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MISC: missing imputation for singlecell RNA sequencing data
BMC Systems Biology volume 12, Article number: 114 (2018)
Abstract
Background
Singlecell RNA sequencing (scRNAseq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNAseq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNAseq data, we need to know where the missing data is; how much data is missing; and what are the values of these data.
Methods
To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for singlecell RNAseq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNAseq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zeroinflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements.
Results
We compared the raw data without imputation, the meansmooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CPCML to the BCCML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary.
Conclusions
Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for singlecell RNAseq data.
Background
Single cell genomic analysis has made it possible to understand cellular heterogeneity [1]. Advances in single cell genomics research have also provided unprecedented opportunities in biomedical research where it is important to identify different cell types pertinent to aging and cellular malignancy. Currently, completely eliminating cancer using molecularly targeted therapies is still a distant goal for many types of malignancy. Thus, investigating rare cancer stem cells that are resistant to therapy and studying intratumoral heterogeneity with differential drug responses in distinct cell subpopulations provides a basis for approaching this goal [2]. Over the past 5 years, single cell studies that aimed at the scale and precision of the genomewide profiling of DNA [3], RNA [4], protein [5], epigenetics [6], chromatin accessibility [7], and other molecular events [8] have reached tens of thousands of cells for massively parallel singlecell RNA sequencing [9] and millions of cells for mass cytometry signature protein measurements [10]. Newer and better methods for conducting single cell analyses can capture cell population heterogeneity, including cancer’s heterogeneous nature, and facilitate the discovery of the underlying molecular mechanisms.
Although singlecell RNA sequencing (scRNAseq) data analysis provides us an opportunity to study the heterogeneity of cells and the genes that are differentially expressed across biological conditions, it is a challenging process to perform the analysis. With the fastincrease in scRNAseq data, computational methods need to overcome challenges ranging from handling technical noise to constructing and characterizing cell identities, and to cell lineage analysis through computing highdimensional sparse matrixes. Therefore, innovative, efficient, robust, and scalable computational analysis methods are essential to this new frontier.
Currently, the main obstacle in scRNAseq data analysis, stems from low capture efficiency and stochastic gene expression, which increases gene dropout events in genomewide scRNAseq data. We designate these dropout events as the missing data events of singlecell data. Previous studies indicate that the overall missing rates are consistently high in some singlecell data. For example, in a mouse embryo cell, the missing rate can reach nearly 30%, even after noise reduction [11] With a high fraction of missing data, direct deletion of the missing data can result in a loss of valuable information [12]. To yield better separation of different cell types and reveal new biologically meaningful subpopulations, several publications have reported the missing data as censored data and false negative error [13,14,15]. All these methodologies assume the distribution of the missing data; however, deriving adequate probability distributions is a difficult problem [12]. In 2016, Regev et al. noted that missing data (false negatives), false positives, and data sparsity can strongly affect the estimates of cell heterogeneity, thus new methods as well as the effectively adaption of existing algorithms are required [1]. Additionally, traditional missing data imputation, such as userbased and itembased joint filtering, often assumes that the missing positions are already known in the matrix [16]. Nevertheless, there are still key questions about scRNAseq expression matrices that need to be addressed. Without the missing position information, the aforementioned data imputation methods cannot be utilized.
To solve the key problems in missing value imputation, we proposed a novel model with a datadriven machine learning method, namely, missing imputation on singlecell RNAseq (MISC). The MISC was designed to address three problems: where the missing data is?; how many pieces of data are missing?; and what their values are?. Its initiation involves modeling the problem to transform the missing data imputation into two machine learning problems for detection and imputation of the missing data events. Then, we proposed a model based on classification and regression methods to solve the aforementioned problems. Finally, we evaluated the missing imputation method on two real datasets for studies of differentiating cells and cell  type detection.
