scholarly journals SSBER: removing batch effect for single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yin Zhang ◽  
Fei Wang
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Downstream Analysis ◽  
Sequencing Platforms

Abstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches. Results In this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches. Conclusions SSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches.

scholarly journals SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection

2019 ◽  
Author(s):  
Yuchen Yang ◽  
Gang Li ◽  
Huijun Qian ◽  
Kirk C. Wilhelmsen ◽  
Yin Shen ◽  
...  
Keyword(s):  
Single Cell ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Cell Type ◽  
Label Information ◽  
Cell Type Specific

AbstractBatch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3, and LIGER. Furthermore, SMNN retains more cell type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841%.Key PointsBatch effect correction has been recognized to be critical when integrating scRNA-seq data from multiple batches due to systematic differences in time points, generating laboratory and/or handling technician(s), experimental protocol, and/or sequencing platform.Existing batch effect correction methods that leverages information from mutual nearest neighbors across batches (for example, implemented in SC3 or Seurat) ignore cell type information and suffer from potentially mismatching single cells from different cell types across batches, which would lead to undesired correction results, especially under the scenario where variation from batch effects is non-negligible compared with biological effects.To address this critical issue, here we present SMNN, a supervised machine learning method that first takes cluster/cell-type label information from users or inferred from scRNA-seq clustering, and then searches mutual nearest neighbors within each cell type instead of global searching.Our SMNN method shows clear advantages over three state-of-the-art batch effect correction methods and can better mix cells of the same cell type across batches and more effectively recover cell-type specific features, in both simulations and real datasets.

scholarly journals MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments

2021 ◽  
Author(s):  
Hanbyeol Kim ◽  
Joongho Lee ◽  
Keunsoo Kang ◽  
Seokhyun Yoon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Expression Level ◽  
Rna Seq ◽  
Cell Type ◽  
Stable Performance ◽  
Downstream Analysis

Abstract Cell type identification is a key step to downstream analysis of single cell RNA-seq experiments. Indispensible information for this is gene expression, which is used to cluster cells, train the model and set rejection thresholds. Problem is they are subject to batch effect arising from different platforms and preprocessing. We present MarkerCount, which uses the number of markers expressed regardless of their expression level to initially identify cell types and, then, reassign cell type in cluster-basis. MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. The performance was evaluated and compared with the existing identifiers, both marker and reference-based, that can be customized with publicly available datasets and marker DB. The results show that MarkerCount provides a stable performance when comparing with other reference-based and marker-based cell type identifiers.

scholarly journals LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals Comprehensive benchmarking of computational deconvolution of transcriptomics data

2020 ◽  
Author(s):  
Francisco Avila Cobos ◽  
José Alquicira-Hernandez ◽  
Joseph Powell ◽  
Pieter Mestdagh ◽  
Katleen De Preter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Factors Affecting ◽  
Marker Selection ◽  
Cell Type Composition ◽  
Type Composition ◽  
Comparable Performance ◽  
Transcriptomics Data ◽  
Combined Impact

AbstractMany computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking.Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference.Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 750
Author(s):  
Olukayode A. Sosina ◽  
Matthew N. Tran ◽  
Kristen R. Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Moderate Correlation ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Reference Dataset ◽  
The Past ◽  
Cell Type Composition ◽  
Type Composition

Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

scholarly journals Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Travis S. Johnson ◽  
Shunian Xiang ◽  
Bryan R. Helm ◽  
Zachary B. Abrams ◽  
Peter Neidecker ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Brain Tissue ◽  
Cell Types ◽  
Brain Atlas ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.

scholarly journals Single Cell Transcriptional Signatures of the Human Placenta in Term and Preterm Parturition

2019 ◽  
Author(s):  
Roger Pique-Regi ◽  
Roberto Romero ◽  
Adi L.Tarca ◽  
Edward D. Sendler ◽  
Yi Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Human Placenta ◽  
Preterm Labor ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Activated T Cell

