Initial import
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import sys
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from pylab import *
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import matplotlib
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from scipy import *
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from scipy.linalg import inv,norm
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sys.path.append("/home/flatberg/fluents/fluents/lib")
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import select_generators
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sys.path.remove("/home/flatberg/fluents/fluents/lib")
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def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
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""" L-shaped Partial Least Sqaures Regression by the nipals algorithm.
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(X!Z)->Y
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:input:
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X : data matrix (m, n)
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Y : data matrix (m, l)
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Z : data matrix (n, o)
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:output:
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T : X-scores
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W : X-weights/Z-weights
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P : X-loadings
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Q : Y-loadings
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U : X-Y relation
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L : Z-scores
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K : Z-loads
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B : Regression coefficients X->Y
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b0: Regression coefficient intercept
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evx : X-explained variance
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evy : Y-explained variance
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evz : Z-explained variance
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:Notes:
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"""
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if mean_ctr:
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xctr, yctr, zctr = mean_ctr
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X, mnX = center(X, xctr)
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Y, mnY = center(Y, xctr)
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Z, mnZ = center(Z, zctr)
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varX = pow(X, 2).sum()
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varY = pow(Y, 2).sum()
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varZ = pow(Z, 2).sum()
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m, n = X.shape
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k, l = Y.shape
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u, o = Z.shape
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# initialize
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U = empty((k, a_max))
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Q = empty((l, a_max))
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T = empty((m, a_max))
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W = empty((n, a_max))
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P = empty((n, a_max))
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K = empty((o, a_max))
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L = empty((u, a_max))
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B = empty((a_max, n, l))
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b0 = empty((a_max, m, l))
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var_x = empty((a_max,))
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var_y = empty((a_max,))
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var_z = empty((a_max,))
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for a in range(a_max):
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if verbose:
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print "\n Working on comp. %s" %a
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u = Y[:,:1]
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diff = 1
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MAX_ITER = 100
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lim = 1e-7
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niter = 0
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while (diff>lim and niter<MAX_ITER):
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niter += 1
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u1 = u.copy()
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w = dot(X.T, u)
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w = w/sqrt(dot(w.T, w))
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l = dot(Z, w)
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k = dot(Z.T, l)
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k = k/sqrt(dot(k.T, k))
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w = alpha*k + (1-alpha)*w
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w = w/sqrt(dot(w.T, w))
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t = dot(X, w)
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c = dot(Y.T, t)
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c = c/sqrt(dot(c.T, c))
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u = dot(Y, c)
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diff = abs(u1 - u).max()
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if verbose:
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print "Converged after %s iterations" %niter
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tt = dot(t.T, t)
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p = dot(X.T, t)/tt
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q = dot(Y.T, t)/tt
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l = dot(Z, w)
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U[:,a] = u.ravel()
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W[:,a] = w.ravel()
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P[:,a] = p.ravel()
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T[:,a] = t.ravel()
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Q[:,a] = q.ravel()
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L[:,a] = l.ravel()
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K[:,a] = k.ravel()
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X = X - dot(t, p.T)
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Y = Y - dot(t, q.T)
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Z = (Z.T - dot(w, l.T)).T
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var_x[a] = pow(X, 2).sum()
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var_y[a] = pow(Y, 2).sum()
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var_z[a] = pow(Z, 2).sum()
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B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
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b0[a] = mnY - dot(mnX, B[a])
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# variance explained
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evx = 100.0*(1 - var_x/varX)
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evy = 100.0*(1 - var_y/varY)
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evz = 100.0*(1 - var_z/varZ)
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return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
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def svd_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
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"""
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NB: In the works ...
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L-shaped Partial Least Sqaures Regression by the svd algorithm.
