EFFICIENT METHODS FOR CALIBRATION, CLUSTERING, VISUALIZATION AND IMPUTATION OF LARGE scRNA-seq DATA

单细胞rna-seq(scrna-seq)图谱为进行详细的细胞学研究提供了前所未有的机会。 细胞亚群分析。实现scRNA-seq在生物医学研究和生物标记物中的前景 发现需要强大的计算方法来支持罕见和意外表型的检测 细胞反应。当前scRNA-seq的归属、校准、聚类和可视化方法 数据面临着诸如错误输入未表达的基因、线性限制等挑战 去除多变量批次效应的假设，以及聚类和降维的低效 非常大的数据集的方法。我们开发了谱、神经网络和快速多极子方法 (FMM)适合在scRNA-seq和其他高吞吐量的背景下解决这些问题的原型 数据背景，并提议进一步开发和调整这些方法以用于scRNA-seq数据分析。我们的团队 数据分析和计算生物学专家小组目前通过NIH BD2K计划获得资金，以 开发对生物医学科学具有广泛适用性的新型大数据工具和方法。这一努力 证明了神经网络、频谱和调和的极高效可扩展原型的可行性 适用于校准、降维和可视化高维数据的分析技术， 寻找本征状态概率密度，并共同组织细胞、标记和样本。我们建议 在包括基质在内的单细胞RNA-SEQ研究中使用的现有分析方法的实质性进展 结合矩阵补全和统计的稀疏和噪声scRNA-seq数据恢复方法 技术(目标1A)，以及基于我们的无监督MMD-ResNet神经网络原型和 最优运输理论(目标1B)。我们将开发FMM方法的一个变体来加速计算 T分布随机邻近嵌入(t-SNE)可视化技术的排斥项，其 将改进我们目前最快的基于t-SNE FFT的Fit-SNE原型，并开发新的可靠的近似 加速t-SNE等聚类吸引项计算的最近邻方法 算法(目标2A)。我们将进一步开发t-SNE的其他变种，以实现更好的分离 在细胞亚群集群之间(后期夸大)和使用1D t-SNE进行更好的可视化 热图基因-细胞表达(目标2A)。我们将采用我们的高效神经网络方法SpectralNet， 用于计算大型数据集的图的拉普拉斯特征向量。这将使频谱计算成为可能 聚类、扩散图和流形学习在许多scRNA研究中使用，但目前 仅限于中等数量的单细胞(目标2B)。最后，我们将开发一种基于核的差分 描述生物条件差异的丰度算法(目标2C)。我们将领养 适当的抽样方法，以显著改进目前的方法。

Single cell RNA-seq (scRNA-seq) profiling provides an unprecedented opportunity to conduct detailed cellular analysis of cell subpopulations. Fulfilling the promise of scRNA-seq for biomedical studies and biomarker discovery requires robust computational approaches to support detection of rare phenotypes and unanticipated cellular responses. Current approaches for imputation, calibration, clustering and visualizing of scRNA-seq data suffer from challenges such as erroneous imputing of non-expressed genes, limitation of linear assumptions in removal of multivariate batch effects, and inefficiencies of clustering and dimensional reduction methods of very large datasets. We have developed spectral, neural network, and Fast Multipole Methods (FMM) prototypes suitable for addressing these issues in the context of scRNA-seq and other high throughput data contexts and propose to further develop and adapt these methods to scRNA-seq data analysis. Our team of experts on data analytics and computational biology is currently funded through the NIH BD2K initiative to develop novel big data tools and methods that have broad applicability to biomedical science. This effort proved the feasibility of extremely efficient scalable prototypes of neural network, spectral, and harmonic analysis techniques suitable for calibrating, reducing the dimensionality and visualizing high dimensional data, finding intrinsic state-probability densities, and co-organizing cells, markers and samples. We propose substantial advances over existing analytical procedures used in single cell RNA-seq studies including matrix recovery approaches for the sparse and noisy scRNA-seq data by combining matrix completion and statistical techniques (Aim 1A), and calibration based on our unsupervised MMD-ResNet neural network prototype and optimal transport theory (Aim 1B). We will develop a variant of the FMM approach to speed up the calculation of the repulsion term of the t-distributed stochastic neighbor embedding (t-SNE) visualization technique, which will improve our current fastest t-SNE FFT-based FIt-SNE prototype, and develop new reliable approximate nearest neighbors approaches to speed up the computation of the attraction term of t-SNE and other clustering algorithms (Aim 2A). Our additional variants of t-SNE will be further developed to allow better separation between clusters of cell subpopulations (late exaggeration) and better visualization using 1D t-SNE for heatmap gene-cell representation (Aim 2A). We will adapt SpectralNet, our efficient neural network approach, for computing graph Laplacian eigenvectors for large datasets. This will enable computation of spectral clustering, diffusion maps and manifold learning that are utilized in many scRNA studies but are currently limited to a moderate number of single cells (Aim 2B). Finally, we will develop a kernel based differential abundance algorithm to characterize differences between biological conditions (Aim 2C). We will adopt appropriate sampling approaches to significantly improve current methods.