Methods
There are four modules (data acquisition, problem modeling, machine learning approach and downstream validation) in our scRNAseq missing data discovery flowchart (Fig. 1). First, the scRNAseq genomewide data are collected. In our experiments, we collected datasets from stem cells of chronic myeloid leukemia [2] from mouse brain cortex and the hippocampus [17], respectively. Then, using problem modeling and machine learning approaches, the RNAseq expression of the missing data can be detected and recovered. For the first problem, where data is missing, we transformed this problem into a binary classification on the RNAseq expression matrix in which each element represented a sample. Then, for the second problem, how many data points are missing, we searched for the intersection of the classification results, between the zeroinflated model (ZIM) and the false negative model (FNC) results. Because the latter two models are not mainly focused on the missing data problem (one is for the identification of the subpopulations of cells, and the other is for the visualization of the singlecell data), they only provide the probability matrixes of the missing data. We selected the top missing elements in the matrixes with a threshold η. In which, η can be computed using the rate of classification results and the counts of the test dataset. Finally, to determine their values, we used a regression model to impute the data in the missing elements.
In the second module, the problem modeling, singlecell missing data was first transformed into a binary classification set. The hypothesis is: if the classifier finds a group of richly expressed genes, whose expression values are equal to zero, than these expressions should be nonzeros and missing values. For the different data, the richly expressed genes can be projected on different gene sets from other genomics data. We used the expression values of these genes as a training set to guide the binary classification model and detect the missing elements in the whole RNAseq matrix. First, to pursue the latent patterns of the missing data, we constructed a training set based on the matrix transformation of richly expressed genes. All the genes are split into richly expressed gene sets and nonrichly expressed gene sets. With these two gene sets, we can construct the richly expressed gene expression matrix as training data and the nonrichly expressed gene expression matrix as test data. The positive set is all the gene expression values larger than zero in a singlecell RNAseq expression matrix and the negative set is all the values equal to zero.
Suppose an element x[i, j] in which X indicates the expression matrix of the richly expressed genes, 0 < i < m, 0 < j < n, where m indicates the number of genes, and n is the number of cells. In generated training set, each element x[i, j] is a sample and the its features j’ are j’ ≠ j, 0 < j’ < n. The missing data value y_{i,j} of a typical gene j in one cell i can be predicted with the gene expression values.
where sgn(•) is the sign function, and F is the machine learning function. Therefore, the training set s has m × n samples, and the feature set f contains n1 features. In our case, we took the mouse cortex and hippocampus data as an example for the process. The training set has 406 genes (m), 3,005 cells (n), 1,220,030 samples (m x n = 406 × 3005) and 3,004 features. Similarly, the test set contains t × n samples and t is the number of nonrichly expressed genes. In the example, the test set has 19,566 genes (m), 3,005 cells (n), 58,795,830 samples and 3,004 features.
In the third module, with the aforementioned problem modeling, it can be seen that the computational complexity reaches O(mn^{2}). Considering the fast development of the single cell experiments, which can perform up to tens of thousands of single cells [1], we employed a large linear classification (LLC) F to discover the missing data, which is of much efficiency for the large data set. The method involves solving the following optimization problem:
where s is the sample, y is the class label for the classification and the expression value for regression, w is the weight vector and w∈R^{n}, C is the penalty factor, C > 0. We adopted two popular binary linear classifiers, named Logistic Regression (LR) and a Support Vector Machine (SVM) with a linear kernel. LR with L2regularization employs the following unconstrained optimization function.
The correspondence dual form is
Then, the problem can be solved with a trust region Newton method [18] or dual coordinate descent method [19] SVM with L2regularization on L2loss uses the following unconstrained optimization function
The dual form is
Then, the problem can be solved with a coordinate descent algorithm [20].