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(X!Z)->Y
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:input:
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X : data matrix (m, n)
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Y : data matrix (m, l)
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Z : data matrix (n, o)
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:output:
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T : X-scores
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W : X-weights/Z-weights
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P : X-loadings
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Q : Y-loadings
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U : X-Y relation
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L : Z-scores
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K : Z-loads
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B : Regression coefficients X->Y
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b0: Regression coefficient intercept
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evx : X-explained variance
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evy : Y-explained variance
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evz : Z-explained variance
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:Notes:
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Not quite there ,,,,,,,,,,,,,,
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"""
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if mean_ctr:
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xctr, yctr, zctr = mean_ctr
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X, mnX = center(X, xctr)
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Y, mnY = center(Y, xctr)
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Z, mnZ = center(Z, zctr)
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varX = pow(X, 2).sum()
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varY = pow(Y, 2).sum()
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varZ = pow(Z, 2).sum()
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m, n = X.shape
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k, l = Y.shape
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u, o = Z.shape
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# initialize
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U = empty((k, a_max))
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Q = empty((l, a_max))
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T = empty((m, a_max))
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W = empty((n, a_max))
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P = empty((n, a_max))
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K = empty((o, a_max))
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L = empty((u, a_max))
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var_x = empty((a_max,))
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var_y = empty((a_max,))
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var_z = empty((a_max,))
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for a in range(a_max):
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if verbose:
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print "\n Working on comp. %s" %a
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xyz = dot(dot(Z,X.T),Y)
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u,s,vt = linalg.svd(xyz, 0)
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w = u[:,o]
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t = dot(X, w)
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tt = dot(t.T, t)
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p = dot(X.T, t)/tt
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q = dot(Y.T, t)/tt
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l = dot(Z.T, w)
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W[:,a] = w.ravel()
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P[:,a] = p.ravel()
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T[:,a] = t.ravel()
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Q[:,a] = q.ravel()
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L[:,a] = l.ravel()
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K[:,a] = k.ravel()
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X = X - dot(t, p.T)
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Y = Y - dot(t, q.T)
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Z = (Z.T - dot(w, l.T)).T
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var_x[a] = pow(X, 2).sum()
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var_y[a] = pow(Y, 2).sum()
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var_z[a] = pow(Z, 2).sum()
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B = dot(dot(W, inv(dot(P.T, W))), Q.T)
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b0 = mnY - dot(mnX, B)
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# variance explained
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evx = 100.0*(1 - var_x/varX)
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evy = 100.0*(1 - var_y/varY)
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evz = 100.0*(1 - var_z/varZ)
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return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
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def lplsr(X, Y, Z, a_max, mean_ctr=[2,0,1]):
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""" Haralds LPLS.
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"""
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if mean_ctr!=None:
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xctr, yctr, zctr = mean_ctr
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X, mnX = center(X, xctr)
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Y, mnY = center(Y, yctr)
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Z, mnZ = center(Z, zctr)
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varX = pow(X, 2).sum()
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varY = pow(Y, 2).sum()
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varZ = pow(Z, 2).sum()
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m, n = X.shape
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k, l = Y.shape
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u, o = Z.shape
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# initialize
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Wy = empty((l, a_max))
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Py = empty((l, a_max))
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Ty = empty((m, a_max))
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Tz = empty((o, a_max))
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Wz = empty((u, a_max))
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Pz = empty((u, a_max))
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var_x = empty((a_max,))
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var_y = empty((a_max,))
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var_z = empty((a_max,))
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# residuals
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Ey = Y.copy()
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Ez = Z.copy()
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Ex = X.copy()
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for i in range(a_max):
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YtXZ = dot(Ey.T, dot(X, Ez.T))
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U, S, V = linalg.svd(YtXZ)
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wy = U[:,0]
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print wy
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wz = V[0,:]
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ty = dot(Ey, wy)
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tz = dot(Ez.T, wz)
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py = dot(Ey.T, ty)/dot(ty.T,ty)
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pz = dot(Ez, tz)/dot(tz.T,tz)
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Wy[:,i] = wy
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Wz[:,i] = wz
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Ty[:,i] = ty
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Tz[:,i] = tz
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Py[:,i] = py
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Pz[:,i] = pz
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Ey = Ey - outer(ty, py.T)
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Ez = (Ez.T - outer(tz, pz.T)).T
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var_y[i] = pow(Ey, 2).sum()
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var_z[i] = pow(Ez, 2).sum()
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tyd = apply_along_axis(norm, 0, Ty)
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tzd = apply_along_axis(norm, 0, Tz)
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Tyu = Ty/tyd
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Tzu = Tz/tzd
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C = dot(dot(Tyu.T, X), Tzu)
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for i in range(a_max):
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Ex = Ex - dot(dot(Ty[:,:i+1],C[:i+1,:i+1]), Tz[:,:i+1].T)
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var_x[i] = pow(Ex,2).sum()
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# variance explained
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print "var_x:"
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print var_x
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print "varX total:"
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print varX
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evx = 100.0*(1 - var_x/varX)
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evy = 100.0*(1 - var_y/varY)
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evz = 100.0*(1 - var_z/varZ)
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return Ty, Tz, Wy, Wz, Py, Pz, C, Ey, Ez, Ex, evx, evy, evz
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def bifpls(X, Y, Z, a_max, alpha):
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"""Swedssihsh LPLS by nipals.
