fuzzy-c-means
A simple python implementation of Fuzzy C-means algorithm.
view repo
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity.
READ FULL TEXT VIEW PDF
We study a variant of the variational autoencoder model (VAE) with a Gau...
read it
Resting-state functional connectivity states are often identified as clu...
read it
In this paper, we propose an end-to-end lifelong learning mixture of exp...
read it
Recent progress in quantum algorithms and hardware indicates the potenti...
read it
For many analytical problems the challenge is to handle huge amounts of
...
read it
Finding an interpretable non-redundant representation of real-world data...
read it
Traditional computational authorship attribution describes a classificat...
read it
A simple python implementation of Fuzzy C-means algorithm.
Clustering has been studied extensively (Aljalbout2018; Min2018)
in machine learning. Recently, many Deep Clustering approaches were proposed, which modified (Variational) Autoencoder ((V)AE) architectures
(Min2018; Zhang2017) or with varying regularization of the latent representation (Dizaji2017; Jiang2017; Yang2017; Fortuin2019).Reconstruction error usually drives the definition of the latent representation learned from an AE or VAE. The representation for AE models is unconstrained and typically places data objects close to each other according to an implicit similarity measure that also yields favorable reconstruction error. In contrast, VAE models regularize the latent representation such that the represented inputs follow a certain variational distribution. This construction enables sampling from the latent representation and data generation via the decoder of a VAE. Typically, the variational distribution is assumed standard Gaussian, but for example, Jiang2017 introduced a mixture of Gaussian variational distribution for clustering purposes.
A key component of clustering approaches is the choice of similarity metric for the considered data objects which we try to group (Irani2016). Such similarity metrics are either defined a priori or learned from the data to specifically solve classification tasks via a Siamese network architecture (Chopra2005). Dimensionality reduction approaches, such as UMAP (McInnes2018) or t-SNE (Maaten2008), allow to specify a similarity metric for projection and thereby define the data separation in the inferred latent representation.
In this work, we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a new deep architecture that performs similarity-based representation learning, clustering of the data and generation of data from each specific data mode. Due to a combined loss function, it can be jointly optimized. We assess the scope of the model on synthetic data and we present superior clustering performance on MNIST. Moreover, in an ablation study, we show the efficiency and precision of MoE-Sim-VAE for data generation purposes in comparison to the most related state-of-the-art method
(Jiang2017). Finally, we show an application of MoE-Sim-VAE on a real-world clustering problem in biology on multiple datasets.Our main contributions are to
Develop a novel autoencoder architecture for
similarity-based representation learning
unsupervised clustering
accurate and efficient data generation
Embed the Mixture-of-Expert architecture into a Variational Autoencoder setup to train a separate generator for each data mode
Show superior clustering performance of the model on benchmark dataset and real-world biological data
Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE, Figure 1). The model is based on the Variational Autoencoder (Kingma2014). While the encoder network is shared across all data points, the decoder of the MoE-Sim-VAE consists of a number of different subnetworks, forming a Mixture-of-Experts architecture (Shazeer2017). Each subnetworks constitutes a generator for a specific data mode and is learned from the data.
The variational distribution over the latent representation is defined to be a mixture of multivariate Gaussians, first introduced by Jiang2017. In our model, we aim to learn the mixture components in the latent representation to be standard Gaussians
(1) |
where are mixture coefficients, are the means for each mixture component,
is the identity matrix and
is the number of mixture components. Similar to optimizing an Evidence Lower Bound (ELBO), we penalize the latent representation via the reconstruction loss of the data and by using the Kullback-Leibler (KL) divergence for multivariate Gaussians (Jiang2017) on the latent representation(2) |
where is a constant, , and is the identity matrix. Further, , where for , for a number of dimensions
, is estimated from the samples of the latent representation. Finally, we assume
resulting in the following simplified objective(3) |
to penalize exclusively the covariance of each cluster. It remains to define the reconstruction loss , where we choose a Binary Cross-Entropy
(4) |
between the original data (scaled between and ) and the reconstructed data , where iterates the batch size and the dimensions of the data . Finally the loss for the VAE part is defined by
(5) |
with a weighting coefficient
which can be optimized as a hyperparameter.
Training of a data mode-specific generator expert requires samples from the same data mode. This necessitates to solve a clustering problem, that is, mapping the data via the latent representation into clusters, each corresponding to one of the generator experts. We solve this clustering problem via a clustering network, also referred to as gating network for MoE models. It takes as input the latent representation of sample
and outputs probabilities
for clustering sample into cluster . According to this cluster assignment, sample is then gated to expert for each sample . We further define the cluster centers forsimilar as in the Expectation Maximization (EM) algorithm for Gaussian Mixture models
(Bishop2006) as(6) |
where is the absolute number of data points assigned to cluster based on highest probability for each sample . The Gaussian mixture distributed latent representation (via KL loss in Equation 3) is motivation for the empirical computation of the cluster means and further, similar as in the EM algorithm, it allows iterative optimization of the means of the Gaussians. We train the clustering network to reconstruct a data-driven similarity matrix , using the Binary Cross-Entropy
(7) |
to minimize the error in , with where is the number of samples (e.g., batch size). Intuitively, approximates the similarity matrix since values in are only close to when similar data objects are assigned to the same cluster, similar to the entries in the adjacency similarity matrix . This similarity matrix is derived in an unsupervised way in our experiments (e.g. UMAP projection of the data and k-nearest-neighbors or distance thresholding to define the adjacency matrix for the batch), but can also be used to include weakly-supervised information (e.g., knowledge about diseased vs. non-diseased patients). If labels are available, the model could even be used to derive a latent representation with supervision. The similarity feature in MoE-Sim-VAE thus allows to include prior knowledge about the best similarity measure on the data.
Moreover, we apply the DEPICT loss from Dizaji2017, to improve the robustness of the clustering. For the DEPICT loss, we additionally propagate a noisy probability through the clustering network using dropout after each layer. The goal is to predict the same cluster for both, the noisy and the clean probability (without applying dropout). Dizaji2017 derived as objective function a standard cross-entropy loss
(8) |
whereby is computed via the auxiliary function
(9) |
where we refer to Dizaji2017 for exact derivation. The DEPICT loss encourages the model to learn invariant features from the latent representation for clustering with respect to noise (Dizaji2017). Looking at it from a different perspective, the loss helps to define a latent representation which has those invariant features to be able to reconstruct the similarity and therefore the clustering correctly. The complete clustering loss function is then defined by
(10) |
with a mixture coefficient which can be optimized as a hyperparameter.