To further validate the missing data and their percentage, we employed our linear classification model, the zeroinflated model [14] and falsenegative curves [15] to construct an ensemble learning method. The zeroinflated model was used as a mixture model for read counts in which the first one is a negative binomial (NB) and the second is a lowmagnitude Poisson. For example, given a single cell c, the reads r_{c} were modeled as a mixture of “dropout” data with Poisson (λ_{0}) and “amplified” components with NB(e), where e is the expected expression magnitude, and the background read frequency for dropout was λ_{0} = 0.1. To fit the mixture model, a subset of genes should be selected. First, given a subpopulation of cells, all the pairs of individual cells (r_{i}, r_{j}) were analyzed with the following model.
Then, a multinomial logistic regression (the mixing parameter m = log(r_{i}) + log(r_{j})) and an expectation–maximization algorithm was used to fit the model. The genes that were assigned to the “amplified” components could be noted, and the set of genes appearing in the amplified components in at least 20% of all the comparisons of the same subpopulation of cells were used to fit the model.
Falsenegative curves employ housekeeping genes to fit a logistic regression function F_{c}(μ) whose odds quantify the cell’s technical detection efficiency [1] In a given gene, its expected expression μ* is conditioned to be detected and 1 F_{c}(μ*) is the missing probability of this gene in cell c.
The differences among the three methods for missing data detection are the training set (subset of genes) and training (fitting) method. First, all three methods need a subset of genes to train or fit the model. From the biology view, the false negative model and large linear classification use the richly expressed genes. However, from the statistical view, the zeroinflated model uses a mixture model of Poisson and negative binomial (NB) to select a subset of genes. Moreover, both the zeroinflated model and false negative model employ logistic regression to fit a model for each cell RNAseq expression value. The large linear classification uses a linear model instead of a logistic function, which is efficient for big data. Therefore, all three methods try to detect the missing data from different views, which satisfied the heterogenous rule of ensemble learning.
After obtaining the ensemble learning and obtaining the missing positions in the RNAseq expression matrix, we employed a linear regression model to recover the missing values. In our experiments, we employed the support vector regression (SVR) model with a linear kernel. The training set is the same as the classification task; however, the label of the training samples using normalized RNAseq expression values, such as reads per kilobase per million (RPKM). For the regression optimization function, we employed three L2regularized methods, which are the dual problem solutions of L1loss support vector regression, the primal problem solution and the dual problem solution of the L2loss support vector regression. The L2regularized SVR is modeled using the following optimization problems:
where p = 1 indicates the L1 loss and p = 2 is the L2 loss, and ε ≥ 0 is the sensitiveness of the loss. The dual forms of the problem are:
where e is the vector of all ones, Q’ = Q + D, Q_{ij} = x_{i}^{T}x_{j}, D is the diagonal matrix and p = 1, D_{ii} = 0; p = 2, D_{ii} = 1/2C; 0 ≤ α_{i}^{+},α_{i}^{+} ≤ U, i = 1,…,l, U=C when p = 1; U = ∞, and when p = 2. We use LIBLINEAR tool to solve this problem [20].
In addition, based on the classification results (which show the missing positions in RNAseq expression matrix), a meansmooth curve with the neighbor cell method on the cell trajectories is also proposed to make a comparison with the MISC. This method recovers the missing values with the expressions of the γ of the previous and following cells (γ = 3 in our experiments).
For the fourth module, we employed the trajectory analysis and subpopulation analysis to directly show the effectiveness of our MISC method.
Two real scRNAseq datasets were used to verify the effectiveness of our model. One is chronic myeloid leukemia (CML) data (Gene Expression Omnibus: GSE76312) [2]. It is used to reveal the heterogeneity of CML stem cells and the identification of subclasses of CML stem cells. It includes five types of stem cells from either patients or normal donors, which are analyzed at different stages of the disease. The other one is the genomewide singlecell RNAseq data of the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells in [17] (Gene Expression Omnibus: GSE60361). It includes 3,005 single cell transcriptomes (19,972 genes) and each RNA molecule was counted using a unique molecular identifier (UMIs) (essentially tags that identify individual molecules) and confirmed by singlemolecule RNA fluorescence in situ hybridization (FISH).
Results