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"""
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u = X[:,0]
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Ey = Y.copy()
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Ez = Z.copy()
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for i in range(100):
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w = dot(X.T,u)
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w = w/vnorm(w)
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t = dot(X, w)
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q = dot(Ey, t.T)/dot(t.T,t)
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qnorm = vnorm(q)
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q = q/qnorm
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v = dot(Ez, q)
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s = dot(Ez.T, v)/dot(v.T,v)
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v = v*vnorm(s)
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s = s/vnorm(s)
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c = qnorm*(alpha*q + (1-alpha)*s)
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u = dot(Ey, c)/dot(s.T,s)
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p = dot(X.T, t)/dot(t.T,t)
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v2 = dot(Ez, s)/dot(s.T,s)
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Ey = Ey - dot(t, p.T)
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Ez = Ez - dot(v2, c.T)
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# variance explained
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evx = 100.0*(1 - var_x/varX)
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evy = 100.0*(1 - var_y/varY)
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evz = 100.0*(1 - var_z/varZ)
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def center(a, axis):
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# 0 = col center, 1 = row center, 2 = double center
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# -1 = nothing
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if axis==-1:
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return a
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elif axis==0:
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mn = a.mean(0)
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return a - mn, mn
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elif axis==1:
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mn = a.mean(1)[:,newaxis]
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return a - mn , mn
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elif axis==2:
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mn = a.mean(0) + a.mean(1)[:,newaxis] - a.mean()
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return a - mn, mn
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else:
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raise IOError("input error: axis must be in [-1,0,1,2]")
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def correlation_loadings(D, T, P, test=True):
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""" Returns correlation loadings.
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:input:
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- D: [nsamps, nvars], data (non-centered data)
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- T: [nsamps, a_max], Scores
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- P: [nvars, a_max], Loadings
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:ouput:
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- Rloads: [nvars, a_max], Correlation loadings
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- rmseVars: [nvars], scaling coeff. for each var in D
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:notes:
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- FIXME: Calculation is not valid .... using corrceof instead
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"""
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nsamps, nvars = D.shape
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nsampsT, a_max = T.shape
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nvarsP, a_maxP = P.shape
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if nsamps!=nsampsT: raise IOError("D/T mismatch")
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if a_max!=a_maxP: raise IOError("a_max mismatch")
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if nvars!=nvarsP: raise IOError("D/P mismatch")
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#init
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Rloads = empty((nvars, a_max), 'd')
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stdvar = stats.std(D, 0)
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rmseVars = sqrt(nsamps-1)*stdvar
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# center
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D = D - D.mean(0)
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TT = diag(dot(T.T, T))
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sTT = sqrt(TT)
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for a in range(a_max):
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Rloads[:,a] = sTT[a]*P[:,a]/rmseVars
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R = empty_like(Rloads)
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for a in range(a_max):
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for k in range(nvars):
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r = corrcoef(D[:,k], T[:,a])
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R[k,a] = r[0,1]
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#Rloads = R
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return Rloads, R, rmseVars
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def cv_lpls(X, Y, Z, a_max=2, nsets=None,alpha=.5):
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"""Performs crossvalidation to get generalisation error in lpls"""
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cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=True,index_out=True)
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k, l = Y.shape
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Yhat = empty((a_max,k,l), 'd')
