ahorn444
2016-08-19 09:31:03 UTC
Dear Matlab users,
I've got the folowing question:
I'm using the following commands for running PCA analysis:
[~,scores,pcvars] = princomp (expressions');
x = scores (:,1);
y = scores (:,2);
z = scores (:,3);
They all work fine but if I put the following command for color-coding of my scores datasets:
gscatter3(x,y,z,subtypes,{'b','g','m'},{'.','.','.'},15);
...I receive the following error message:
If clr is a cell array of strings, the length of clr must be equal to the length of x, y and z.
Do you have any solution for that?
-------------------------------------------------------------------------------------------------------------
In my workspace I have the following files:
expressions <486 x 14 double>
pcvars <486 x 1 double>
scores <14 x 486 double>
subtypes <1 x 14 cell>
x <14x1 double>
y <14x1 double>
z <14x1 double>
-----------------------------------------------------------------------------------------------------------
I'm using following m-files:
pca.m:
[~,scores,pcvars] = princomp(expressions');
x = zscores(scores(:,1));
y = zscores(scores(:,2));
z = zscores(scores(:,3));
gscatter3(x,y,z,subtypes,{'b','g','m'},{'.','.','.'},15);
title('Gene Expressions of Cancer Subtypes');
xlablel(['PC1-(',num2str(round(pcvars(1)/sum(pcvars)*100)),'%)']);
ylablel(['PC2-(',num2str(round(pcvars(2)/sum(pcvars)*100)),'%)']);
zlablel(['PC3-(',num2str(round(pcvars(3)/sum(pcvars)*100)),'%)']);
-----------------------------------------------------------------
princomp.m:
function [coeff, score, latent, tsquare] = princomp(x,econFlag)
%PRINCOMP Principal Components Analysis (PCA) from raw data.
% COEFF = PRINCOMP(X) performs principal components analysis on the N-by-P
% data matrix X, and returns the principal component coefficients, also
% known as loadings. Rows of X correspond to observations, columns to
% variables. COEFF is a P-by-P matrix, each column containing coefficients
% for one principal component. The columns are in order of decreasing
% component variance.
%
% PRINCOMP centers X by subtracting off column means, but does not
% rescale the columns of X. To perform PCA with standardized variables,
% i.e., based on correlations, use PRINCOMP(ZSCORE(X)). To perform PCA
% directly on a covariance or correlation matrix, use PCACOV.
%
% [COEFF, SCORE] = PRINCOMP(X) returns the principal component scores,
% i.e., the representation of X in the principal component space. Rows
% of SCORE correspond to observations, columns to components.
%
% [COEFF, SCORE, LATENT] = PRINCOMP(X) returns the principal component
% variances, i.e., the eigenvalues of the covariance matrix of X, in
% LATENT.
%
% [COEFF, SCORE, LATENT, TSQUARED] = PRINCOMP(X) returns Hotelling's
% T-squared statistic for each observation in X.
%
% When N <= P, SCORE(:,N:P) and LATENT(N:P) are necessarily zero, and the
% columns of COEFF(:,N:P) define directions that are orthogonal to X.
%
% [...] = PRINCOMP(X,'econ') returns only the elements of LATENT that are
% not necessarily zero, i.e., when N <= P, only the first N-1, and the
% corresponding columns of COEFF and SCORE. This can be significantly
% faster when P >> N.
%
% See also BARTTEST, BIPLOT, CANONCORR, FACTORAN, PCACOV, PCARES, ROTATEFACTORS.
% References:
% [1] Jackson, J.E., A User's Guide to Principal Components,
% Wiley, 1988.
% [2] Jolliffe, I.T. Principal Component Analysis, 2nd ed.,
% Springer, 2002.
% [3] Krzanowski, W.J., Principles of Multivariate Analysis,
% Oxford University Press, 1988.
% [4] Seber, G.A.F., Multivariate Observations, Wiley, 1984.
% Copyright 1993-2010 The MathWorks, Inc.
% $Revision: 1.1.8.4 $ $Date: 2011/05/09 01:26:32 $
% When X has more variables than observations, the default behavior is to
% return all the pc's, even those that have zero variance. When econFlag
% is 'econ', those will not be returned.
if nargin < 2, econFlag = 0; end
[n,p] = size(x);
if isempty(x)
pOrZero = ~isequal(econFlag, 'econ') * p;
coeff = zeros(p,pOrZero); coeff(1:p+1:end) = 1;
score = zeros(n,pOrZero);
latent = zeros(pOrZero,1);
tsquare = zeros(n,1);
return
end
% Center X by subtracting off column means
x0 = bsxfun(@minus,x,mean(x,1));
if nargout < 2
if n >= p && (isequal(econFlag,0) || isequal(econFlag,'econ'))
% When only coefs are needed, EIG is significantly faster than SVD.
[coeff,~] = eig(x0'*x0);
coeff = fliplr(coeff);
else
% The principal component coefficients are the eigenvectors of
% S = X0'*X0./(n-1), but computed using SVD.