The CML data includes 2,287 stem cells throughout the disease course and 23,384 genes. To analyze the heterogeneity of the stem cells from normal HSCs, we selected 1,102 stem cells without tyrosine kinase inhibitor treatments. Then, the tSNE analysis of these samples was performed using the top 234 differentially expressed genes with a falsediscovery rate (FDR) cutoff of 0.05 and an absolute log fold change cutoff of 1. The training dataset of our MISC machine learning model is based on the richly expressed gene set, which employs human housekeeping genes from reference [21] for CML stem cell data. It contained 38 genes, 1,102 stem cells, and 41,876 samples. The corresponding test dataset includes 196 genes, 1,102 stem cells and 215,992 samples. For the large linear classifiers, we used 5fold cross validation on the training set and achieved a classification accuracy of 0.80. Finally, for the two L2regularization based LLCs, we selected an L2loss support vector machine (with parameter C = 2) due to better accuracy. The missing rate threshold η = 0.35 for the false negative curve (the raw reads count data is not provided, therefore, we only use FNC method to determine the intersection). The final missing rate of CML data (the overlap of the missing data sets between MISC and FNC method) is 13.6%. After several parameter selection experiments, we selected L2loss support vector regression with primal problem solution (parameter C = 0.125) due to its lowest meansquare error among the three regression methods.
For singlecell trajectory analysis, five different types of stem cell chronicphase CMLs (CPCML), normal hematopoietic stem cells (HSCs), preBC samples taken from the patients who were presented in CP (preBC) 12 months and 3 months before transformation to myeloid and lymphoid blast crisis (BC), blast crisis CML (BCCML), K562 human erythroleukemic cell lines derived from a patient in CML blast crisis appear in branches in trajectories during cell development in Fig. 2. Using the top 234 differentially expressed genes, 1102 stem cells without any imputation methods (Fig. 2a) show the branches of CPCML but failed to divide the preBC and BCCML cells. The meansmooth neighbor cells on the trajectory method (Fig. 2b) strips the BCCML from the preBC cells; however, the branches of CPCML have been weakened. The MISC method (Fig. 2c) clearly divides the BCCML and preBC cells. Furthermore, the RNAseq expression data shows a trajectory branch from CPCML to BCCML, which provides direct evidence of the evolution from CP to BC stem cells. In reference [2], a similar result was achieved by clustering, which consists of both of CP and BC stem cells. In addition, normal HSCs are also divided into three branches, which provide further analysis potential. One of them shows a branch mix with normal and preBC stem cells, which can provide clinical research opportunity.
With tSNE analysis, all five different types of stem cells are visualized in Fig. 3. The original distribution of the five cell types is a mess (Fig. 3a), especially for the BCCML type in the red oval. Moreover, the CPCML cells mix with the preBC cells, normal cells and K562 cells. With the meansmooth method with neighbor cells on the trajectory, the split groups in Fig. 3b are clearer than those without missing imputation. However, there are two cells are mixed with normal HSCs. The tSNE visualization on the singlecell RNAseq data using MISC imputation (Fig. 3c) shows the clearest groups among the three figures. Furthermore, the lowest red oval also proves the evolution from CP to BC stem cells as our trajectory analysis. In addition, the MISC imputed singlecell RNAseq data present more compact clusters in Fig. 3c, which provides opportunities for subpopulations and rare cell type analysis on CML stem cells. From Figs. 2 and 3, it can be seen that the MISC data imputation method can help to analyze the trajectory branches of CML stem cells and their subpopulation detection.
For the primary somatosensory cortex and hippocampal CA1 region, the single cell data contains 19,972 genes, including 406 housekeeping genes (using the same list in reference [15]) and 3,005 cells. Therefore, the training set contains 1,220,030 samples and the test set, includes 58,795,830 samples. For the large linear classifier (LLC), we used 5fold cross validation on the training set and achieved 80% accuracy as the CML data. Finally, for the two L2regularization based LLCs, we selected the L2loss Logistic Regression (with parameter C = 104.858) due to better accuracy. The missing rate threshold η = 0.397 for the false negative curve (FNC) and zeroinflated model (ZIM). The final missing rate of the primary somatosensory cortex and hippocampal CA1 region of mouse data is 23.4% (Fig. 4). It is approximately 10% higher than the CML data due to these data using 19, 972 genes without differential gene filters. At last, after several parameter selection experiments, we selected L2loss support vector regression with the primal problem solution (parameter C = 4) due to its lowest meansquare error among the three regression methods.
For singlecell trajectory analysis, seven different types of cells, astrocytesependymal, interneurons, oligodendrocytes, pyramidal SS, endothelialmural, microglia and pyramidal CA1, appeared in branches in trajectories in Fig. 5. Using all the 19,972 genes, 3,005 brain cells without any imputation methods (Fig. 5a) show the branches of astrocytesependymal, interneurons, oligodendrocytes, endothelial−mural and microglia, but failed to divide the pyramidal SS and pyramidal CA1 cells. The meansmooth neighbor cells method (Fig. 2b) strips the pyramidal SS from the pyramidal CA1 cells; however, all the pyramidal CA1 in purple 939 cells stay in one branch. The MISC method (Fig. 2c) clearly divides the pyramidal CA1 into different branches, which is direct evidence that pyramidal CA1 has subpopulations [17]. Furthermore, the RNAseq expression data shows a subbranch at the middle left of Fig. 5a, which provides direct evidence of the subclasses of brain cells.
