Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies

被引:0
|
作者
Papiez, Anna [1 ]
Pioch, Jonathan [2 ]
Mollenkopf, Hans-Joachim [3 ]
Corleis, Bjoern [2 ]
Dorhoi, Anca [2 ]
Polanska, Joanna [1 ]
机构
[1] Silesian Tech Univ, Dept Data Sci & Engn, Gliwice, Poland
[2] Friedrich Loeffler Inst, Inst Immunol, Greifswald, Germany
[3] Max Planck Inst Infect Biol, Dept Immunol, Berlin, Germany
来源
PLOS ONE | 2024年 / 19卷 / 06期
关键词
EXPRESSION;
D O I
10.1371/journal.pone.0305874
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Single-cell transcription analysis of Plasmodium vivax blood-stage parasites identifies stage- and species-specific profiles of expression
    Sa, Juliana M.
    Cannon, Matthew V.
    Caleon, Ramoncito L.
    Wellems, Thomas E.
    Serre, David
    PLOS BIOLOGY, 2020, 18 (05)
  • [42] Comparative study on differential expression analysis methods for single-cell RNA sequencing data with small biological replicates: Based on single-cell transcriptional data of PBMCs from COVID-19 severe patients
    Xue, Jie
    Zhou, Xinfan
    Yang, Jing
    Niu, Adan
    PLOS ONE, 2024, 19 (03):
  • [43] Advances in sequencing-based studies of microDNA and ecDNA: Databases, identification methods, and integration with single-cell analysis
    Jiang, Rong
    Yang, Manqiu
    Zhang, Shufan
    Huang, Moli
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 3073 - 3080
  • [44] Single-cell multiple gene expression analysis based on single-molecule-detection microarray assay for multi-DNA determination
    Li, Lu
    Wang, Xianwei
    Zhang, Xiaoli
    Wang, Jinxing
    Jin, Wenrui
    ANALYTICA CHIMICA ACTA, 2015, 854 : 122 - 128
  • [45] Genome-wide assessment of population structure and genetic diversity of Eucalyptus urophylla based on a multi-species single-nucleotide polymorphism chip analysis
    Huixiao Yang
    Huanqin Liao
    Weihua Zhang
    Wen Pan
    Tree Genetics & Genomes, 2020, 16
  • [46] Single-cell multi-omic analysis profiles defective genome activation and epigenetic reprogramming associated with human pre-implantation embryo arrest
    Mora, Jose Ramon Hernandez
    Buhigas, Claudia
    Clark, Stephen
    Bonilla, Raquel Del Gallego
    Daskeviciute, Dagne
    Monteagudo-Sanchez, Ana
    Poo-Llanillo, Maria Eugenia
    Medrano, Jose Vicente
    Simon, Carlos
    Meseguer, Marcos
    Kelsey, Gavin
    Monk, David
    CELL REPORTS, 2023, 42 (02):
  • [47] Single-Cell Multiomic Analysis of the Effect of GIP and GIP-1 Mono- and Multi-agonism on the Hypothalamus and Hindbrain
    Gutgesell, Robert M.
    Maity-Kumar, Gandhari
    Caceres, Cristina Garcia
    Tschop, Matthias H.
    Mueller, Timo D.
    DIABETES, 2024, 73
  • [48] Single-cell multi-omic analysis reveals serum amyloid A3 restrains alternative activations in macrophages derived from different origins
    Lin, Jian-Da
    Chou, Tzu-Yin
    Ye, Yu-Zhen
    Lin, I-Jung
    Lien, Chia-Jung
    Wang, Zhih-Yuan
    Yang, Chen-Hsuan
    Chao, Pei-An
    Chen, Yen-Ting
    Loke, P'ng
    JOURNAL OF IMMUNOLOGY, 2023, 210 (01):
  • [49] 10X Genomics-Based Single-Cell RNA-Seq Analysis Identifies a Transcriptional Landscape of Inflammation and Fibrosis in Lupus Nephritis
    Suryawanshi, Hemant
    Der, Evan
    Morozov, Pavel
    Clancy, Robert M.
    Goilav, Beatrice
    Belmont, H. Michael
    Izmirly, Peter M.
    Bornkamp, Nicole
    Jordan, Nicole
    Wu, Ming
    James, Judith A.
    Guthridge, Joel M.
    Raychaudhuri, Soumya
    Buyon, Jill P.
    Putterman, Chaim
    Tuschl, Thomas
    ARTHRITIS & RHEUMATOLOGY, 2018, 70
  • [50] Single-Cell Multi-Omics Reveals Anticipated Erythroid Differentiation and Transcriptional Regulation in Differentiation Trajectories of SF3B1-JAK2/MPL Mutated Cells in MDS/MPN-RS-T
    Zampini, Matteo
    Todisco, Gabriele
    Chiodi, Alice
    Chiereghin, Chiara
    Saba, Elena
    Gandolfi, Francesco
    Ventura, Denise
    Pinocchio, Nicole
    Crisafulli, Laura
    Brindisi, Matteo
    Campagna, Alessia
    Termanini, Alberto
    Lanino, Luca
    Ubezio, Marta
    Maggioni, Giulia
    Russo, Antonio
    Buizza, Alessandro
    Sauta, Elisabetta
    Asti, Gianluca
    D'Amico, Saverio
    Vallelonga, Veronica
    Ghisletti, Serena M. L.
    Mosca, Ettore
    Ficara, Francesca
    Della Porta, Matteo Giovanni
    BLOOD, 2024, 144 : 3194 - 3195