h for h in unique_headers if h in header_intersection]
out_data = []
for h in out_headers:
row = []
for data, h_to_i in zip(data_list, h_to_i_list):
if h in h_to_i:
row.extend(data[h_to_i[h]])
out_data.append(row)
return hud.encode(out_headers, out_data) + '\n'
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
# normalize the names of the isolates
if args.clean_isolates:
names = [Carbone.clean_isolate_element(x) for x in names]
# get the pcs
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.npcs:
msg_a = 'the number of requested principal components '
msg_b = 'must be no more than the number of OTUs'
raise ValueError(msg_a + msg_b)
# create the R frame
headers = ['otu'] + ['pc%d' % (i + 1) for i in range(args.npcs)]
print >> out, '\t'.join(headers)
for i, name in enumerate(names):
typed_row = [name] + [pcs[j][i] for j in range(args.npcs)]
if args.add_indices:
typed_row = [i + 1] + typed_row
row = [str(x) for x in typed_row]
print >> out, '\t'.join(row)
return out.getvalue()
def process(line_sources):
"""
@param line_sources: sources of line iterables
"""
# get the headers and data from all of the input sources
header_data_pairs = [hud.decode(lines) for lines in line_sources]
header_list, data_list = zip(*header_data_pairs)
# get the header to index map for each input source
h_to_i_list = [Util.inverse_map(x) for x in header_list]
# get the intersection of headers in all lists
header_sets = [set(x) for x in header_list]
header_intersection = set.intersection(*header_sets)
# get the ordered list of all headers
unique_headers = list(iterutils.unique_everseen(
itertools.chain.from_iterable(header_list)))
# get the ordered list of headers present in every input source
out_headers = [h for h in unique_headers if h in header_intersection]
out_data = []
for h in out_headers:
row = []
for data, h_to_i in zip(data_list, h_to_i_list):
if h in h_to_i:
row.extend(data[h_to_i[h]])
out_data.append(row)
return hud.encode(out_headers, out_data) + '\n'
def process(hud_lines, matpheno_lines):
"""
@param hud_lines: lines of a .hud file
@param matpheno_lines: lines of a MAT_pheno.txt file
@return: contents of an .ind file
"""
# get the ordered names from the .hud file
names, hud_data = hud.decode(hud_lines)
# get case and control status from the matpheno file
cases = set()
controls = set()
for line in iterutils.stripped_lines(matpheno_lines):
name, classification = line.split(None, 1)
if classification == '1':
cases.add(name)
elif classification == '2':
controls.add(name)
elif classification in ('12', 'null'):
# skip individuals classified like this
pass
else:
msg = 'invalid MAT_pheno classification: ' + classification
raise Exception(msg)
# write the .ind file contents
out = StringIO()
for name in names:
gender = 'U'
classification = 'Ignore'
if name in cases:
classification = 'Case'
elif name in controls:
classification = 'Control'
row = [name, gender, classification]
print >> out, '\t'.join(row)
return out.getvalue().rstrip()
def process(args, raw_hud_lines, nseconds=2):
nwords = args.nwords
nchars = args.nchars
names, data = hud.decode(raw_hud_lines)
out = StringIO()
if len(data) < nwords:
msg = 'the number of OTUs is smaller than the desired sample'
raise HandlingError(msg)
if len(data[0]) < nchars:
msg = 'the number of characters is smaller than the desired sample'
raise HandlingError(msg)
# create the matrix
M = np.array(data)
# select row and column indices
row_indices, col_indices = get_selections(M, nwords, nchars, nseconds)
sorted_row_indices = list(sorted(row_indices))
sorted_col_indices = list(sorted(col_indices))
# print the separation
d = get_separation(M, row_indices, col_indices)
print >> out, 'best separation:', d
# print the index selections
print >> out, 'selected row indices:', sorted_row_indices
print >> out, 'selected column indices:', sorted_col_indices
# print some selected values
for i in sorted_row_indices:
s = ' '.join(str(M[i, j]) for j in sorted_col_indices)
print >> out, names[i] + '\t' + s
return out.getvalue().rstrip()
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