The complex brain cognitive functions, such as social behaviors and sensorimotor integration, rely on a diverse set of differentiated cells [17]. Therefore, accurate classification of the brain cell types is essential to understand the cognitive functions of the brain. Using MISC, we imputed the scRNAseq data of the primary somatosensory cortex and the hippocampal CA1 region of the mouse brain cells. The imputation results are shown in Fig. 6. The oligodendrocyte cells in the original data without data imputation were divided into two groups (Fig. 6a). Using meansmooth neighbor cells on trajectory imputation, these divided cells that previously were merged together (Fig. 6b); however, it can be seen that these oligodendrocyte cells connect to the other big group, which mainly constitutes interneurons, pyramidal SS, and pyramidal CA1. With MISC, the oligodendrocyte cells became an independent group and its boundary was apparent, although there are few cells in the group that still need further study. The detailed branches in Fig. 5 and the more apparent groups in Fig. 6 indicates that the MISC model can also recover the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells.
Discussion
The dropout events are abundant in the singlecell sequencing data [13, 22]. The missing data imputation is essential for reliable downstream analysis. Most existing data imputation methods are designed to handle bulklevel data. The latent missing data distributions between singlecell and bulklevel data are very distinct. The data missing rate for scRNAseq data is significantly higher than the one for bulklevel data. For example, the missing rate of a scRNAseq dataset can be over 80% [17]. Additionally, the zeros in the scRNAseq matrix either reflect the true biological values or cause by dropout. To accurately impute missing values, we developed a new method that decomposed the data imputation into three subsequent steps: missing position detection, position refinement via ensemble learning, and imputation. Our method was designed for imputing only the expression levels of the dropout genes. To achieve this, we included a refinement step to identify the missing positions with high confidence. The positions that were simultaneously detected by our model and the other two methods [14, 15] were considered as true missing positions. This strategy can improve the specificity of missing value detection. We examined the MISC model using the chronic myeloid leukemia and mouse brain scRNAseq datasets [2, 17]. The experimental evidences suggested that our model could help to optimize the construction of cell trajectory and enable more accurate cell type detection.
The linear classification was used to achieve efficiency in computational time in our method. A more sophisticated model might provide better performance at the cost of computational expense. Hence, the method coupling parallel computing and advanced modeling could help to enhance the efficiency and accuracy of single cell data imputation. Our missing position refinement via ensemble learning may potentially exclude true missing positions. With a better model, we can also address this limitation.
Conclusions
Singlecell RNAseq expression profiling offers a static snapshot of the gene expression, provides estimates of cell heterogeneity and rare cell type detection. Through successfully solving the three problems of missing data, the proposed model MISC can effectively recover the missing values in the scRNAseq data. Regarding the chronic myeloid leukemia data, MISC discovered a trajectory branch from CPCML to BCCML, which provides direct evidence of evolution from CP to BC stem cells. Meanwhile, tSNE on MISC imputed data proves the evolution from CP to BC stem cells as our trajectory analysis and presents more compact clusters. On the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells, it clearly divides the pyramidal CA1 into different branches, it is a direct evidence of pyramidal CA1 has subpopulations. In addition through the use of MISC, oligodendrocyte cells became an independent entity with an apparent boundary. Furthermore, for filtered CML data, the MISC model can present a clear trajectory and cell type classification. For the scRNAseq data with a large number of genes,, MISC can also help us study the cellular heterogeneity. All this indicates that MISC is a robust missing data imputation model for singlecell RNAseq data.
Change history
22 January 2019
It was highlighted that the original article [1] contained a typesetting error in the last name of Allon Canaan. This was incorrectly captured as Allon Canaann in the original article which has since been updated.
Abbreviations
 CML:

Chronic myeloid leukemia
 FDR:

False discover rate
 FNC:

False negative curve
 HSC:

Hematopoietic stem cells
 LLC:

Large linear classification
 LR:

Logistic Regression
 MISC:

Missing imputation on singlecell RNAseq
 NB:

Negative binomial
 RPKM:

Reads per kilobase per million
 scRNAseq:

Singlecell RNA sequencing
 SVM:

Support Vector Machine
 SVR:

Support vector regression
 ZIM:

Zeroinflated model
References
Wagner A, Regev A, Yosef N. Revealing the vectors of cellular identity with singlecell genomics. Nat Biotechnol. 2016;34(11):1145.
Giustacchini A, Thongjuea S, Barkas N, Woll PS, Povinelli BJ, Booth CA, Sopp P, Norfo R, RodriguezMeira A, Ashley N. Singlecell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat Med. 2017;23(6):692–702.
Leung ML, Wang Y, Waters J, Navin NE. SNES: single nucleus exome sequencing. Genome Biol. 2015;16(1):55.
Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smartseq2 for sensitive fulllength transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096.
Bendall SC, Simonds EF, Qiu P, Elad DA, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI. Singlecell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science (New York, NY). 2011;332(6030):687–96.
Guo H, Zhu P, Yan L, Li R, Hu B, Lian Y, Yan J, Ren X, Lin S, Li J. The DNA methylation landscape of human early embryos. Nature. 2014;511(7511):606.
Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, Steemers FJ, Trapnell C, Shendure J. Multiplex singlecell profiling of chromatin accessibility by combinatorial cellular indexing. Science (New York, NY). 2015;348(6237):910–4.
Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P. Singlecell hiC reveals celltocell variability in chromosome structure. Nature. 2013;502(7469):59.
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM. Highly parallel genomewide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–14.
Levine JH, Simonds EF, Bendall SC, Davis KL, Elad DA, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER. Datadriven phenotypic dissection of AML reveals progenitorlike cells that correlate with prognosis. Cell. 2015;162(1):184–97.
Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in singlecell transcriptomics. Nat Rev Genet. 2015;16(3):133.
Buettner F, Moignard V, Göttgens B, Theis FJ. Probabilistic PCA of censored data: accounting for uncertainties in the visualization of highthroughput singlecell qPCR data. Bioinformatics. 2014;30(13):1867–75.
Wang Y, Navin NE. Advances and applications of singlecell sequencing technologies. Mol Cell. 2015;58(4):598–609.
Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to singlecell differential expression analysis. Nat Methods. 2014;11(7):740.
DeTomaso D, Yosef N. FastProject: a tool for lowdimensional analysis of singlecell RNASeq data. BMC bioinformatics. 2016;17(1):315.
Ma H, King I, Lyu MR. Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. Amsterdam, The Netherlands: ACM; 2007. p. 39–46.
Zeisel A, MuñozManchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, Marques S, Munguba H, He L, Betsholtz C. Cell types in the mouse cortex and hippocampus revealed by singlecell RNAseq. Science (New York, NY). 2015;347(6226):1138–42.
Lin CJ, Weng RC, Keerthi SS. Trust region newton methods for largescale logistic regression. In: Proceedings of the 24th international conference on machine learning. Corvalis, Oregon, USA: ACM; 2007. p. 561–8.
Yu HF, Huang FL, Lin CJ. Dual coordinate descent methods for logistic regression and maximum entropy models. Mach Learn. 2011;85(1–2):41–75.
Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. LIBLINEAR: a library for large linear classification. J Mach Learn Res. 2008;9(Aug):1871–4.
Eisenberg E, Levanon EY. Human housekeeping genes are compact. Trends in genetics : TIG. 2003;19(7):362–5.
Grün D, van Oudenaarden A. Design and analysis of singlecell sequencing experiments. Cell. 2015;163(4):799–810.
Acknowledgements
This project was supported in part by the United States NIH Academic Research Enhancement Award 1R15GM114739. WY was supported in part by NIH 5R25DK101408 KUH research traineeship at Yale University School of Medicine. This paper is a journal expansion of the invited lecture notes entitled “Invited talk: Developing deep multisource intelligent learning that facilitates the advancement of single cell genomics research” (DOI: http://ieeexplore.ieee.org/document/8217749/).
Funding
The publication cost of this article was funded by United States National Institutes of Health (NIH) Academic Research Enhancement Award 1R15GM114739.
Availability of data and materials
All the RNAseq data used in this study were public available from the Gene Expression Omnibus.
About this supplement
This article has been published as part of BMC Systems Biology Volume 12 Supplement 7, 2018: From Genomics to Systems Biology. The full contents of the supplement are available online at https://bmcsystbiol.biomedcentral.com/articles/supplements/volume12supplement7.
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MQY and SMW conceived the project and guided the research. RG and MQY designed the project. RG, WY, JZ, AC and MQY implemented the project, performed the research, and analyzed the data. SMW, WY, AC, JZ, RG and MQY discussed the results and RG and MQY drafted the manuscript. All authors agreed the content of the manuscript. All authors read and approved the final manuscript.
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Yang, M.Q., Weissman, S.M., Yang, W. et al. MISC: missing imputation for singlecell RNA sequencing data. BMC Syst Biol 12 (Suppl 7), 114 (2018). https://doi.org/10.1186/s129180180638y
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DOI: https://doi.org/10.1186/s129180180638y