Finally, the MoE-Sim-VAE model loss is defined by
(11) |
which consists of the two main loss functions , acting as a regularization for the latent representation, and , which helps to learn the mixture components based on an a priori defined data similarity. The model objective function can then be optimized end-to-end to train all parts of the model.
(V)AEs have been extensively used for clustering (Xie2016; Dizaji2017; Li2017; Yang2017; Saito2017; Chen2017; Aljalbout2018; Fortuin2019). The most related approaches to MoE-Sim-VAE are Jiang2017 and Zhang2017.
Jiang2017 introduced the VaDE model, comprising a mixture of Gaussians as underlying distribution in the latent representation of a Variational Autoencoder. Optimizing the Evidence Lower Bound (ELBO) of the log-likelihood of the data can be rewritten to optimize the reconstruction loss of the data and KL divergence between the variational posterior and the mixture of Gaussians prior. Jiang2017 motivate the use of to two separate networks for reconstruction and the generation process of the model. Further, to effectively generate images from a specific data mode and to increase image quality, the sampled points have to surpass a certain posterior threshold and are otherwise rejected. This leads to an increased computational effort. The MoE Decoder of our model, which is used for both reconstruction and generation, does not need such a threshold, as we discuss in more detail in Section 4.2.1.
Zhang2017
have introduced a mixture of autoencoders (MIXAE) model. The latent representation of the MIXAE is defined as the concatenation of the latent representation vectors of each single autoencoder in the model. Based on this concatenated latent representation, a Mixture Assignment Network predicts probabilities which are used in the Mixture Aggregation to form the output of the generator network. Each AE model learns the manifold of a specific cluster, similarly to our MoE Decoder. However, MIXAE does not optimize a variational distribution, such that generation of data from a distribution over the latent representation is not possible, in contrast to the MoE-Sim-VAE (Figure
2).We evaluate the MoE-Sim-VAE using synthetic data and the MNIST data set of handwritten digits (LeCun1998) for clustering and data generation. Furthermore, we performed an ablation study to demonstrate the importance of the MoE Decoder. Finally, we present experiments on a real-world application of defining cellular subpopulations from mass cytometry measurements (Bandura2009) of multiple publicly available datasets (Weber2016; Bodenmiller2012). Model implementation details are reported in the appendix in section A.1
We found that our model achieves superior clustering performance compared to other models on synthetic, MNIST and real-world datasets. Moreover, we show that MoE-Sim-VAE can more effectively and efficiently generate data from specific modes in comparison to other methods.
We evaluated our model using data sampled from a 100-dimensional multivariate Gaussian with equal mixture weights for each component. We tested two aspects of our model: Firstly, we evaluated up to how many clusters our model can fit well. Therefore, we sampled data from distributions with up to a hundred mixture components. For this experiment, we assume knowledge of the true number of clusters in the data for both methods, MoE-Sim-VAE and GMMs. Secondly, we tested if our model is able to identify the true number of clusters in the data. The similarity matrix was defined as an adjacency matrix over the data items. Adjacency indicators were based on projecting the data via dimensionality reduction with UMAP (McInnes2018) and selecting neighbors according to a distance threshold. Details on model parameters can be found in Section A.1.1.
MoE-Sim-VAE performs better or comparable to the baseline for the number of clusters of up to 40 (Figure (a)a). The model predicts with a close to perfect F-measure until reaching a true number of clusters of 30. Within the range of true number of clusters from 30 to 40, the model performs comparable to GMMs. Further, MoE-Sim-VAE learns the true number of clusters on its own (Figure (b)b). For up to components in the data, MoE-Sim-VAE learns the true number of clusters even when defining a model with experts in the MoE Decoder. This suggests that the model is robust to misspecification regarding the number of experts.
METHOD | NMI | ACC |
---|---|---|
JULE, Yang2016b | 0.915 | - |
CCNN, Hsu2017 | 0.876 | - |
DEC, Xie2016 | 0.8 | 0.843 |
DBC, Li2017 | 0.917 | 0.964 |
DEPICT, Dizaji2017 | 0.916 | 0.965 |
DCN, Yang2017 | 0.81 | 0.83 |
Neural Clustering, Saito2017 | - | 0.966 |
UMMC, Chen2017 | 0.864 | - |
VaDE, Jiang2017 | - | 0.945 |
TAGnet, Wang2016 | 0.651 | 0.692 |
IMSAT, Hu2017 | - | 0.984 |
Aljalbout2018 | 0.923 | 0.961 |
MoE-Sim-VAE (proposed) | 0.935 | 0.975 |
We trained a MoE-Sim-VAE model on images from MNIST. We compared our model against multiple models which were recently reviewed in Aljalbout2018, and specifically against VaDE (Jiang2017) which shares similar properties with MoE-Sim-VAE (see Sec 3).
We compare the models with the Normalized Mutual Information (NMI) criterion but also classification accuracy (ACC) (Table 1). The MoE-Sim-VAE outperforms the other methods w.r.t. clustering performance when comparing NMI and achieves the second-best result when comparing ACC. Note that we used the number of experts in our model to fit the existing number of digits in MNIST. Regarding the similarity measure, we decided to use as similarity a UMAP projection (McInnes2018) of MNIST and then apply k-nearest-neighbors of each sample in a batch. More details on the model are reported in Section A.1.2.
In addition to the clustering network, we can make use of the latent representation for image generation purposes. The latent representation is trained as a mixture of standard Gaussians. The means of these Gaussians are the centers of the clusters trained via the clustering network. Therefore, the variational distribution can be sampled from and gated to the cluster-specific expert in the MoE-decoder. The expert then generates new data points for the specific data mode. Results and the schematic are displayed in Figure 2 and in more detail and with greater sample size in the Appendix in Figure A4.
