Merge pull request #49 from KrishnaswamyLab/dev

PHATE v0.2.9

Author: scottgigante

Matlab/compute_alpha_kernel_sparse.m

@@ -69,8 +69,8 @@
 
 idx_thresh=find(below_thresh);
 
-if ~isempty(idx_thresh) 
-    K=exp(-(kdist(idx_thresh,:)./epsilon(idx_thresh)).^a);
+if ~isempty(idx_thresh)
+    K=exp(-(kdist(idx_thresh,:)./repmat(epsilon(idx_thresh),1,size(kdist,2))).^a);
     K(K<=th)=0;
     K=K(:);
     i = repmat(idx_thresh',1,size(idx,2));
@@ -94,7 +94,7 @@
     idx_thresh2=find(below_thresh2);
 
     if ~isempty(idx_thresh2)
-        K2=exp(-(kdist2(idx_thresh2,:)./epsilon2(idx_thresh2)).^a);
+        K2=exp(-(kdist2(idx_thresh2,:)./repmat(epsilon2(idx_thresh2),1,size(kdist2,2))).^a);
         K2(K2<=th)=0;
         idx_notthresh=find(~below_thresh);
         i2=repmat(idx_notthresh(idx_thresh2)',1,size(idx2,2));

Matlab/compute_kernel_sparse.m

@@ -1,4 +1,4 @@
-function W = compute_kernel_sparse(data, varargin)
+function [W,Wns] = compute_kernel_sparse(data, varargin)
 % W = compute_kernel_sparse(data, varargin)
 %   computes kernel W
 % varargin:
@@ -50,10 +50,10 @@
 i = repmat((1:N)',1,size(idx,2));
 i = i(:);
 j = idx(:);
-W = sparse(i, j, ones(size(j)));
+Wns = sparse(i, j, ones(size(j)));
 
 disp '   Symmetrize affinities'
-W = W + W';
+W = Wns + Wns';
 
 disp '   Done computing kernel'
 

Matlab/phate.m

@@ -1,4 +1,4 @@
-function Y = phate(data, varargin)
+function [Y, P, K] = phate(data, varargin)
 % phate  Run PHATE for visualizing noisy non-linear data in lower dimensions
 %   Y = phate(data) runs PHATE on data (rows: samples, columns: features)
 %   with default parameter settings and returns a 2 dimensional embedding.
@@ -20,7 +20,7 @@
 %   e.g. 10 or 15. Defaults to 5.
 %
 %   'a' - alpha of alpha decaying kernel. when a=[] knn (unweighted) kernel
-%   is used. Defaults to 15.
+%   is used. Defaults to 40.
 %
 %   't' - number of diffusion steps. Defaults to [] wich autmatically picks
 %   the optimal t.
@@ -85,7 +85,7 @@
 distfun_mds = 'euclidean';
 pot_method = 'log';
 K = [];
-a = 15;
+a = 40;
 Pnm = [];
 t_max = 100;
 pot_eps = 1e-7;

Matlab/run_phate_DLA_tree.m

@@ -21,6 +21,7 @@
 axis tight
 xlabel 'PCA1'
 ylabel 'PCA2'
+drawnow
 
 %% PHATE 2D
 Y_PHATE_2D = phate(M, 't', 20, 'gamma', 0);
@@ -34,6 +35,7 @@
 axis tight
 xlabel 'PHATE1'
 ylabel 'PHATE2'
+drawnow
 
 %% PHATE 3D
 Y_PHATE_3D = phate(M, 'ndim', 3, 't', 20);
@@ -49,6 +51,7 @@
 xlabel 'PHATE1'
 ylabel 'PHATE2'
 zlabel 'PHATE3'
+drawnow
 
 %% tSNE
 tic;
@@ -64,6 +67,7 @@
 axis tight
 xlabel 'tSNE1'
 ylabel 'tSNE2'
+drawnow
 
 %% plot combined
 figure;

Matlab/run_phate_EB.m

@@ -30,6 +30,7 @@
 h = colorbar;
 set(h,'xtick',1:5);
 ylabel(h, 'time');
+drawnow
 
 %% PHATE 2D
 Y_PHATE_2D = phate(data, 't', 20);
@@ -47,6 +48,7 @@
 h = colorbar;
 set(h,'xtick',1:5);
 ylabel(h, 'time');
+drawnow
 
 %% PHATE 3D
 Y_PHATE_3D = phate(data, 't', 20, 'ndim', 3);
@@ -62,11 +64,12 @@
 xlabel 'PHATE1'
 ylabel 'PHATE2'
 zlabel 'PHATE3'
-title 'PHATE'
+title 'PHATE 3D'
 h = colorbar;
 set(h,'xtick',1:5);
 ylabel(h, 'time');
 view([-170 15]);
+drawnow
 
 %% tSNE -- slow!!!
 % tic;
@@ -130,3 +133,10 @@
 set(h,'xtick',1:5);
 ylabel(h, 'time');
 
+
+
+
+
+
+
+

Matlab/run_phate_mESC.m

@@ -41,6 +41,7 @@
 title 'PCA'
 h = colorbar;
 ylabel(h, gene);
+drawnow
 
 %% PHATE 2D
 Y_PHATE_2D = phate(M);
@@ -59,6 +60,7 @@
 title 'PHATE 2D'
 h = colorbar;
 ylabel(h, gene);
+drawnow
 
 %% PHATE 3D
 Y_PHATE_3D = phate(M, 'ndim', 3);
@@ -79,6 +81,7 @@
 title 'PHATE 3D'
 h = colorbar;
 ylabel(h, gene);
+drawnow
 
 %% tSNE
 tic;
@@ -99,6 +102,7 @@
 title 'tSNE'
 h = colorbar;
 ylabel(h, gene);
+drawnow
 
 
 %% plot combined

Matlab/run_phate_tree.m

@@ -16,6 +16,7 @@
 axis tight
 xlabel 'PCA1'
 ylabel 'PCA2'
+drawnow
 
 %% PHATE 2D
 Y_PHATE_2D = phate(M, 't', 32);
@@ -29,6 +30,7 @@
 axis tight
 xlabel 'PHATE1'
 ylabel 'PHATE2'
+drawnow
 
 %% PHATE 3D
 Y_PHATE_3D = phate(M, 'ndim', 3, 't', 32);
@@ -45,6 +47,7 @@
 ylabel 'PHATE2'
 zlabel 'PHATE3'
 view([-15 20]);
+drawnow
 
 %% tSNE
 tic;
@@ -60,6 +63,7 @@
 axis tight
 xlabel 'tSNE1'
 ylabel 'tSNE2'
+drawnow
 
 %% plot combined
 figure;

Matlab/svdpca.m

@@ -1,10 +1,11 @@
-function [pc,U,S] = svdpca(X, k, method)
+function [pc,U,S,mu] = svdpca(X, k, method)
 
 if ~exist('method','var')
     method = 'svd';
 end
 
-X = bsxfun(@minus, X, mean(X));
+mu = mean(X);
+X = bsxfun(@minus, X, mu);
 
 switch method
     case 'svd'

Python/phate/io.py

@@ -58,7 +58,9 @@ def load_10X(data_dir, sparse=True, gene_labels='symbol'):
     data : pandas.DataFrame shape=(n_cell, n_genes)
         imported data matrix
     """
-
+    warnings.warn("phate.io is deprecated. Please use scprep.io instead. "
+                  "Read more at http://scprep.readthedocs.io",
+                  FutureWarning)
     if gene_labels not in ['id', 'symbol', 'both']:
         raise ValueError("gene_labels not in ['id', 'symbol', 'both']")