[~,~,coeff] = svd(x0,econFlag);
end
% When econFlag is 'econ', only (n-1) components should be returned.
% See comment below.
if (n <= p) && isequal(econFlag, 'econ')
coeff(:,n) = [];
end
else
r = min(n-1,p); % max possible rank of X0
% The principal component coefficients are the eigenvectors of
% S = X0'*X0./(n-1), but computed using SVD.
[U,sigma,coeff] = svd(x0,econFlag); % put in 1/sqrt(n-1) later
% Project X0 onto the principal component axes to get the scores.
if n == 1 % sigma might have only 1 row
sigma = sigma(1);
else
sigma = diag(sigma);
end
score = bsxfun(@times,U,sigma'); % == x0*coeff
sigma = sigma ./ sqrt(n-1);
% When X has at least as many variables as observations, eigenvalues
% n:p of S are exactly zero.
if n <= p
% When econFlag is 'econ', nothing corresponding to the zero
% eigenvalues should be returned. svd(,'econ') won't have
% returned anything corresponding to components (n+1):p, so we
% just have to cut off the n-th component.
if isequal(econFlag, 'econ')
sigma(n,:) = []; % make sure this shrinks as a column
coeff(:,n) = [];
score(:,n) = [];
% Otherwise, set those eigenvalues and the corresponding scores to
% exactly zero. svd(,0) won't have returned columns of U
% corresponding to components (n+1):p, need to fill those out.
else
sigma(n:p,1) = 0; % make sure this extends as a column
score(:,n:p) = 0;
end
end
% The variances of the pc's are the eigenvalues of S = X0'*X0./(n-1).
latent = sigma.^2;
% Hotelling's T-squared statistic is the sum of squares of the
% standardized scores, i.e., Mahalanobis distances. When X appears to
% have column rank < r, ignore components that are orthogonal to the
% data.
if nargout == 4
if n > 1
q = sum(sigma > max(n,p).*eps(sigma(1)));
if q < r
warning(message('stats:princomp:colRankDefX', q));
end
else
q = 0;
end
tsquare = (n-1) .* sum(U(:,1:q).^2,2); % == sum((score*diag(1./sigma)).^2,2)
end
end
% Enforce a sign convention on the coefficients -- the largest element in each
% column will have a positive sign.
[~,maxind] = max(abs(coeff),[],1);
d = size(coeff,2);
colsign = sign(coeff(maxind + (0:p:(d-1)*p)));
coeff = bsxfun(@times,coeff,colsign);
if nargout > 1
score = bsxfun(@times,score,colsign);
end
----------------------------------------------------------------------------------------------------------------
I've got the folowing question:
I'm using the following commands for running PCA analysis:
[~,scores,pcvars] = princomp (expressions');
x = scores (:,1);
y = scores (:,2);
z = scores (:,3);
They all work fine but if I put the following command for color-coding of my scores datasets:
gscatter3(x,y,z,subtypes,{'b','g','m'},{'.','.','.'},15);
...I receive the following error message:
If clr is a cell array of strings, the length of clr must be equal to the length of x, y and z.
Do you have any solution for that?
-------------------------------------------------------------------------------------------------------------
In my workspace I have the following files:
expressions <486 x 14 double>
pcvars <486 x 1 double>
scores <14 x 486 double>
subtypes <1 x 14 cell>
x <14x1 double>
y <14x1 double>
z <14x1 double>
-----------------------------------------------------------------------------------------------------------
I'm using following m-files:
pca.m:
[~,scores,pcvars] = princomp(expressions');
x = zscores(scores(:,1));
y = zscores(scores(:,2));
z = zscores(scores(:,3));
gscatter3(x,y,z,subtypes,{'b','g','m'},{'.','.','.'},15);
title('Gene Expressions of Cancer Subtypes');
xlablel(['PC1-(',num2str(round(pcvars(1)/sum(pcvars)*100)),'%)']);
ylablel(['PC2-(',num2str(round(pcvars(2)/sum(pcvars)*100)),'%)']);
zlablel(['PC3-(',num2str(round(pcvars(3)/sum(pcvars)*100)),'%)']);
-----------------------------------------------------------------
princomp.m:
function [coeff, score, latent, tsquare] = princomp(x,econFlag)
%PRINCOMP Principal Components Analysis (PCA) from raw data.
% COEFF = PRINCOMP(X) performs principal components analysis on the N-by-P
% data matrix X, and returns the principal component coefficients, also
% known as loadings. Rows of X correspond to observations, columns to
% variables. COEFF is a P-by-P matrix, each column containing coefficients
% for one principal component. The columns are in order of decreasing
% component variance.
%
% PRINCOMP centers X by subtracting off column means, but does not
% rescale the columns of X. To perform PCA with standardized variables,
% i.e., based on correlations, use PRINCOMP(ZSCORE(X)). To perform PCA
% directly on a covariance or correlation matrix, use PCACOV.
%
% [COEFF, SCORE] = PRINCOMP(X) returns the principal component scores,
% i.e., the representation of X in the principal component space. Rows
% of SCORE correspond to observations, columns to components.