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for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
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T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
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a_max=a_max,
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alpha=alpha,
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mean_ctr=[0,0,1],
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verbose=False)
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for a in range(a_max):
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Yhat[a,ind,:] = b0[a][0][0] + dot(xi, B[a])
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Yhat_class = zeros_like(Yhat)
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for a in range(a_max):
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for i in range(k):
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Yhat_class[a,i,argmax(Yhat[a,i,:])]=1.0
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class_err = 100*((Yhat_class+Y)==2).sum(1)/Y.sum(0).astype('d')
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sep = (Y - Yhat)**2
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rmsep = sqrt(sep.mean(1))
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return rmsep, Yhat, class_err
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def jk_lpls(X, Y, Z, a_max, nsets=None, alpha=.5):
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cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=True,index_out=False)
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m, n = X.shape
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k, l = Y.shape
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o, p = Z.shape
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if nsets==None:
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nsets = m
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WWx = empty((nsets, n, a_max), 'd')
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WWz = empty((nsets, o, a_max), 'd')
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WWy = empty((nsets, l, a_max), 'd')
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for i, (xcal,xi,ycal,yi) in enumerate(cv_iter):
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T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
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a_max=a_max,
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alpha=alpha,
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mean_ctr=[0,0,1],
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verbose=False)
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WWx[i,:,:] = W
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WWz[i,:,:] = L
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WWy[i,:,:] = Q
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print "Q"
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print Q
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return WWx, WWz, WWy
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@ -0,0 +1,38 @@
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import pylab
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import matplotlib
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def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None):
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""" Correlation loading plot."""
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# backgorund
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if ax==None or drawback==True:
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radius = 1
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center = (0,0)
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c100 = matplotlib.patches.Circle(center,radius=radius,
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facecolor='gray',
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alpha=.1,
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zorder=1)
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c50 = matplotlib.patches.Circle(center, radius=radius/2.0,
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facecolor='gray',
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alpha=.1,
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zorder=2)
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ax = pylab.gca()
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ax.add_patch(c100)
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ax.add_patch(c50)
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ax.axhline(lw=1.5,color='k')
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ax.axvline(lw=1.5,color='k')
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# corrloads
|
||||
ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder)
|
||||
ax.set_xlim([-1,1])
|
||||
ax.set_ylim([-1,1])
|
||||
if expvar!=None:
|
||||
xstring = "Comp: %d expl.var: %.1f " %(pc1+1, expvar[pc1])
|
||||
pylab.xlabel(xstring)
|
||||
ystring = "Comp: %d expl.var.: %.1f " %(pc2+1, expvar[pc2])
|
||||
pylab.ylabel(ystring)
|
||||
if labels:
|
||||
assert(len(labels)==R.shape[0])
|
||||
for name, r in zip(labels, R):
|
||||
ax.text(r[pc1], r[pc2], " " + name)
|
||||
#pylab.show()
|
||||
|
|
@ -0,0 +1,110 @@
|
|||
""" Module for Gene ontology related functions called in R"""
|
||||
import scipy
|
||||
|
||||
import rpy
|
||||
silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
|
||||
|
||||
def get_term_sim(termlist, method = "JiangConrath", verbose=False):
|
||||
"""Returns the similariy matrix between go-terms.
|
||||
|
||||
Arguments:
|
||||
termlist: character vector of GO terms
|
||||
method: one of
|
||||
("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
|
||||
verbose: print out various information or not
|
||||
"""
|
||||
_methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
|
||||
assert(method in _methods)
|
||||
assert(termlist[0][:2]=='GO')
|
||||
rpy.r.library("GOSim")
|
||||
return rpy.r.getTermSim(termlist, method = method, verbose = verbose)
|
||||
|
||||
def get_gene_sim(genelist, similarity='OA',
|
||||
distance="Resnick"):
|
||||
rpy.r.library("GOSim")
|
||||
rpy.r.assign("ids", genelist)
|
||||
silent_eval('a<-getGeneSim(ids)', verbose=FALSE)
|
||||
|
||||
def goterms_from_gene(genelist, ontology=['BP'], garbage = ['IEA', 'ISS', 'ND']):
|
||||
""" Returns the go-terms from a specified genelist (Entrez id).
