Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses

Abstract

The transcriptome, the complete set of RNA molecules within a cell, plays a critical role in regulating physiological processes. The advent of RNA sequencing (RNA-seq) facilitated by Next Generation Sequencing (NGS) technologies, has revolutionized transcriptome research, providing unique insights into gene expression dynamics. This powerful strategy can be applied at both bulk tissue and single-cell levels. Bulk RNA-seq provides a gene expression profile within a tissue sample. Conversely, single-cell RNA sequencing (scRNA-seq) offers resolution at the cellular level, allowing the uncovering of cellular heterogeneity, identification of rare cell types, and distinction between distinct cell populations. As computational tools, machine learning techniques, and NGS sequencing platforms continue to evolve, the field of transcriptome research is poised for significant advancements. Therefore, to fully harness this potential, a comprehensive understanding of bulk RNA-seq and scRNA-seq technologies, including their advantages, limitations, and computational considerations, is crucial. This review provides a systematic comparison of the computational processes involved in both RNA-seq and scRNA-seq, highlighting their fundamental principles, applications, strengths, and limitations, while outlining future directions in transcriptome research.

Keywords

bioinformatics tools / next generation sequencing / RNA-seq / scRNA-seq / transcriptome

References

[1] Stark R , Grzelak M , Hadfield J . RNA sequencing: the teenage years. Nat Rev Genet. 2019; 20 (11): 631- 56.

[2] Satam H , Joshi K , Mangrolia U , Waghoo S , Zaidi G , Rawool S , et al. Next-generation sequencing technology: current trends and advancements. Biology. 2023; 12 (7): 997.

[3] Hrdlickova R , Toloue M , Tian B . RNA-Seq methods for transcriptome analysis. WIREs RNA. 2017; 8 (1).

[4] Avila Cobos F , Alquicira-Hernandez J , Powell JE , Mestdagh P , De Preter K . Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 2020; 11 (1): 5650.

[5] Wilk AJ , Lee MJ , Khatri P , Blish CA , et al. Single-cell RNA-seq methods to interrogate virus-host interactions. Semin Immunopathol. 2023; 45 (1): 71- 89.

[6] Finkel Y , Mizrahi O , Nachshon A , Weingarten-Gabbay S , Morgenstern D , Yahalom-Ronen Y , et al. The coding capacity of SARS-CoV-2. Nature. 2021; 589 (7840): 125- 30.

[7] Kim D , Lee JY , Yang JS , Kim JW , Kim VN , Chang H . The architecture of SARS-CoV-2 transcriptome. Cell. 2020; 181 (4): 914- 21.e10.

[8] Wen W , Su W , Tang H , Le W , Zhang X , Zheng Y , et al. Immune cell profiling of COVID-19 patients in the recovery stageby single-cell sequencing. Cell Discovery. 2020; 6 (1): 31.

[9] Nguyen KL , Grondin A , Courtois B , Gantet P . Next-generation sequencing accelerates crop gene discovery. Trends Plant Sci. 2019; 24 (3): 263- 74.

[10] Benny J , Pisciotta A , Caruso T , Martinelli F . Identification of key genes and its chromosome regions linked to drought responses in leaves across different crops through meta-analysis of RNA-Seq data. BMC Plant Biol. 2019; 19 (1): 194.

[11] Hasan MS , Feugang JM , Liao SF . A nutrigenomics approach using RNA sequencing technology to study nutrient-gene interactions in agricultural animals. Curr Dev Nutr. 2019; 3 (8): nzz082.

[12] Wickramasinghe S , Cánovas A , Rincón G , Medrano JF . RNA-Sequencing: a tool to explore new frontiers in animal genetics. Livest Sci. 2014; 166: 206- 16.

[13] Jones CH , Androsavich JR , So N , Jenkins MP , MacCormack D , Prigodich A , et al. Breaking the mold with RNA-a "RNAissance" of life science. npj Genomic Medicine. 2024; 9 (1): 2.

[14] He X , Long F , Li Y , Xu Y , Hu L , Yao T , et al. Comparative transcriptome analysis revealing the potential mechanism of low-temperature stress in Machilus microcarpa. Front Plant Sci. 2022; 13.

[15] Upton RN , Correr FH , Lile J , Reynolds GL , Falaschi K , Cook JP , et al. Design, execution, and interpretation of plant RNA-seq analyses. Front Plant Sci. 2023; 14.