In an ablation study, we compare the two models MoE-Sim-VAE and VaDE (Jiang2017) on generating MNIST images with the request for a specific digit. The goal is to show that a MoE decoder, as proposed in our model, is beneficial. We focus our comparison to VaDE since this model, as the MoE-Sim-VAE, resorts to a mixture of Gaussian latent representation but differs in generating images by means of a single decoder network instead of a Mixture-of-Expert decoder network. The rationale for our design choice is to ensure that smaller sub-networks learn to reproduce and generate specific modes of the data, in this case of specific MNIST digits.
To show that both models’ latent representations are separating the different clusters well, we computed the Maximum Mean Discrepancy (MMD), defined in Section A.1.2. The MMD can be interpreted as a distance between distributions computed based on samples drawn from these distributions. The heatmaps of the MMDs for VaDE and MoE-Sim-VAE as well as an UMAP projection of the latent representation colored with the mixture component confirm visually the separation of the clusters in the latent representations of both models (Fig. A7). As a result, we can conclude that both latent representations can separate the clusters of respective digits well, such that the decoder gets well-defined samples to generate the requested digit. Therefore, the main difference of generating specific digits arises in the decoder/generator networks.
We evaluated the importance of the MoE-Decoder to (1) accurately generate requested digits and (2) be efficient in generating requested digits. Specifically, we sampled points from each mixture component in the latent representation, generated images, and used the model’s internal clustering to assign a probability to which digits were generated. To generate correct and high-quality images with VaDE, the posterior of the latent representation needs to be evaluated for each sample. This was done for the different thresholds . The default threshold Jiang2017 used was . Instead of thresholding the latent representation, we ran the generation process for MoE-Sim-VAE for each threshold with the same settings. To generate images from VaDE we used the Python implementation^{1}^{1}1https://github.com/slim1017/VaDE and model weights publicly available from Jiang2017.
As a result of this analysis we report a confusion matrix for MoE-Sim-VAE in Figure
A11, the confusion matrices for each threshold for VaDE in Figure A23, the accuracy of generating a requested digit and the number of runs required in Figure A10. In summary, one can see that the MoE-Sim-VAE generates digits more accurately with fewer resources required. This can especially be seen when comparing the number of iterations required to fulfill the default posterior threshold of . VaDE needs nearly million iterations to find samples that fulfill the aforementioned threshold criterion whereas the MoE-Sim-VAE only requires for a comparable sample accuracy. In comparison the mean accuracy over all thresholds for MoE-Sim-VAE is , whereas VaDE reaches on average . VaDE reaches a maximum accuracy of , which costs the aforementioned million iterations for generating images, whereas MoE-Sim-VAE reaches a maximum accuracy of with runs, without accounting for a systematic generating/clustering error (confusing and ) of MoE-Sim-VAE which can be seen in the confusion matrix in Figure A11.Method | Levine_32dim | Levine_13dim | Samusik_01 | Samusik_all |
---|---|---|---|---|
ACCENSE | 0.494 | 0.358 | 0.517 | 0.502 |
ClusterX | 0.682 | 0.474 | 0.571 | 0.603 |
DensVM | 0.66 | 0.448 | 0.239 | 0.496 |
FLOCK | 0.727 | 0.379 | 0.608 | 0.631 |
flowClust | NA | 0.416 | 0.612 | 0.61 |
flowMeans | 0.769 | 0.518 | 0.625 | 0.653 |
flowMerge | NA | 0.247 | 0.452 | 0.341 |
flowPeaks | 0.237 | 0.215 | 0.058 | 0.323 |
FlowSOM | 0.78 | 0.495 | 0.707 | 0.702 |
FlowSOM_pre | 0.502 | 0.422 | 0.583 | 0.528 |
immunoClust | 0.413 | 0.308 | 0.552 | 0.523 |
k‐means | 0.42 | 0.435 | 0.65 | 0.59 |
PhenoGraph | 0.563 | 0.468 | 0.671 | 0.653 |
Rclusterpp | 0.605 | 0.465 | 0.637 | 0.613 |
SamSPECTRAL | 0.512 | 0.253 | 0.263 | 0.138 |
SPADE | NA | 0.127 | 0.169 | 0.13 |
SWIFT | 0.177 | 0.179 | 0.202 | 0.208 |
X‐shift | 0.691 | 0.47 | 0.679 | 0.657 |
MoE-Sim-VAE (proposed) | 0.70 | 0.68 | 0.76 | 0.74 |
In the following, we want to show representation learning performance on a real-world problem in biology. Specifically, we focus on cell type definition from single-cell measurements. Cytometry by time-of-flight mass spectrometry (CyTOF) (Bandura2009) is a state-of-the-art technique allowing measurement of up to cells per second and in parallel over protein markers of the cells (Kay2013). Defining biologically relevant cell subpopulations by clustering this data is a common learning task (Aghaeepour2013; Weber2016).
Many methods have been developed to tackle the problem introduced above and were compared on four publicly available datasets in Weber2016. The best out of methods were FlowSOM (VanGassen2015), PhenoGraph (Levine2015) and X-shift (Samusik2016)
. These are based on k-nearest-neighbors heuristics, either defined from a spanning graph or from estimating the data density. In contrast to these methods, MoE-Sim-VAE can map new cells into the latent representation, assign probabilities for cell types and infer an interpretable latent representation allowing intuitive downstream analysis by domain experts.
We applied MoE-Sim-VAE to the same datasets as in Weber2016 and achieve superior results in classification using the F-measure (Equation 12) in three out of four datasets. Similarly as in Weber2016 we trained MoE-Sim-VAE times and report in Table 2 (adopted from Weber2016) the means across all runs. The reproducibility of our model for each dataset can be seen in Figure A24.
Further, we trained a MoE-Sim-VAE model on datasets from Bodenmiller2012 (more details on the data in A.1.3), and achieve superior classification results of cell subpopulations in the data when comparing to state-of-the-art methods in this field (PhenoGraph, X-Shift, FlowSOM). Exact results can be seen in Table A1 or visualized in Figure 3. More details on the MoE-Sim-VAE setting used for all results on CyTOF data are reported in the appendix (Section A.1.3).