|
||||
|
||||
"""
|
||||
rpy.r.library("GO")
|
||||
_CODES = {"IMP" : "inferred from mutant phenotype",
|
||||
"IGI" : "inferred from genetic interaction",
|
||||
"IPI" :"inferred from physical interaction",
|
||||
"ISS" : "inferred from sequence similarity",
|
||||
"IDA" : "inferred from direct assay",
|
||||
"IEP" : "inferred from expression pattern",
|
||||
"IEA" : "inferred from electronic annotation",
|
||||
"TAS" : "traceable author statement",
|
||||
"NAS" : "non-traceable author statement",
|
||||
"ND" : "no biological data available",
|
||||
"IC" : "inferred by curator"
|
||||
}
|
||||
_ONTOLOGIES = ['BP', 'CC', 'MF']
|
||||
assert(scipy.all([(code in _CODES) for code in garbage]))
|
||||
assert(scipy.all([(ont in _ONTOLOGIES) for ont in ontology]))
|
||||
|
||||
goterms = {}
|
||||
for gene in genelist:
|
||||
goterms[gene] = []
|
||||
info = rpy.r('GOENTREZID2GO[["' + str(gene) + '"]]')
|
||||
#print info
|
||||
if info:
|
||||
for term, desc in info.items():
|
||||
if desc['Ontology'] in ontology and desc['Evidence'] not in garbage:
|
||||
goterms[gene].append(term)
|
||||
return goterms
|
||||
|
||||
def genego_matrix(goterms, tmat, gene_ids, term_ids, func=min):
|
||||
ngenes = len(gene_ids)
|
||||
nterms = len(term_ids)
|
||||
gene2indx = {}
|
||||
for i,id in enumerate(gene_ids):
|
||||
gene2indx[id]=i
|
||||
term2indx = {}
|
||||
for i,id in enumerate(term_ids):
|
||||
term2indx[id]=i
|
||||
#G = scipy.empty((nterms, ngenes),'d')
|
||||
G = []
|
||||
newindex = []
|
||||
for gene, terms in goterms.items():
|
||||
g_ind = gene2indx[gene]
|
||||
if len(terms)>0:
|
||||
t_ind = []
|
||||
newindex.append(g_ind)
|
||||
for term in terms:
|
||||
if term2indx.has_key(term): t_ind.append(term2indx[term])
|
||||
print t_ind
|
||||
subsim = tmat[t_ind, :]
|
||||
gene_vec = scipy.apply_along_axis(func, 0, subsim)
|
||||
G.append(gene_vec)
|
||||
|
||||
return scipy.asarray(G), newindex
|
||||
|
||||
def goterm2desc(gotermlist):
|
||||
"""Returns the go-terms description keyed by go-term
|
||||
"""
|
||||
rpy.r.library("GO")
|
||||
term2desc = {}
|
||||
for term in gotermlist:
|
||||
try:
|
||||
desc = rpy.r('Term(GOTERM[["' +str(term)+ '"]])')
|
||||
term2desc[str(term)] = desc
|
||||
except:
|
||||
raise Warning("Description not found for %s\n Mapping incomplete" %term)
|
||||
return term2desc
|
||||
|
||||
def parents_dag(go_terms, ontology=['BP']):
|
||||
""" Returns a list of lists representation of a GO DAG parents of goterms."""