[16] Schurch NJ , Schofield P , Gierliński M , Cole C , Sherstnev A , Singh V , et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA. 2016; 22 (6): 839- 51.

[17] Liu Y , Zhou J , White KP . RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2014; 30 (3): 301- 4.

[18] Fang Z , Cui X . Design and validation issues in RNA-seq experiments. Briefings Bioinf. 2011; 12 (3): 280- 7.

[19] Lamarre S , Frasse P , Zouine M , Labourdette D , Sainderichin E , Hu G , et al. Optimization of an RNA-seq differential gene expression analysis depending on biological replicate number and library size. Front Plant Sci. 2018; 9.

[20] Van Den Berge K , Hembach KM , Soneson C , Tiberi S , Clement L , Love MI , et al. RNA sequencing data: hitchhiker's guide to expression analysis. Annual Review of Biomedical Data Science. 2019; 2 (1): 139- 73.

[21] Chomczynski P , Sacchi N . The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nat Protoc. 2006; 1 (2): 581- 5.

[22] Hummon AB , Lim SR , Difilippantonio MJ , Ried T . Isolation and solubilization of proteins after TRI zol ® extraction of RNA and DNA from patient material following prolonged storage. Biotechniques. 2007; 42 (4): 467- 72.

[23] Scholes AN , Lewis JA . Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses. BMC Genom. 2020; 21 (1): 249.

[24] Wang Y , Zhao Y , Bollas A , Au KF . Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021; 39 (11): 1348- 65.

[25] Kajala K , Zipfel C , Ding P , et al. Low-cost and high-throughput RNA-seq library preparation for Illumina sequencing from plant tissue. BIO-PROTOCOL. 2020; 10 (20).

[26] Li F , Wang L , Lan Q , Yang H , Li Y , Liu X , et al. RNA-seq analysis and gene discovery of Andrias davidianus using Illumina short read sequencing. PLoS One. 2015; 10 (4): e0123730.

[27] Kerr SC , Gaiti F , Tanurdzic M . De novo plant transcriptome assembly and annotation using Illumina RNA-seq reads. In: Plant long non-coding RNAs, 1933. New York: Springer; 2019. p. 265- 75.

[28] Stoler N , Nekrutenko A . Sequencing error profiles of Illumina sequencing instruments. NAR Genomics and Bioinformatics. 2021; 3 (1).

[29] Hu T , Chitnis N , Monos D , Dinh A . Next-generation sequencing technologies: an overview. Hum Immunol. 2021; 82 (11): 801- 11.

[30] Zhang G , Sun M , Wang J , Lei M , Zhao D , et al. PacBio full-length cDNA sequencing integrated with RNA-seq reads drastically improves the discovery of splicing transcripts in rice. Plant J. 2019; 97 (2): 296- 305.

[31] Zhang H , Kohnen MV , Prasad KVSK , Gu L , Reddy ASN , et al. Analysis of transcriptome and epitranscriptome in plants using PacBio iso-seq and nanopore-based direct RNA sequencing. Front Genet. 2019; 10.

[32] Lucas MC , Novoa EM . Long-read sequencing in the era of epigenomics and epitranscriptomics. Nat Methods. 2023; 20 (1): 25- 9.

[33] de Cesare M , Wang Y , Piazza P , Sebastiano V , Wang XJ , et al. Comprehensive comparison of pacific Biosciences and Oxford Nanopore technologies and their applications to transcriptome analysis. F1000Research. 2017; 6: 100.

[34] Karst SM , Ziels RM , Kirkegaard RH , Sørensen EA , McDonald D , Zhu Q , et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat Methods. 2021; 18 (2): 165- 9.

[35] Kharchenko PV . The triumphs and limitations of computational methods for scRNA-seq. Nat Methods. 2021; 18 (7): 723- 32.

[36] Squair JW , Gautier M , Kathe C , Anderson MA , James ND , Hutson TH , et al. Confronting false discoveries in single-cell differential expression. Nat Commun. 2021; 12 (1): 5692.

[37] Jiang R , Sun T , Song D , Li JJ . Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol. 2022; 23 (1): 31.

[38] Ziegenhain C , Vieth B , Parekh S , Reinius B , Guillaumet-Adkins A , Smets M , et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017; 65 (4): 631- 43.e4.