AbstractMore than 135 million births occur each year; yet, the molecular underpinnings of human parturition in gestational tissues, and in particular the placenta, are still poorly understood. The placenta is a complex heterogeneous organ including cells of both maternal and fetal origin, and insults that disrupt the maternal-fetal dialogue could result in adverse pregnancy outcomes such as preterm birth. There is limited knowledge of the cell type composition and transcriptional activity of the placenta and its compartments during physiologic and pathologic parturition. To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal plate, and chorioamniotic membranes of women with or without labor at term and those with preterm labor. Significant differences in cell type composition and transcriptional profiles were found among placental compartments and across study groups. For the first time, two cell types were identified: 1) lymphatic endothelial decidual cells in the chorioamniotic membranes, and 2) non-proliferative interstitial cytotrophoblasts in the placental villi. Maternal macrophages from the chorioamniotic membranes displayed the largest differences in gene expression (e.g. NFKB1) in both processes of labor; yet, specific gene expression changes were also detected in preterm labor. Importantly, several placental scRNA-seq transcriptional signatures were modulated with advancing gestation in the maternal circulation, and specific immune cell type signatures were increased with labor at term (NK-cell and activated T-cell) and with preterm labor (macrophage, monocyte, and activated T-cell). Herein, we provide a catalogue of cell types and transcriptional profiles in the human placenta, shedding light on the molecular underpinnings and non-invasive prediction of the physiologic and pathologic parturition.One sentence summaryThe common molecular pathway of parturition for both term and preterm spontaneous labor is characterized using single cell gene expression analysis of the human placenta.

scholarly journals Cell-type composition analysis of scRNA-seq data with deep convolution neural network

2020 ◽  
Author(s):  
He Ma ◽  
Zhihao Fang ◽  
Zongbin Liu ◽  
Yan Chen
Keyword(s):  
Single Cell ◽  
Data Matrix ◽  
Composition Analysis ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Markers ◽  
Continuous Increase ◽  
Single Cell Sequencing ◽  
Cell Type Composition ◽  
Type Composition

Abstract BackgroundWith the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and effcient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability.ResultsWe present scCAE, an effective and effcient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks.ConclusionsOur method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Because of the structure of convolution network, scCAE has less time and space overhead than other deep-learning-based methods. Thus, we demonstrate that scCAE is a competitive method for analysis of cell-type composition from scRNA-seq data. We expect that our study can be a stepping stone for further prosperity of single-cell transcriptomics analysis.

scholarly journals THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data

2020 ◽  
Author(s):  
Bryce Rowland ◽  
Ruth Huh ◽  
Zoey Hou ◽  
Ming Hu ◽  
Yin Shen ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Real Data ◽  
Cell Types ◽  
Deconvolution Method ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Organization Studies ◽  
Cell Type Composition ◽  
Type Composition ◽  
Downstream Analysis

AbstractHi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. To effectively leverage Hi-C data for biological insights, we need to control for the confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive real data based simulations to test THUNDER constructed from published single-cell Hi-C (scHi-C) data. THUNDER more accurately estimates the underlying cell type proportions when compared to both supervised and unsupervised deconvolution methods including CIBERSORT, TOAST, and NMF. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimates cell-type-specific chromatin contact profiles for all cell types in bulk Hi-C mixtures. These estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still sparse.

MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments

2021 ◽  
Author(s):  
HanByeol Kim ◽  
Joongho Lee ◽  
Keunsoo Kang ◽  
Seokhyun Yoon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Expression Level ◽  
Rna Seq ◽  
Cell Type ◽  
Stable Performance ◽  
Downstream Analysis

Abstract Cell type identification is a key step to downstream analysis of single cell RNA-seq experiments. Indispensible information for this is gene expression, which is used to cluster cells, train the model and set rejection thresholds. Problem is they are subject to batch effect arising from different platforms and preprocessing. We present MarkerCount, which uses the number of markers expressed regardless of their expression level to initially identify cell types and, then, reassign cell type in cluster-basis. MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. The performance was evaluated and compared with the existing identifiers, both marker and reference-based, that can be customized with publicly available datasets and marker DB. The results show that MarkerCount provides a stable performance when comparing with other reference-based and marker-based cell type identifiers.

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