|
||||
try:
|
||||
rpy.r.library("GOstats")
|
||||
except:
|
||||
raise ImportError, "Gostats"
|
||||
assert(go_terms[0][:3]=='GO:')
|
||||
|
||||
# go valid namespace
|
||||
go_env = {'BP':rpy.r.BPPARENTS, 'MF':rpy.r.MFPARENTS, 'CC': rpy.r.CCPARENTS}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
import sys
|
||||
import rpy
|
||||
from pylab import gca, figure, subplot
|
||||
from scipy import *
|
||||
from lpls import *
|
||||
import rpy_go
|
||||
sys.path.append("../../fluents") # home of dataset
|
||||
sys.path.append("../../fluents/lib") # home of cx_stats
|
||||
import dataset
|
||||
import cx_stats
|
||||
from plots_lpls import plot_corrloads
|
||||
|
||||
######## DATA ##########
|
||||
# full smoker data
|
||||
DX = dataset.read_ftsv(open("../../data/smokers-full/Xfull.ftsv"))
|
||||
DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
|
||||
Y = DY.asarray()
|
||||
# select subset genes by SAM
|
||||
rpy.r.library("siggenes")
|
||||
rpy.r.library("qvalue")
|
||||
data = DX.asarray().T
|
||||
# data = data[:100,:]
|
||||
rpy.r.assign("data", data)
|
||||
cl = dot(DY.asarray(), diag([1,2,3])).sum(1)
|
||||
rpy.r.assign("cl", cl)
|
||||
rpy.r.assign("B", 100)
|
||||
# Perform a SAM analysis.
|
||||
print "Starting SAM"
|
||||
sam = rpy.r('sam.out<-sam(data=data,cl=cl,B=B,rand=123)')
|
||||
print "SAM done"
|
||||
# Compute the q-values of the genes.
|
||||
qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
|
||||
qvals = asarray(qq['qvalues'])
|
||||
# cut off
|
||||
co = 0.001
|
||||
index = where(qvals<0.01)[0]
|
||||
|
||||
# Subset data
|
||||
X = DX.asarray()
|
||||
Xr = X[:,index]
|
||||
gene_ids = DX.get_identifiers('gene_ids', index)
|
||||
|
||||
### Build GO data ####
|
||||
|
||||
print "Go terms ..."
|
||||
goterms = rpy_go.goterms_from_gene(gene_ids)
|
||||
terms = set()
|
||||
for t in goterms.values():
|
||||
terms.update(t)
|
||||
terms = list(terms)
|
||||
rpy.r.library("GOSim")
|
||||
# Go-term similarity matrix
|
||||
methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
|
||||
meth = methods[0]
|
||||
print "Term-term similarity matrix (method = %s)" %meth
|
||||
if meth=="CoutoEnriched":
|
||||
rpy.r('setEnrichmentFactors(alpha=0.1,beta=0.5)')
|
||||
tmat = rpy.r.getTermSim(terms, verbose=False, method=meth)
|
||||
# check if all terms where found
|
||||
nanindex = where(isnan(tmat[:,0]))[0]
|
||||
keep=[]
|
||||
has_miss = False
|
||||
if len(nanindex)>0:
|
||||
has_miss = True
|
||||
print "Some terms missing in similarity matrix"
|
||||
keep = where(isnan(tmat[:,0])!=True)[0]
|
||||
print "Number of nans: %d" %len(nanindex)
|
||||
tmat_new = tmat[:,keep][keep,:]
|
||||
new_terms = [i for ind,i in enumerate(terms) if ind in keep]
|
||||
bad_terms = [i for ind,i in enumerate(terms) if ind not in keep]
|
||||
# update go-term dict
|
||||
for gene,trm in goterms.items():
|
||||
for t in trm:
|
||||
if t in bad_terms:
|
||||
trm.remove(t)
|
||||
if len(trm)==0:
|
||||
print "Removing gene: %s" %gene
|
||||
goterms[gene]=trm
|
||||
terms = new_terms
|
||||
tmat = tmat_new
|
||||
# Z-matrix
|
||||
# func (min, max, median, mean, etc),
|
||||
# func decides on the representation of gene-> goterm when multiple
|
||||
# goterms exist for one gene
|
||||
Z, newind = rpy_go.genego_matrix(goterms, tmat, gene_ids, terms,func=mean)
|
||||
Z = Z.T
|
||||
# update X matrix (no go-terms available)
|
||||
Xr = Xr[:,newind]
|
||||
gene_ids = asarray(gene_ids)[newind]
|
||||
|
||||
|
||||
######## LPLSR ########
|
||||
print "LPLSR ..."
