Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data

Abstract

Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.

Graphical abstract

Keywords

single-cell RNA-sequencing / feature selection / principal component analysis / highly variable gene detection / background noise / clustering analysis

References

[1] Oshlack A , Robinson M D , Young M D . From RNA-seq reads to differential expression results. Genome Biology, 2010, 11( 12): 220–

[2] Ye X , Zhang W , Sakurai T . Adaptive unsupervised feature learning for gene signature identification in non-small-cell lung cancer. IEEE Access, 2020, 8 : 154354– 154362

[3] Ozsolak F , Milos P M . RNA sequencing: advances, challenges and opportunities. Nature Reviews Genetics, 2011, 12( 2): 87– 98

[4] Wagner A , Regev A , Yosef N . Revealing the vectors of cellular identity with single-cell genomics. Nature Biotechnology, 2016, 34( 11): 1145– 1160

[5] Kiselev V Y , Andrews T S , Hemberg M . Challenges in unsupervised clustering of single-cell RNA-seq data. Nature Reviews Genetics, 2019, 20( 5): 273– 282

[6] Ye X , Zhang W , Futamura Y , Sakurai T . Detecting interactive gene groups for single-cell RNA-Seq data based on co-expression network analysis and subgraph learning. Cells, 2020, 9( 9): 1938–

[7] Ye X , Sakurai T . Robust similarity measure for spectral clustering based on shared neighbors. ETRI Journal, 2016, 38( 3): 540– 550

[8] Emmert-Streib F , Dehmer M , Haibe-Kains B . Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Frontiers in Cell and Developmental Biology, 2014, 2 : 38–

[9] Thompson D , Regev A , Roy S . Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annual Review of Cell and Developmental Biology, 2015, 31 : 399– 428

[10] Ye X , Sakurai T . Spectral clustering with adaptive similarity measure in Kernel space. Intelligent Data Analysis, 2018, 22( 4): 751– 765

[11] Yip S H , Sham P C , Wang J . Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Briefings in Bioinformatics, 2019, 20( 4): 1583– 1589

[12] Finak G , McDavid A , Yajima M , Deng J , Gersuk V , Shalek A K , Slichter C K , Miller H W , McElrath M J , Prlic M , Linsley P S , Gottardo R . MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology, 2015, 16 : 278–

[13] Yip S H , Wang P , Kocher J P A , Sham P C , Wang J . Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Research, 2017, 45( 22): e179–

[14] Law C W , Chen Y , Shi W , Smyth G K . Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014, 15( 2): R29–

[15] Vallejos C A , Marioni J C , Richardson S . BASiCS: bayesian analysis of single-cell sequencing data. PLoS Computational Biology, 2015, 11( 6): e1004333–

[16] Lun A T L , Bach K , Marioni J C . Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology, 2016, 17 : 75–

[17] Lun A T L , McCarthy D J , Marioni J C . A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research, 2016, 5 : 2122–

[18] Brennecke P , Anders S , Kim J K , Kolodziejczyk A A , Zhang X W , Proserpio V , Baying B , Benes V , Teichmann S A , Marioni J C , Heisler M G . Accounting for technical noise in single-cell RNA-seq experiments. Nature Methods, 2013, 10( 11): 1093– 1095

[19] Chen H I H , Jin Y , Huang Y , Chen Y . Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics, 2016, 17( S7): 508–

[20] Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning, 2002, 46(1−3): 389−422

[21] Díaz-Uriarte R , de Andrés S A . Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 2006, 7 : 3–

[22] Satija R , Farrell J A , Gennert D , Schier A F , Regev A . Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 2015, 33( 5): 495– 502

[23] Stuart T , Butler A , Hoffman P , Hafemeister C , Papalexi E , Mauck III W M , Hao Y , Stoeckius M , Smibert P , Satija R . Comprehensive integration of single-cell data. Cell, 2019, 177( 7): 1888– 1902.e21

[24] Mayer C , Hafemeister C , Bandler R C , Machold R , Brito R B , Jaglin X , Allaway K , Butler A , Fishell G , Satija R . Developmental diversification of cortical inhibitory interneurons. Nature, 2018, 555( 7697): 457– 462

[25] Hotelling H . Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 1993, 24( 6): 417– 441

[26] Jolliffe I T. Principal Component Analysis. Springer, 1986

[27] Pearson K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. In: Kotz S, Johnson N L, eds. Breakthroughs in Statistics. New York: Springer, 1992

[28] Heckert N A, Filliben J J. NIST/SEMATECH e-Handbook of statistical methods; Chapter 1: Exploratory Data Analysis. 2003

[29] Gierahn T M , Wadsworth II M H , Hughes T K , Bryson B D , Butler A , Satija R , Fortune S , Love J C , Shalek A K . Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nature Methods, 2017, 14( 4): 395– 398

[30] Liu A H , Nowakowski T J , Pollen A A , Lui J H , Horlbeck M A , Attenello F J , He D , Weissman J S , Kriegstein A R , Diaz A A , Lim D A . Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biology, 2016, 17 : 67–

[31] Pollen A A , Nowakowski T J , Shuga J , Wang X , Leyrat A A . Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nature Biotechnology, 2014, 32( 10): 1053– 1058

[32] Hafemeister C , Satija R . Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 2019, 20( 1): 296–

[33] Rand W M . Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 1971, 66( 336): 846– 850

[34] McInnes L , Healy J , Saul N , Großberger L . UMAP: uniform manifold approximation and projection. The Journal of Open Source Software, 2018, 3( 29): 861–

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Higher Education Press 2021