Our MoE-Sim-VAE model can infer similarity-based representations, perform clustering tasks, and efficiently as well as accurately generate high-dimensional data. The training of the model is performed by optimizing a joint objective function consisting of data reconstruction, clustering, and KL loss, where the latter regularizes the latent representation. On synthetic data, we have shown the strengths and limitations of the model. On the benchmark dataset of MNIST, we presented superior clustering performance and the efficiency and accuracy of MoE-Sim-VAE in generating high-dimensional data. On the biological real-world task of defining cell subpopulations in complex single-cell data, we show superior clustering performances compared to state-of-the-art methods on over datasets and therefore demonstrate MoE-Sim-VAE’s real-world usefulness.
Future work might include to add adversarial training to the MoE decoder, which could improve image generation to create even more realistic images. Also, specific applications might benefit from replacing the Gaussian with a different mixture model. So far the MoE-Sim-VAE’s similarity measure has to be defined by the user. Relaxing this requirement and allowing for learning a useful similarity measure automatically for inferring latent representations will be an interesting extension to explore. This could be useful in a weakly-supervised setting, which often occurs for example in clinical data consisting of healthy and diseased patients. Minor details between a healthy and diseased patient might make a huge difference and could be learned from the data using neural networks.
AK is supported by the "SystemsX.ch HDL-X" and "ERASysApp Rootbook". AK wants to thank Florian Buettner for helpful discussions and his inspirational attitude.
In the following sections we provide more details on model implementations, metrics used and additional result figures for the experiments described in the main text.
Model and training details:
number of experts:
batch size:
code size:
Number of iterations:
activation function; elu
loss coefficient data reconstruction:
loss coefficient clustering :
loss coefficient mixture of Gaussian:
learning rate: 0.001
dropout rate:
distance threshold (perplexity parameter):
depth clustering network:
internal size clustering network:
trainable parameters: depending on number of experts
We compare results based on F-measure (Aghaeepour2013), which is defined as follows
(12) |
where is the number of samples and are the cluster result and the reference cluster, respectively. Further
is the harmonic mean of precision and recall according to
(13) |
whereby is the precision and is the recall. Results are shown in Table 2.
Model and training details:
number of experts:
batch size:
code size:
Number of iterations:
activation function; elu
loss coefficient data reconstruction:
loss coefficient clustering :
loss coefficient mixture of Gaussian:
learning rate: 0.0001
batch normalization
dropout rate:
k from kNN (perplexity parameter):
depth clustering network:
internal size clustering network:
trainable parameters:
One estimator of the Maximum Mean Discrepancy (MMD) (Gretton2008) is defined as
(14) |
where , are samples from two distributions (e.g. samples from two different clusters of the latent representation, for MNIST of two different digits) and is a kernel function, where we use the popular RBF kernel. Based on that estimator Dougal2019 introduced the hypothesis test
(15) | ||||
(16) |
using the statistic . The distribution for and is not required to be known. Dougal2019 used MMD and this test to train a Generative Adversarial Network (GAN) and also to evaluate the generative performance of the model. In this work we use to test if samples of different clusters of the latent representation are similar, or in other words the distance of the distributions. We used the Python implementation^{2}^{2}2https://github.com/dougalsutherland/opt-mmd/blob/master/two_sample/mmd_test.py from Dougal2019.
Model and training details for all experiments on CyTOF data:
number of experts: (Weber2016), (Bodenmiller2012)
batch size:
code size:
Number of iterations: (Weber2016), (Bodenmiller2012)
activation function: relu
loss coefficient data reconstruction:
loss coefficient clustering :
loss coefficient mixture of Gaussian:
learning rate: (Weber2016), (Bodenmiller2012)
batch normalization
dropout rate:
distance threshold (perplexity parameter):
distance metric: correlation
depth clustering network:
internal size clustering network:
trainable parameters: (Weber2016), (Bodenmiller2012)
Results are computed setting the loss coefficient for the KL loss 3 equal to zero, since we do not intend to generate any data, but rather give the chance to the AE to pick up the correct subpouplations. Also here we use the F-measure defined in Equation 12 as metric to evaluate the models. For the data compared in Weber2016 we ran each model times and report reproducability of our results in A24. The model was trained on all data and validated on the on with labels.
For the data from Bodenmiller2012 we run each model on one time on the each of the datasets. Hereby we focused on the following surface markers: CD3(110:114)Dd, CD45(In115)Dd, CD4(Nd145)Dd, CD20(Sm147)Dd, CD33(Nd148)Dd, CD123(Eu151)Dd, CD14(Gd160)Dd, IgM(Yb171)Dd, HLA-DR(Yb174)Dd, CD7(Yb176)Dd. The subpopulations were originally defined via the SPADE algorithm (Qiu2011)
, which is a visualization tool using Agglomerative hierarchical clustering and minimum spanning trees. The gating of the cells is done manually via coloring of the tree leaves. With MoE-Sim-VAE we try to reconstruct the defined manually defined subpopulations.
Bodenmiller2012 performed experiments on multiple well plates were different inhibitors and their effect was tested. We selected for each well plate row A to test our model on. We decided for all methods to discard subpopulations which are smaller then cells. As a similarity measure for MoE-Sim-VAE we reduced the dimension of the data using UMAP (McInnes2018) using the Canberra distance(17) |
where and . Cells were defined to be similar in MoE-Sim-VAE when the distance between the cells in the UMAP-projection was smaller then a threshold. We trained and tested MoE-Sim-VAE on a splitted dataset with rations and evaluated the performance on the unseen test dataset. In comparison the compatitor methods were trained and tested on all the data, which is an advantage in comparison to our model, but still MoE-Sim-VAE outpreforms the compatitors.
