Estimated parameters.

<p>The estimated time constant (<i>τ_r</i>) and existence ratio (<i>h_r</i>) are shown as the mean of the results shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone-0032352-g003" target="_blank">Fig. 3</a> (<i>R_s</i>, <i>dim</i>) = (3, 145). The results obtained by BzNMF + AIC and BzNMF + AICc are the same when the optimization criterion is the same.</p

Decomposition results for the Rh6G signal sequence.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone-0032352-g005" target="_blank">Figure 5</a> shows an example of the decomposition results for one signal sequence. The input matrix is the Rh6G measurement data in aqueous solution and consists of 54 signal sequences. The signal sequence is represented by a 92-dimensional vector. The rank was estimated to be 3 using the AIC. The open circles, the solid line, and the broken lines show the input signal sequence, the approximated signal sequence, and the decomposed components, respectively.</p

Comparison of computation times and estimated ranks.

<p>The computation times (CPU times) and the estimated ranks are evaluated using three sets of input matrices, similar to the case for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone-0032352-g003" target="_blank">Fig. 3</a> (<i>dim</i> = 145). Parameter <i>k</i> in CV is set to 3.</p

Decomposition results for the simulated signal sequence.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone-0032352-g001" target="_blank">Figure 1</a> shows examples of the decomposition results for one signal sequence. The input matrix is rank 2 and consists of 50 vectors (signal sequences). The signal sequence is represented by a 75-dimensional vector. The open circles, the solid line, and the broken lines show the input signal sequence, the approximated signal sequence, and the decomposed components, respectively. (a) shows the decomposition results obtained using the NMF optimized in the LSE. The rank of (a) is assumed to be 2. (b) shows the decomposition result obtained using BzNMF + AIC optimized in the LSE. The rank of (b) is estimated to be 2 using the AIC.</p

Rank estimation results for different ranks and sample dimensions.

<p>The simulation rank <i>R_s</i> and the dimension of the signal sequence (<i>dim</i>) are set to <i>R_s</i> = {2, 3, 4, 5} and <i>dim</i> = {75, 145, 715, 1,430, 7,150}, respectively. The input matrix is constructed from 50 signal sequences in a set. The ranks are estimated by three sets of input matrices. The blue, yellow, green, and purple bars show the mean of estimated ranks in <i>R_s</i> = 2, <i>R_s</i> = 3, <i>R_s</i> = 4, and <i>R_s</i> = 5, respectively. The red error bars show the maximum and minimum estimated ranks.</p

Error rates of parameters estimated by BzNMF + AIC optimized in the LSE.

<p>The simulation rank <i>R_s</i> and the dimension of signal sequence (<i>dim</i>) are set to <i>R_s</i> = {2, 3, 4, 5} and <i>dim</i> = {145, 715, 1,430, 7,150}, respectively. The input matrix is constructed from 50 signal sequences in a set. The parameters (existence rate and time constant) are estimated from the three sets of input matrices. The error rates of the parameters are calculated from 50×3 signal sequences (error rate of existence rate) and three sets of input matrices (error rate of the time constant). The blue, yellow, green, and purple bars show the averaged error rates for <i>dim</i> = 145, <i>dim</i> = 715, <i>dim</i> = 1,430, and <i>dim</i> = 7,150, respectively.</p

Rank estimation results by AIC and AICc.

<p>The input matrix setting is the same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone-0032352-g001" target="_blank">Figure 1</a>. The experimental results are obtained from one set of input matrices (50 signal sequences). The AIC and AICc are optimized in the LSE. The solid line and the broken line show the results obtained by the AIC and the AICc, respectively.</p

Estimated parameters of the EGFP input matrix.

<p>The results are evaluated using the EGFP input matrix, which consists of 44 signal sequences. The signal sequence is represented by a 101-dimensional vector. The rank of the fitting method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone.0032352-Krichevsky1" target="_blank">[23]</a> is set to 2 based on previous biological knowledge, and the rank of the proposed method is estimated automatically using the AIC (12). In the fitting method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone.0032352-Krichevsky1" target="_blank">[23]</a>, the time constants and the existence ratios are the mean values of 44 signal sequences.</p

Estimated parameters for the Rh6G input matrix.

<p>The results are evaluated using the Rh6G input matrix, which consists of 54 signal sequences, each of which is represented by a 92-dimensional vector. The rank of the fitting method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone.0032352-Krichevsky1" target="_blank">[23]</a> is set to 2 based on chemical knowledge, and the rank of the proposed method is automatically estimated using the AIC (12). In the fitting method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032352#pone.0032352-Krichevsky1" target="_blank">[23]</a>, the time constants and the existence ratios are the mean values of 54 signal sequences.</p

Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells

<div><p>Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector–based convolutional neural network (V-CNN) with respect to extracted features of the induced pluripotent stem cell (iPSC) colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM) classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and <i>F</i>-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87–93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%), textural (91.0%), and combined (93.2%) features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively). Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75–77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental procedures.</p></div