%
% [COEFF, SCORE, LATENT] = PRINCOMP(X) returns the principal component
% variances, i.e., the eigenvalues of the covariance matrix of X, in
% LATENT.
%
% [COEFF, SCORE, LATENT, TSQUARED] = PRINCOMP(X) returns Hotelling's
% T-squared statistic for each observation in X.
%
% When N <= P, SCORE(:,N:P) and LATENT(N:P) are necessarily zero, and the
% columns of COEFF(:,N:P) define directions that are orthogonal to X.
%
% [...] = PRINCOMP(X,'econ') returns only the elements of LATENT that are
% not necessarily zero, i.e., when N <= P, only the first N-1, and the
% corresponding columns of COEFF and SCORE. This can be significantly
% faster when P >> N.
%
% See also BARTTEST, BIPLOT, CANONCORR, FACTORAN, PCACOV, PCARES, ROTATEFACTORS.
% References:
% [1] Jackson, J.E., A User's Guide to Principal Components,
% Wiley, 1988.
% [2] Jolliffe, I.T. Principal Component Analysis, 2nd ed.,
% Springer, 2002.
% [3] Krzanowski, W.J., Principles of Multivariate Analysis,
% Oxford University Press, 1988.
% [4] Seber, G.A.F., Multivariate Observations, Wiley, 1984.
% Copyright 1993-2010 The MathWorks, Inc.
% $Revision: 1.1.8.4 $ $Date: 2011/05/09 01:26:32 $
% When X has more variables than observations, the default behavior is to
% return all the pc's, even those that have zero variance. When econFlag
% is 'econ', those will not be returned.
if nargin < 2, econFlag = 0; end
[n,p] = size(x);
if isempty(x)
pOrZero = ~isequal(econFlag, 'econ') * p;
coeff = zeros(p,pOrZero); coeff(1:p+1:end) = 1;
score = zeros(n,pOrZero);
latent = zeros(pOrZero,1);
tsquare = zeros(n,1);
return
end
% Center X by subtracting off column means
x0 = bsxfun(@minus,x,mean(x,1));
if nargout < 2
if n >= p && (isequal(econFlag,0) || isequal(econFlag,'econ'))
% When only coefs are needed, EIG is significantly faster than SVD.
[coeff,~] = eig(x0'*x0);
coeff = fliplr(coeff);
else
% The principal component coefficients are the eigenvectors of
% S = X0'*X0./(n-1), but computed using SVD.
[~,~,coeff] = svd(x0,econFlag);
end
% When econFlag is 'econ', only (n-1) components should be returned.
% See comment below.
if (n <= p) && isequal(econFlag, 'econ')
coeff(:,n) = [];
end
else
r = min(n-1,p); % max possible rank of X0
% The principal component coefficients are the eigenvectors of
% S = X0'*X0./(n-1), but computed using SVD.
[U,sigma,coeff] = svd(x0,econFlag); % put in 1/sqrt(n-1) later
% Project X0 onto the principal component axes to get the scores.
if n == 1 % sigma might have only 1 row
sigma = sigma(1);
else
sigma = diag(sigma);
end
score = bsxfun(@times,U,sigma'); % == x0*coeff
sigma = sigma ./ sqrt(n-1);
% When X has at least as many variables as observations, eigenvalues
% n:p of S are exactly zero.
if n <= p
% When econFlag is 'econ', nothing corresponding to the zero
% eigenvalues should be returned. svd(,'econ') won't have
% returned anything corresponding to components (n+1):p, so we
% just have to cut off the n-th component.
if isequal(econFlag, 'econ')
sigma(n,:) = []; % make sure this shrinks as a column
coeff(:,n) = [];
score(:,n) = [];
% Otherwise, set those eigenvalues and the corresponding scores to
% exactly zero. svd(,0) won't have returned columns of U
% corresponding to components (n+1):p, need to fill those out.
else
sigma(n:p,1) = 0; % make sure this extends as a column
score(:,n:p) = 0;
end
end
% The variances of the pc's are the eigenvalues of S = X0'*X0./(n-1).
latent = sigma.^2;
% Hotelling's T-squared statistic is the sum of squares of the
% standardized scores, i.e., Mahalanobis distances. When X appears to
% have column rank < r, ignore components that are orthogonal to the
% data.
if nargout == 4
if n > 1
q = sum(sigma > max(n,p).*eps(sigma(1)));
if q < r
warning(message('stats:princomp:colRankDefX', q));
end
else
q = 0;
end
tsquare = (n-1) .* sum(U(:,1:q).^2,2); % == sum((score*diag(1./sigma)).^2,2)
end
end
% Enforce a sign convention on the coefficients -- the largest element in each
% column will have a positive sign.
[~,maxind] = max(abs(coeff),[],1);
d = size(coeff,2);
colsign = sign(coeff(maxind + (0:p:(d-1)*p)));
coeff = bsxfun(@times,coeff,colsign);
if nargout > 1
score = bsxfun(@times,score,colsign);
end
----------------------------------------------------------------------------------------------------------------