Inhibitor | Well | MoE-Sim-VAE | FlowSOM | X-shift | PhenoGraph |
AKTi | A02 | 0.7666 | 0.5147 | 0.5704 | 0.6588 |
AKTi | A03 | 0.7541 | 0.4793 | 0.546 | 0.6026 |
AKTi | A04 | 0.6815 | 0.6405 | 0.5298 | 0.5974 |
AKTi | A05 | 0.7127 | 0.7108 | 0.6089 | 0.6104 |
AKTi | A06 | 0.6711 | 0.7383 | 0.572 | 0.6611 |
AKTi | A07 | 0.7233 | 0.7034 | 0.5583 | 0.6981 |
AKTi | A08 | 0.7901 | 0.7024 | 0.4541 | 0.5287 |
AKTi | A09 | 0.7604 | 0.4292 | 0.5014 | 0.6414 |
AKTi | A10 | 0.7275 | 0.4952 | 0.4144 | 0.677 |
AKTi | A11 | 0.7540 | 0.6456 | 0.6673 | 0.6302 |
BTKi | A02 | 0.7261 | 0.698 | 0.7136 | 0.7478 |
BTKi | A03 | 0.7982 | 0.6643 | 0.6012 | 0.7141 |
BTKi | A04 | 0.7835 | 0.6864 | 0.6983 | 0.7103 |
BTKi | A05 | 0.7484 | 0.6397 | 0.7454 | 0.7474 |
BTKi | A06 | 0.8196 | 0.703 | 0.7625 | 0.7949 |
BTKi | A07 | 0.7976 | 0.6729 | 0.6841 | 0.7102 |
BTKi | A08 | 0.8108 | 0.6715 | 0.5887 | 0.6884 |
BTKi | A09 | 0.7789 | 0.5299 | 0.6426 | 0.7236 |
BTKi | A10 | 0.7726 | 0.6319 | 0.6775 | 0.7148 |
BTKi | A11 | 0.7857 | 0.6078 | 0.5939 | 0.6786 |
BTKi | A12 | 0.6600 | 0.5503 | 0.6028 | 0.6308 |
Crassin | A01 | 0.6727 | 0.6488 | 0.6315 | 0.6237 |
Crassin | A02 | 0.8225 | 0.557 | 0.6435 | 0.7165 |
Crassin | A03 | 0.8346 | 0.5736 | 0.6628 | 0.7085 |
Crassin | A04 | 0.8446 | 0.5348 | 0.7146 | 0.7045 |
Crassin | A05 | 0.8462 | 0.7444 | 0.6227 | 0.7202 |
Crassin | A06 | 0.8569 | 0.7448 | 0.7078 | 0.6972 |
Crassin | A07 | 0.8170 | 0.5164 | 0.6546 | 0.6309 |
Crassin | A08 | 0.8431 | 0.8283 | 0.5504 | 0.6546 |
Crassin | A09 | 0.8412 | 0.5814 | 0.6027 | 0.6684 |
Crassin | A10 | 0.8527 | 0.7537 | 0.6586 | 0.6338 |
Crassin | A11 | 0.8453 | 0.7174 | 0.6437 | 0.7358 |
Crassin | A12 | 0.7320 | 0.6161 | 0.6436 | 0.6949 |
Dasatinib | A01 | 0.7235 | 0.4466 | 0.554 | 0.6725 |
Dasatinib | A02 | 0.8019 | 0.516 | 0.6238 | 0.701 |
Dasatinib | A03 | 0.7864 | 0.5108 | 0.5366 | 0.6566 |
Dasatinib | A04 | 0.6661 | 0.4796 | 0.5527 | 0.647 |
Dasatinib | A05 | 0.7910 | 0.5014 | 0.5804 | 0.6904 |
Dasatinib | A06 | 0.7979 | 0.5167 | 0.6258 | 0.6707 |
Dasatinib | A07 | 0.8105 | 0.5215 | 0.6016 | 0.6809 |
Dasatinib | A08 | 0.8047 | 0.6928 | 0.5802 | 0.633 |
Dasatinib | A09 | 0.7485 | 0.5203 | 0.5958 | 0.6861 |
Dasatinib | A10 | 0.8062 | 0.5158 | 0.5742 | 0.6503 |
Dasatinib | A11 | 0.7837 | 0.5066 | 0.6331 | 0.6813 |
GDC-0941 | A01 | 0.5632 | 0.6434 | 0.5987 | 0.6279 |
GDC-0941 | A02 | 0.8257 | 0.7291 | 0.7349 | 0.7507 |
GDC-0941 | A03 | 0.8268 | 0.7321 | 0.6822 | 0.7853 |
GDC-0941 | A04 | 0.8389 | 0.7115 | 0.7569 | 0.7421 |
GDC-0941 | A05 | 0.8382 | 0.7946 | 0.7171 | 0.7735 |
GDC-0941 | A06 | 0.8463 | 0.6125 | 0.6858 | 0.764 |
GDC-0941 | A07 | 0.8382 | 0.6061 | 0.7776 | 0.7612 |
GDC-0941 | A08 | 0.8249 | 0.5493 | 0.6058 | 0.7796 |
GDC-0941 | A09 | 0.8606 | 0.7689 | 0.8043 | 0.7206 |
GDC-0941 | A10 | 0.8412 | 0.7227 | 0.653 | 0.6465 |
GDC-0941 | A11 | 0.7859 | 0.5703 | 0.7297 | 0.7891 |
GDC-0941 | A12 | 0.7803 | 0.6326 | 0.69 | 0.6727 |
Go69 | A01 | 0.6520 | 0.6571 | 0.718 | 0.5822 |
Go69 | A02 | 0.7835 | 0.7693 | 0.6075 | 0.7322 |
Go69 | A03 | 0.7305 | 0.7334 | 0.757 | 0.6414 |
Go69 | A04 | 0.7640 | 0.7456 | 0.8013 | 0.7425 |
Go69 | A05 | 0.7812 | 0.7555 | 0.7294 | 0.7727 |
Go69 | A06 | 0.7816 | 0.7404 | 0.7437 | 0.6443 |
Go69 | A07 | 0.7407 | 0.8513 | 0.7527 | 0.6811 |
Go69 | A08 | 0.7293 | 0.7338 | 0.6984 | 0.6525 |
Go69 | A09 | 0.8228 | 0.6955 | 0.6985 | 0.7317 |
Go69 | A10 | 0.7560 | 0.7512 | 0.7689 | 0.7071 |