# normalize the names of the isolates
if args.clean_isolates:
names = [Carbone.clean_isolate_element(x) for x in names]
# get the pcs
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.npcs:
msg_a = 'the number of requested principal components '
msg_b = 'must be no more than the number of OTUs'
raise ValueError(msg_a + msg_b)
# create the R frame
headers = ['otu'] + ['pc%d' % (i+1) for i in range(args.npcs)]
print >> out, '\t'.join(headers)
for i, name in enumerate(names):
typed_row = [name] + [pcs[j][i] for j in range(args.npcs)]
if args.add_indices:
typed_row = [i+1] + typed_row
row = [str(x) for x in typed_row]
print >> out, '\t'.join(row)
return out.getvalue()
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
axis_index = args.axis - 1
# check for sufficient number of eigenvectors
if axis_index >= len(pcs):
msg = 'the requested axis is not available'
raise ValueError(msg)
# compute the correlation of each SNP vector the requested PC
pc = pcs[axis_index]
corrs = [mycorr(snp, pc) for snp in C_full.T]
sqcorrs = [mycorr(snp, pc)**2 for snp in C_full.T]
if args.rank_squared:
keys = sqcorrs
else:
keys = corrs
corr_index_pairs = [(cor, i) for i, cor in enumerate(keys)]
sorted_pairs = list(reversed(sorted(corr_index_pairs)))
indices = zip(*sorted_pairs)[1]
if args.locus_from_1:
nominal_indices = [i+1 for i in indices]
else:
nominal_indices = indices
rows = [(nom_i, corrs[i]) for i, nom_i in zip(indices, nominal_indices)]
lines = ['\t'.join(str(x) for x in row) for row in rows]
return '\n'.join(lines) + '\n'
def do_pca(hud_lines):
"""
@param hud_lines: lines of a .hud file
@return: names, scaled vectors
"""
# get the ordered names from the .hud file
names, data = hud.decode(hud_lines)
# create the floating point count matrix
C_full = np.array(data)
m_full, n_full = C_full.shape
# remove invariant columns
C = np.vstack([v for v in C_full.T if len(set(v))>1]).T
# get the shape of the matrix
m, n = C.shape
# get the column means
u = C.mean(axis=0)
# get the centered and normalized counts matrix
M = (C - u) / np.sqrt(u * (1 - u))
# construct the sample covariance matrix
X = np.dot(M, M.T) / n
# get the eigendecomposition of the covariance matrix
evals, evecs = EigUtil.eigh(X)
# scale the eigenvectos by the eigenvalues
pcs = [w*v for w, v in zip(evals, evecs)]
return names, pcs
def do_pca(hud_lines):
"""
@param hud_lines: lines of a .hud file
@return: names, scaled vectors
"""
# get the ordered names from the .hud file
names, data = hud.decode(hud_lines)
# create the floating point count matrix
C_full = np.array(data)
m_full, n_full = C_full.shape
# remove invariant columns
C = np.vstack([v for v in C_full.T if len(set(v)) > 1]).T
# get the shape of the matrix
m, n = C.shape
# get the column means
u = C.mean(axis=0)
# get the centered and normalized counts matrix
M = (C - u) / np.sqrt(u * (1 - u))
# construct the sample covariance matrix
X = np.dot(M, M.T) / n
# get the eigendecomposition of the covariance matrix
evals, evecs = EigUtil.eigh(X)
# scale the eigenvectos by the eigenvalues
pcs = [w * v for w, v in zip(evals, evecs)]
return names, pcs
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
rtable_header_line = '\t'.join(headers)
rows = []
for i, row in enumerate(zip(*data_rows)):
rows.append([i] + list(row))
rtable_data_lines = ['\t'.join(str(x) for x in row) for row in rows]
return '\n'.join([rtable_header_line] + rtable_data_lines) + '\n'
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
data_transpose = zip(*data_rows)
out = StringIO()
print >> out, ' '.join(headers)
for row in data_transpose:
print >> out, ' '.join(str(x) for x in row)
return out.getvalue()
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
rtable_header_line = '\t'.join(headers)
rows = []
for i, row in enumerate(zip(*data_rows)):
rows.append([i] + list(row))
rtable_data_lines = ['\t'.join(str(x) for x in row) for row in rows]
return '\n'.join([rtable_header_line] + rtable_data_lines) + '\n'
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
validate_diploid_data_rows(data_rows)
nheaders = len(headers)