|
||||
a_max = 5
|
||||
aopt = 2
|
||||
alpha=.5
|
||||
T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(Xr,Y,Z, a_max, alpha)
|
||||
|
||||
# Correlation loadings
|
||||
dx,Rx,ssx= correlation_loadings(Xr, T, P)
|
||||
dx,Ry,ssx= correlation_loadings(Y, T, Q)
|
||||
cadx,Rz,ssx= correlation_loadings(Z.T, K, L)
|
||||
|
||||
# Prediction error
|
||||
rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
|
||||
|
||||
# Significance Hotellings T
|
||||
Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt)
|
||||
tsqx = cx_stats.hotelling(Wx,W[:,:aopt])
|
||||
tsqz = cx_stats.hotelling(Wz,L[:,:aopt])
|
||||
|
||||
|
||||
## plots ##
|
||||
figure(1) #rmsep
|
||||
#bar()
|
||||
figure(2) # Hypoid correlations
|
||||
plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
|
||||
ax = gca()
|
||||
plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax)
|
||||
|
||||
figure(3)
|
||||
subplot(221)
|
||||
ax = gca()
|
||||
plot_corrloads(Rx, pc1=0, pc2=1, s=tsqx/2.0, c='b', zorder=5, expvar=evx, ax=ax)
|
||||
# title('X correlation')
|
||||
subplot(222)
|
||||
ax = gca()
|
||||
plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax)
|
||||
#title('Y correlation')
|
||||
subplot(223)
|
||||
ax = gca()
|
||||
plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='r', zorder=5, expvar=evz, ax=ax)
|
||||
#title('Z correlation')
|
||||
subplot(224)
|
||||
plot(arange(len(evx)), evx, 'b', label='X', linewidth=2)
|
||||
plot(evy, 'g', label='Y', linewidth=2)
|
||||
plot(evz, 'r', label='Z', linewidth=2)
|
||||
legend(loc=2)
|
||||
ylabel('Explained variance')
|
||||
xlabel('Component')
|
||||
show()
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
|
||||
def smdb_annot(orflist=None, input_fname='registry.genenames.tab', output_fname='yeast.annot'):
|
||||
|
||||
"""Reads registry.genenames.tab from the Stanford yeast
|
||||
microarray database.
|
||||
|
||||
Available from:
|
||||
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/gene_registry/registry.genenames.tab
|
||||
|
||||
input: orf -- list of orfs (open reading frames)
|
||||
file -- (optional) file to fetch info from
|
||||
|
||||
registry.genames contains:
|
||||
|
||||
0 = Locus name
|
||||
1 = Other name
|
||||
2 = Description
|
||||
3 = Gene product
|
||||
4 = Phenotype
|
||||
5 = ORF name
|
||||
6 = SGDID
|
||||
|
||||
NB! Other name, Gene product and Phenotype may have more
|
||||
than one mapping. These are separated by |
|
||||
|
||||
Output: writes an annotation file
|
||||
|
||||
"""
|
||||
outfile = open(output_fname, 'w')
|
||||
header = "Orf\tLocus_id\tOther_name\tDescription\tGene_product\tPhenotype\tSGD_ID\n"
|
||||
outfile.write(header)
|
||||
text = open(input_fname, 'r').read().splitlines()
|
||||
for line in text:
|
||||
els = line.split('\t')
|
||||
orf_name = els.pop(5)
|
||||
if orf_name!='': # we dont care about non-named orfs
|
||||
if orflist and orf_name not in orflist:
|
||||
break
|
||||
for e in els:
|
||||
if e !='':
|
||||
outfile.write(str(e) + "\t")
|
||||
else:
|
||||
outfile.write("NA")
|
||||
f.write("\n")
|
||||
Reference in New Issue