Go69 | A11 | 0.7565 | 0.7373 | 0.7213 | 0.7315 |
Go69 | A12 | 0.7426 | 0.7086 | 0.7846 | 0.6442 |
H89 | A01 | 0.6734 | 0.6952 | 0.6003 | 0.6105 |
H89 | A02 | 0.7288 | 0.5391 | 0.5918 | 0.678 |
H89 | A03 | 0.8051 | 0.5414 | 0.6856 | 0.6759 |
H89 | A04 | 0.8144 | 0.7314 | 0.662 | 0.7287 |
H89 | A05 | 0.7821 | 0.5468 | 0.6485 | 0.6672 |
H89 | A06 | 0.7647 | 0.5636 | 0.8281 | 0.7165 |
H89 | A07 | 0.7762 | 0.6983 | 0.7284 | 0.6442 |
H89 | A09 | 0.8131 | 0.5442 | 0.5906 | 0.6707 |
H89 | A10 | 0.7517 | 0.5549 | 0.6028 | 0.682 |
H89 | A11 | 0.7417 | 0.7414 | 0.6863 | 0.7257 |
H89 | A12 | 0.7939 | 0.6934 | 0.5831 | 0.6401 |
IKKi | A02 | 0.7945 | 0.6619 | 0.7371 | 0.6475 |
IKKi | A03 | 0.6873 | 0.6568 | 0.5661 | 0.6895 |
IKKi | A04 | 0.7942 | 0.6754 | 0.6386 | 0.7052 |
IKKi | A05 | 0.6977 | 0.6569 | 0.6157 | 0.6899 |
IKKi | A06 | 0.7442 | 0.6931 | 0.7024 | 0.7077 |
IKKi | A07 | 0.7352 | 0.5303 | 0.669 | 0.7001 |
IKKi | A08 | 0.7470 | 0.7006 | 0.5358 | 0.6869 |
IKKi | A09 | 0.8097 | 0.5175 | 0.6299 | 0.6969 |
IKKi | A10 | 0.7647 | 0.6308 | 0.657 | 0.7334 |
IKKi | A11 | 0.7878 | 0.6365 | 0.6757 | 0.6613 |
IKKi | A12 | 0.6673 | 0.5629 | 0.497 | 0.6043 |
Imatinib | A02 | 0.7935 | 0.7571 | 0.6721 | 0.7677 |
Imatinib | A03 | 0.7763 | 0.7429 | 0.7041 | 0.7499 |
Imatinib | A04 | 0.8058 | 0.7564 | 0.6921 | 0.7229 |
Imatinib | A05 | 0.7714 | 0.7559 | 0.6689 | 0.7609 |
Imatinib | A06 | 0.7756 | 0.746 | 0.6956 | 0.7296 |
Imatinib | A07 | 0.7468 | 0.7515 | 0.6974 | 0.7137 |
Imatinib | A08 | 0.7631 | 0.7534 | 0.5189 | 0.7096 |
Imatinib | A09 | 0.8082 | 0.5605 | 0.5819 | 0.7447 |
Imatinib | A10 | 0.7964 | 0.5645 | 0.5637 | 0.78 |
Imatinib | A11 | 0.7289 | 0.7664 | 0.7576 | 0.7395 |
Imatinib | A12 | 0.7012 | 0.8451 | 0.6369 | 0.7259 |
Jak1i | A02 | 0.8210 | 0.5167 | 0.5771 | 0.616 |
Jak1i | A03 | 0.7343 | 0.7139 | 0.6526 | 0.7133 |
Jak1i | A04 | 0.7321 | 0.7066 | 0.6346 | 0.7189 |
Jak1i | A05 | 0.7413 | 0.5163 | 0.6551 | 0.7089 |
Jak1i | A06 | 0.7244 | 0.5525 | 0.6804 | 0.6905 |
Jak1i | A07 | 0.7779 | 0.5499 | 0.5605 | 0.7099 |
Jak1i | A08 | 0.7281 | 0.6995 | 0.6021 | 0.6605 |
Jak1i | A09 | 0.8043 | 0.5064 | 0.6054 | 0.6717 |
Jak1i | A10 | 0.7801 | 0.5295 | 0.5538 | 0.7015 |
Jak1i | A11 | 0.7128 | 0.7307 | 0.7386 | 0.6812 |
Jak1i | A12 | 0.7204 | 0.6229 | 0.6321 | 0.6905 |
Jak2i | A01 | 0.6944 | 0.6379 | 0.6014 | 0.6207 |
Jak2i | A02 | 0.7961 | 0.664 | 0.6656 | 0.7083 |
Jak2i | A03 | 0.7629 | 0.6742 | 0.7138 | 0.7024 |
Jak2i | A04 | 0.7890 | 0.6716 | 0.6227 | 0.7072 |
Jak2i | A05 | 0.6666 | 0.4689 | 0.5314 | 0.6459 |
Jak2i | A06 | 0.8110 | 0.6474 | 0.6651 | 0.6833 |
Jak2i | A07 | 0.7595 | 0.6818 | 0.7593 | 0.6982 |
Jak2i | A08 | 0.8050 | 0.6601 | 0.6152 | 0.686 |
Jak2i | A09 | 0.8028 | 0.5253 | 0.6414 | 0.6501 |
Jak2i | A10 | 0.8030 | 0.6762 | 0.6067 | 0.6364 |
Jak2i | A11 | 0.8228 | 0.5398 | 0.694 | 0.7473 |
Jak2i | A12 | 0.6831 | 0.6214 | 0.5825 | 0.5687 |
Jak3i | A02 | 0.7986 | 0.7108 | 0.5666 | 0.6912 |
Jak3i | A03 | 0.7170 | 0.7116 | 0.6991 | 0.7001 |
Jak3i | A04 | 0.7983 | 0.5243 | 0.6654 | 0.691 |
Jak3i | A05 | 0.7087 | 0.6498 | 0.6884 | 0.7073 |
Jak3i | A06 | 0.7272 | 0.7244 | 0.654 | 0.7059 |
Jak3i | A07 | 0.7768 | 0.5167 | 0.696 | 0.735 |
Jak3i | A08 | 0.7196 | 0.6797 | 0.5946 | 0.7287 |
Jak3i | A09 | 0.7988 | 0.6918 | 0.6013 | 0.6826 |
Jak3i | A10 | 0.8026 | 0.7103 | 0.7104 | 0.7219 |
Jak3i | A11 | 0.7281 | 0.5107 | 0.6854 | 0.6614 |
Jak3i | A12 | 0.7511 | 0.6135 | 0.4861 | 0.61 |