D = np.zeros((nheaders, nheaders))
for i in range(nheaders):
for j in range(nheaders):
ri = np.array(data_rows[i])
rj = np.array(data_rows[j])
D[i, j] = np.mean(np.abs(rj - ri))
return '\n'.join('\t'.join(str(x) for x in r) for r in D)
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
validate_diploid_data_rows(data_rows)
nheaders = len(headers)
D = np.zeros((nheaders, nheaders))
for i in range(nheaders):
for j in range(nheaders):
ri = np.array(data_rows[i])
rj = np.array(data_rows[j])
D[i, j] = np.mean(np.abs(rj - ri))
return "\n".join("\t".join(str(x) for x in r) for r in D)
def process(lines):
"""
@param lines: lines of a .hud file
"""
names, data = hud.decode(lines)
out = StringIO()
for i, genotype in enumerate(data[0]):
name = 'SNP_%d' % i
chromosome = '1'
morgans = '0.0'
bases = i+1
row = [name, chromosome, morgans, bases]
print >> out, '\t'.join(str(x) for x in row)
return out.getvalue().rstrip()
def process(lines):
"""
@param lines: lines of a .hud file
"""
names, data = hud.decode(lines)
out = StringIO()
for i, genotype in enumerate(data[0]):
name = 'SNP_%d' % i
chromosome = '1'
morgans = '0.0'
bases = i + 1
row = [name, chromosome, morgans, bases]
print >> out, '\t'.join(str(x) for x in row)
return out.getvalue().rstrip()
def get_response_content(fs):
# get the headers and data from all of the input sources
headers, sequences = hud.decode(fs.hud.splitlines())
h_to_s = dict((h, s) for h, s in zip(headers, sequences))
headers_out = []
sequences_out = []
for p, hs in process_headers(headers):
headers_out.append(p)
data = np.vstack(h_to_s[h] for h in hs).sum(axis=0)
if fs.combine_exist:
data = np.minimum(1, data)
sequences_out.append(data)
if fs.remove_invariant:
sequences_out = remove_invariant_columns(sequences_out)
return hud.encode(headers_out, sequences_out) + '\n'
def get_response_content(fs):
# get the headers and data from all of the input sources
headers, sequences = hud.decode(fs.hud.splitlines())
h_to_s = dict((h, s) for h, s in zip(headers, sequences))
headers_out = []
sequences_out = []
for p, hs in process_headers(headers):
headers_out.append(p)
data = np.vstack(h_to_s[h] for h in hs).sum(axis=0)
if fs.combine_exist:
data = np.minimum(1, data)
sequences_out.append(data)
if fs.remove_invariant:
sequences_out = remove_invariant_columns(sequences_out)
return hud.encode(headers_out, sequences_out) + '\n'
def get_response_content(fs):
out = StringIO()
# extract names from the .hud file
names, hud_data = hud.decode(fs.hud.splitlines())
# read the csv file
rows = list(csv.reader(Util.get_stripped_lines(fs.info.splitlines())))
header, data_rows = rows[0], rows[1:]
cases, controls = get_precipitation_info(data_rows, fs.threshold)
# write the .ind file contents
for name in names:
gender = 'U'
classification = 'Ignore'
if name in cases:
classification = 'Case'
elif name in controls:
classification = 'Control'
row = [name, gender, classification]
print >> out, '\t'.join(row)
return out.getvalue()
def get_response_content(fs):
out = StringIO()
# extract name order from the .hud file
names, hud_data = hud.decode(fs.hud.splitlines())
# read the csv file
rows = list(csv.reader(Util.get_stripped_lines(fs.info.splitlines())))
header, data_rows = rows[0], rows[1:]
cases, controls = get_temperature_info(data_rows, fs.threshold)
# write the .ind file contents
for name in names:
gender = 'U'
classification = 'Ignore'
if name in cases:
classification = 'Case'
elif name in controls:
classification = 'Control'
row = [name, gender, classification]
print >> out, '\t'.join(row)
return out.getvalue()
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.ncoords:
raise ValueError('the number of requested principal components '
'must be no more than the number of OTUs')
# compute the correlation of each SNP vector with each principal PC
mylist = []
for snp in C_full.T:
row = [mycorr(snp, pc) for pc in pcs[:args.ncoords]]
mylist.append(row)
np.set_printoptions(linewidth=300, threshold=10000)
return str(np.array(mylist))
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.ncoords:
raise ValueError(