Lcki | A01 | 0.7359 | 0.7582 | 0.6106 | 0.7201 |
Lcki | A02 | 0.7605 | 0.7453 | 0.6391 | 0.7696 |
Lcki | A03 | 0.8032 | 0.5608 | 0.6814 | 0.721 |
Lcki | A04 | 0.7608 | 0.5764 | 0.6788 | 0.7904 |
Lcki | A05 | 0.8210 | 0.5435 | 0.7204 | 0.7442 |
Lcki | A06 | 0.7564 | 0.7662 | 0.728 | 0.7556 |
Lcki | A07 | 0.8304 | 0.579 | 0.6992 | 0.696 |
Lcki | A08 | 0.7854 | 0.7457 | 0.5904 | 0.6972 |
Lcki | A09 | 0.8452 | 0.5859 | 0.6018 | 0.7569 |
Lcki | A10 | 0.7387 | 0.744 | 0.6598 | 0.6627 |
Lcki | A11 | 0.7835 | 0.7639 | 0.6836 | 0.7558 |
Lcki | A12 | 0.7467 | 0.8271 | 0.6888 | 0.6878 |
PP2 | A02 | 0.7687 | 0.759 | 0.7717 | 0.7605 |
PP2 | A03 | 0.8395 | 0.7644 | 0.7304 | 0.7953 |
PP2 | A04 | 0.8442 | 0.7703 | 0.7116 | 0.7162 |
PP2 | A05 | 0.8248 | 0.5777 | 0.7205 | 0.7547 |
PP2 | A06 | 0.7866 | 0.7612 | 0.7461 | 0.7431 |
PP2 | A07 | 0.8595 | 0.7616 | 0.724 | 0.7213 |
PP2 | A08 | 0.8505 | 0.7489 | 0.7109 | 0.7195 |
PP2 | A09 | 0.7902 | 0.5755 | 0.6511 | 0.7738 |
PP2 | A10 | 0.8089 | 0.743 | 0.6635 | 0.7389 |
PP2 | A11 | 0.7977 | 0.5852 | 0.6564 | 0.7846 |
PP2 | A12 | 0.7667 | 0.6012 | 0.6524 | 0.6636 |
Rapamycin | A01 | 0.7028 | 0.675 | 0.5882 | 0.5677 |
Rapamycin | A02 | 0.7215 | 0.6831 | 0.6124 | 0.6697 |
Rapamycin | A03 | 0.7322 | 0.6707 | 0.6296 | 0.6861 |
Rapamycin | A04 | 0.6787 | 0.6696 | 0.6887 | 0.7267 |
Rapamycin | A05 | 0.7231 | 0.653 | 0.7134 | 0.6466 |
Rapamycin | A06 | 0.7310 | 0.6473 | 0.7009 | 0.6386 |
Rapamycin | A07 | 0.7595 | 0.6642 | 0.748 | 0.5882 |
Rapamycin | A08 | 0.7773 | 0.836 | 0.6371 | 0.571 |
Rapamycin | A09 | 0.7732 | 0.6573 | 0.6826 | 0.6615 |
Rapamycin | A10 | 0.7586 | 0.6702 | 0.7136 | 0.6344 |
Rapamycin | A12 | 0.6955 | 0.6361 | 0.6561 | 0.5472 |
SB202 | A01 | 0.6884 | 0.6713 | 0.941 | 0.7101 |
SB202 | A03 | 0.7869 | 0.7549 | 0.6686 | 0.7633 |
SB202 | A05 | 0.7856 | 0.5564 | 0.7387 | 0.6999 |
SB202 | A06 | 0.7707 | 0.755 | 0.7913 | 0.7869 |
SB202 | A10 | 0.7559 | 0.7554 | - | 0.7749 |
SP6 | A01 | 0.7033 | 0.6882 | 0.4191 | 0.532 |
SP6 | A02 | 0.7536 | 0.5035 | 0.5104 | 0.657 |
SP6 | A03 | 0.7387 | 0.6973 | 0.534 | 0.5858 |
SP6 | A04 | 0.6910 | 0.503 | 0.5065 | 0.5975 |
SP6 | A05 | 0.7210 | 0.5068 | 0.5643 | 0.6869 |
SP6 | A06 | 0.7052 | 0.719 | 0.5063 | 0.6384 |
SP6 | A07 | 0.7281 | 0.7074 | 0.5382 | 0.6501 |
SP6 | A08 | 0.7301 | 0.6832 | 0.4665 | 0.6133 |
SP6 | A09 | 0.7743 | 0.5001 | 0.4618 | 0.6208 |
SP6 | A10 | 0.7198 | 0.5111 | 0.524 | 0.6773 |
SP6 | A11 | 0.7494 | 0.493 | 0.5407 | 0.5935 |
SP6 | A12 | 0.7311 | 0.6131 | 0.4488 | 0.6198 |
Sorafenib | A01 | 0.7185 | 0.7217 | 0.5884 | 0.6574 |
Sorafenib | A02 | 0.8250 | 0.7659 | 0.6658 | 0.7664 |
Sorafenib | A03 | 0.7689 | 0.7732 | 0.7078 | 0.6869 |
Sorafenib | A04 | 0.8360 | 0.7094 | 0.7114 | 0.7218 |
Sorafenib | A05 | 0.8304 | 0.5571 | 0.7672 | 0.7153 |
Sorafenib | A06 | 0.8021 | 0.5783 | 0.6991 | 0.7506 |
Sorafenib | A07 | 0.8461 | 0.7051 | 0.7267 | 0.6701 |
Sorafenib | A09 | 0.8226 | 0.7275 | 0.7522 | 0.7587 |
Sorafenib | A10 | 0.8103 | 0.7561 | 0.7457 | 0.7214 |
Sorafenib | A11 | 0.8465 | 0.5777 | 0.7192 | 0.7503 |
Sorafenib | A12 | 0.7715 | 0.6533 | 0.6084 | 0.6129 |
Staurosporine | A01 | 0.7985 | 0.8464 | 0.6057 | 0.5945 |
Staurosporine | A02 | 0.8347 | 0.8312 | 0.5999 | 0.6626 |
Staurosporine | A03 | 0.8079 | 0.7072 | 0.6704 | 0.6787 |
Staurosporine | A04 | 0.8418 | 0.8666 | 0.6452 | 0.6776 |
Staurosporine | A05 | 0.8657 | 0.7305 | 0.7071 | 0.7515 |
Staurosporine | A06 | 0.8694 | 0.516 | 0.6453 | 0.6619 |
Staurosporine | A07 | 0.8277 | 0.7052 | 0.6349 | 0.6657 |
Staurosporine | A08 | 0.8310 | 0.8316 | 0.6213 | 0.678 |
Staurosporine | A09 | 0.8319 | 0.5117 | 0.6747 | 0.6726 |
Staurosporine | A10 | 0.8417 | 0.5108 | 0.6211 | 0.7126 |
Staurosporine | A11 | 0.8246 | 0.8711 | 0.6547 | 0.7445 |
Streptonigrin | A01 | 0.7128 | 0.5689 | 0.6571 | 0.6599 |
Streptonigrin | A02 | 0.7836 | 0.5095 | 0.549 | 0.6155 |
Streptonigrin | A03 | 0.7776 | 0.547 | 0.6497 | 0.6527 |
Streptonigrin | A04 | 0.8466 | 0.7521 | 0.5762 | 0.7061 |
Streptonigrin | A05 | 0.8130 | 0.5406 | 0.6459 | 0.6928 |
Streptonigrin | A06 | 0.8031 | 0.7409 | 0.6446 | 0.6343 |
Streptonigrin | A07 | 0.7987 | 0.5353 | 0.5882 | 0.6657 |
Streptonigrin | A08 | 0.7470 | 0.7458 | 0.5864 | 0.6443 |
Streptonigrin | A09 | 0.7586 | 0.7034 | 0.5928 | 0.6196 |
Streptonigrin | A10 | 0.7159 | 0.6974 | 0.5174 | 0.6809 |
Streptonigrin | A11 | 0.8178 | 0.5649 | 0.593 | 0.6814 |
Streptonigrin | A12 | 0.7410 | 0.6034 | 0.5896 | 0.6286 |
Sunitinib | A01 | 0.7152 | 0.6622 | 0.5653 | 0.6522 |
Sunitinib | A02 | 0.8056 | 0.498 | 0.6138 | 0.6521 |
Sunitinib | A03 | 0.8095 | 0.6873 | 0.6889 | 0.6913 |
Sunitinib | A04 | 0.8142 | 0.6925 | 0.6467 | 0.7121 |
Sunitinib | A05 | 0.8157 | 0.6959 | 0.673 | 0.7073 |
Sunitinib | A06 | 0.7968 | 0.5061 | 0.6654 | 0.7025 |
Sunitinib | A07 | 0.8110 | 0.7 | 0.6333 | 0.6572 |
Sunitinib | A08 | 0.8186 | 0.6894 | 0.5999 | 0.674 |
Sunitinib | A09 | 0.8029 | 0.4886 | 0.6699 | 0.6621 |
Sunitinib | A10 | 0.8126 | 0.848 | 0.6087 | 0.6713 |
Sunitinib | A11 | 0.8241 | 0.824 | 0.6408 | 0.6811 |
Sunitinib | A12 | 0.7747 | 0.7898 | 0.5942 | 0.5867 |
Syki | A02 | 0.7682 | 0.7073 | 0.6636 | 0.685 |
Syki | A03 | 0.7224 | 0.7042 | 0.6424 | 0.7116 |
Syki | A04 | 0.7461 | 0.7069 | 0.7908 | 0.7256 |
Syki | A05 | 0.7468 | 0.7182 | 0.6263 | 0.6804 |
Syki | A06 | 0.7381 | 0.7134 | 0.7718 | 0.7154 |
Syki | A07 | 0.7891 | 0.7 | 0.7434 | 0.6479 |
Syki | A08 | 0.7509 | 0.7154 | 0.6903 | 0.6542 |
Syki | A09 | 0.7712 | 0.73 | 0.7357 | 0.6918 |
Syki | A10 | 0.7695 | 0.7531 | 0.7197 | 0.7242 |
Syki | A11 | 0.7360 | 0.7311 | 0.7577 | 0.78 |
Syki | A12 | 0.6717 | 0.6793 | 0.7426 | 0.7123 |
U0126 | A01 | 0.6844 | 0.6178 | - | 0.6486 |
U0126 | A02 | 0.8440 | 0.5545 | 0.5362 | 0.7043 |
U0126 | A03 | 0.8340 | 0.5346 | 0.616 | 0.6881 |
U0126 | A04 | 0.8263 | 0.7079 | 0.6166 | 0.7059 |
U0126 | A05 | 0.8535 | 0.5468 | 0.7091 | 0.7031 |
U0126 | A06 | 0.8199 | 0.5285 | 0.6018 | 0.6874 |
U0126 | A07 | 0.8079 | 0.5304 | 0.5671 | 0.7249 |
U0126 | A08 | 0.8278 | 0.6864 | 0.5359 | 0.6577 |
U0126 | A09 | 0.8331 | 0.5394 | 0.5678 | 0.6967 |
U0126 | A10 | 0.8436 | 0.5593 | 0.6092 | 0.6867 |
U0126 | A11 | 0.7654 | 0.5072 | 0.6374 | 0.6767 |
U0126 | A12 | 0.7227 | 0.6496 | 0.6253 | 0.6281 |
VX680 | A01 | 0.6930 | 0.4818 | 0.6028 | 0.6452 |
VX680 | A02 | 0.7340 | 0.711 | 0.5587 | 0.633 |
VX680 | A03 | 0.7525 | 0.6976 | 0.5663 | 0.7292 |
VX680 | A04 | 0.8127 | 0.6435 | 0.6722 | 0.5954 |
VX680 | A05 | 0.6937 | 0.6742 | 0.7374 | 0.6454 |
VX680 | A06 | 0.7168 | 0.7101 | 0.5769 | 0.6202 |
VX680 | A07 | 0.7663 | 0.4944 | 0.5382 | 0.718 |
VX680 | A08 | 0.7315 | 0.7082 | 0.4753 | 0.6482 |
VX680 | A09 | 0.7703 | 0.7054 | 0.5859 | 0.6722 |
VX680 | A10 | 0.7143 | 0.7137 | 0.6648 | 0.6167 |
VX680 | A11 | 0.7050 | 0.6773 | 0.7269 | 0.6947 |
VX680 | A12 | 0.7852 | 0.7922 | 0.5583 | 0.6808 |
Comments
There are no comments yet.