'the number of requested principal components '
'must be no more than the number of OTUs')
# compute the correlation of each SNP vector with each principal PC
mylist = []
for snp in C_full.T:
row = [mycorr(snp, pc) for pc in pcs[:args.ncoords]]
mylist.append(row)
np.set_printoptions(linewidth=300, threshold=10000)
return str(np.array(mylist))
def process(hud_lines, info_lines, location):
"""
@param hud_lines: lines of a .hud file
@param info_lines: lines of a phenotype .csv file
@param location: the control location string
"""
out = StringIO()
# extract name order from the .hud file
names, hud_data = hud.decode(hud_lines)
# read the csv file
rows = list(csv.reader(info_lines))
header, data_rows = rows[0], rows[1:]
cases, controls = get_location_info(data_rows, location)
# write the .ind file contents
for name in names:
gender = 'U'
classification = 'Ignore'
if name in cases:
classification = 'Case'
elif name in controls:
classification = 'Control'
row = [name, gender, classification]
print >> out, '\t'.join(row)
return out.getvalue().rstrip()
def process(hud_lines, info_lines, location):
"""
@param hud_lines: lines of a .hud file
@param info_lines: lines of a phenotype .csv file
@param location: the control location string
"""
out = StringIO()
# extract name order from the .hud file
names, hud_data = hud.decode(hud_lines)
# read the csv file
rows = list(csv.reader(info_lines))
header, data_rows = rows[0], rows[1:]
cases, controls = get_location_info(data_rows, location)
# write the .ind file contents
for name in names:
gender = 'U'
classification = 'Ignore'
if name in cases:
classification = 'Case'
elif name in controls:
classification = 'Control'
row = [name, gender, classification]
print >> out, '\t'.join(row)
return out.getvalue().rstrip()
def process(args, hud_lines):
"""
@param hud_lines: lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
# get the ordered names from the .hud file
names, data = hud.decode(hud_lines)
# create the floating point count matrix
C_full = np.array(data)
m_full, n_full = C_full.shape
# remove invariant columns
C = np.vstack([v for v in C_full.T if len(set(v)) > 1]).T
# get the shape of the matrix
m, n = C.shape
# get the column means
u = C.mean(axis=0)
# get the centered and normalized counts matrix
M = (C - u) / np.sqrt(u * (1 - u))
# construct the sample covariance matrix
X = np.dot(M, M.T) / n
# get the eigendecomposition of the covariance matrix
evals, evecs = EigUtil.eigh(X)
L1 = evals.sum()
L2 = np.dot(evals, evals)
proportion = evals[0] / L1
# compute the relative size of the first eigenvalue
L = m * proportion
# compute the Tracy-Widom statistic
x = get_tracy_widom_statistic(m, n, L)
# do linkage correction
n_prime = ((m + 1) * L1 * L1) / ((m - 1) * L2 - L1 * L1)
# detect additional structure using alpha level of 0.05
crit = 0.9794
if n_prime < n:
L_prime = (m - 1) * proportion
x_prime = get_tracy_widom_statistic(m, n_prime, L_prime)
sigs, insig = get_corrected_structure(crit, evals, m, n_prime)
else:
sigs, insig = get_corrected_structure(crit, evals, m, n)
# print some infos
print >> out, 'number of isolates:'
print >> out, m_full
print >> out
print >> out, 'total number of SNPs:'
print >> out, n_full
print >> out
print >> out, 'number of informative SNPs:'
print >> out, n
print >> out
print >> out, 'effective number of linkage-corrected SNPs:'
if n_prime < n:
print >> out, n_prime
else:
print >> out, '[sample is too degenerate for estimation]'
print >> out
print >> out, 'Tracy-Widom statistic (linkage-naive):'
print >> out, x
print >> out
print >> out, 'Tracy-Widom statistic (linkage-corrected):'
if n_prime < n:
print >> out, x_prime
else:
print >> out, '[sample is too degenerate for estimation]'
print >> out
print >> out, 'proportion of variance explained by principal axis:'
print >> out, proportion
print >> out
print >> out, 'number of significant axes of variation:'
print >> out, len(sigs)
print >> out
print >> out, 'significant Tracy-Widom statistics:'
for sig in sigs:
print >> out, sig
print >> out
print >> out, 'first insignificant Tracy-Widom statistic:'
print >> out, insig
print >> out
print >> out, 'principal axis projection:'
for loading, name in sorted(zip(evecs[0] * evals[0], names)):
print >> out, '\t'.join([name, str(loading)])
print >> out
# evals should sum to the number of OTUs
evals_sum = sum(evals)
if args.sum_to_n:
print >> out, 'eigenvalues normalized to sum to the number of OTUs:'
for w in evals:
print >> out, m_full * w / float(evals_sum)
elif args.sum_to_1:
print >> out, 'eigenvalues normalized to sum to 1.0:'
for w in evals:
print >> out, w / float(evals_sum)
return out.getvalue().rstrip()
def process(args, hud_lines):
"""
@param hud_lines: lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
# get the ordered names from the .hud file
names, data = hud.decode(hud_lines)
# create the floating point count matrix
C_full = np.array(data)
m_full, n_full = C_full.shape
# remove invariant columns
C = np.vstack([v for v in C_full.T if len(set(v))>1]).T
# get the shape of the matrix
m, n = C.shape
# get the column means
u = C.mean(axis=0)
# get the centered and normalized counts matrix
M = (C - u) / np.sqrt(u * (1 - u))
# construct the sample covariance matrix
X = np.dot(M, M.T) / n
# get the eigendecomposition of the covariance matrix
evals, evecs = EigUtil.eigh(X)
L1 = evals.sum()
L2 = np.dot(evals, evals)
proportion = evals[0] / L1
# compute the relative size of the first eigenvalue
L = m*proportion
# compute the Tracy-Widom statistic
x = get_tracy_widom_statistic(m, n, L)
# do linkage correction
n_prime = ((m+1)*L1*L1) / ((m-1)*L2 - L1*L1)
# detect additional structure using alpha level of 0.05
crit = 0.9794
if n_prime < n:
L_prime = (m-1)*proportion
x_prime = get_tracy_widom_statistic(m, n_prime, L_prime)
sigs, insig = get_corrected_structure(crit, evals, m, n_prime)
else:
sigs, insig = get_corrected_structure(crit, evals, m, n)
# print some infos
print >> out, 'number of isolates:'
print >> out, m_full
print >> out
print >> out, 'total number of SNPs:'
print >> out, n_full
print >> out
print >> out, 'number of informative SNPs:'
print >> out, n
print >> out
print >> out, 'effective number of linkage-corrected SNPs:'
if n_prime < n:
print >> out, n_prime
else:
print >> out, '[sample is too degenerate for estimation]'
print >> out
print >> out, 'Tracy-Widom statistic (linkage-naive):'
print >> out, x
print >> out
print >> out, 'Tracy-Widom statistic (linkage-corrected):'
if n_prime < n:
print >> out, x_prime
else:
print >> out, '[sample is too degenerate for estimation]'
print >> out
print >> out, 'proportion of variance explained by principal axis:'
print >> out, proportion
print >> out
print >> out, 'number of significant axes of variation:'
print >> out, len(sigs)
print >> out
print >> out, 'significant Tracy-Widom statistics:'
for sig in sigs:
print >> out, sig
print >> out
print >> out, 'first insignificant Tracy-Widom statistic:'
print >> out, insig
print >> out
print >> out, 'principal axis projection:'
for loading, name in sorted(zip(evecs[0] * evals[0], names)):
print >> out, '\t'.join([name, str(loading)])
print >> out
# evals should sum to the number of OTUs
evals_sum = sum(evals)
if args.sum_to_n:
print >> out, 'eigenvalues normalized to sum to the number of OTUs:'
for w in evals:
print >> out, m_full * w / float(evals_sum)
elif args.sum_to_1:
print >> out, 'eigenvalues normalized to sum to 1.0:'
for w in evals:
print >> out, w / float(evals_sum)
return out.getvalue().rstrip()
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
sequences = [''.join(str(x) for x in row) for row in data_rows]
return Phylip.encode(headers, sequences)
def process(raw_hud_lines):
names, data = hud.decode(raw_hud_lines)
columns = zip(*data)
return '\n'.join(''.join(str(x) for x in c) for c in columns)
def process(raw_hud_lines):
names, data = hud.decode(raw_hud_lines)
columns = zip(*data)
return '\n'.join(''.join(str(x) for x in c) for c in columns)
def get_response_content(fs):
headers, data_rows = hud.decode(fs.table.splitlines())
sequences = [''.join(str(x) for x in row) for row in data_rows]
return Phylip.encode(headers, sequences)