[39] Adil A , Kumar V , Jan AT , Asger M . Single-cell transcriptomics: current methods and challenges in data acquisition and analysis. Front Neurosci. 2021; 15.

[40] Jovic D , Liang X , Zeng H , Lin L , Xu F , Luo Y . Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med. 2022; 12 (3).

[41] Netla VR , Shinde H , Kumar G , Dudhate A , Hong JC , Kadam US . A comparative analysis of single-cell transcriptomic technologies in plants and animals. Current Plant Biology. 2023; 35-36: 100289.

[42] Sant P , Rippe K , Mallm JP . Approaches for single-cell RNA sequencing across tissues and cell types. Transcription. 2023; 14 (3-5). 127- 45.

[43] Han X , Xu X , Yang C , Liu G . Microfluidic design in single-cell sequencing and application to cancer precision medicine. Cell Reports Methods. 2023; 3 (9): 100591.

[44] Huang D , Li X , Gou Y , Duan Y , Liu B , et al. Advances in single-cell RNA sequencing and its applications in cancer research. J Hematol Oncol. 2023; 16 (1): 98.

[45] Sasagawa Y , Nikaido I , Hayashi T , Danno H , Uno KD , Imai T , et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 2013; 14 (4): 3097.

[46] Ramsköld D , Luo S , Wang YC , Li R , Deng Q , Faridani OR , et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012; 30 (8): 777- 82.

[47] Picelli S , Björklund ÅK , Faridani OR , Sagasser S , Winberg G , Sandberg R . Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013; 10 (11): 1096- 8.

[48] Hashimshony T , Senderovich N , Avital G , Klochendler A , de Leeuw Y , Anavy L , et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016; 17 (1): 77.

[49] Macosko EZ , Basu A , Satija R , Nemesh J , Shekhar K , Goldman M , et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015; 161 (5): 1202- 14.

[50] Klein AM , Mazutis L , Akartuna I , Tallapragada N , Veres A , Li V , et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015; 161 (5): 1187- 201.

[51] Zheng GXY , Terry JM , Belgrader P , Ryvkin P , Bent ZW , Wilson R , et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017; 8 (1): 14049.

[52] Islam S , Kjällquist U , Moliner A , Zajac P , Fan JB , Lönnerberg P , et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011; 21 (7): 1160- 7.

[53] Chen H , Ye F , Guo G . Revolutionizing immunology with single-cell RNA sequencing. Cell Mol Immunol. 2019; 16 (3): 242- 9.

[54] Ewing B , Hillier L , Wendl MC , Green P . Base-calling of automated sequencer traces using phred I. Accuracy assessment. Genome Res. 1998; 8 (3): 175- 85.

[55] Cock PJA , Fields CJ , Goto N , Heuer ML , Rice PM . The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010; 38 (6): 1767- 71.

[56] Negi A , et al. Applications and challenges of microarray and RNA-sequencing. In: Bioinformatics. Elsevier; 2022. p. 91- 103.

[57] Conesa A , Madrigal P , Tarazona S , Gomez-Cabrero D , Cervera A , McPherson A , et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016; 17 (1): 13.

[58] Andrews S . FastQC: a quality control tool for high throughput sequence data. 2010.

[59] Chanumolu SK , Albahrani M , Otu HH . FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics. BMC Bioinf. 2019; 20 (1): 424.

[60] De Sena Brandine G , Smith AD . Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Research; 2021.

[61] Bolger AM , Lohse M , Usadel B . Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30 (15): 2114- 20.

[62] Bedre R , Avila C , Mandadi K . HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis. Sci Rep. 2021; 11 (1): 18725.

[63] Chen S , Zhou Y , Chen Y , Gu J . fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018; 34 (17): i884- 90.

[64] Bonenfant Q , Noé L , Touzet H . Porechop_ABI: discovering unknown adapters in Oxford Nanopore Technology sequencing reads for downstream trimming. Bioinformatics Advances. 2023; 3 (1).

[65] Haas BJ , Papanicolaou A , Yassour M , Grabherr M , Blood PD , Bowden J , et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013; 8 (8): 1494- 512.

[66] Schulz MH , Zerbino DR , Vingron M , Birney E . Oases: robust De novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012; 28 (8): 1086- 92.

[67] Xie Y , Wu G , Tang J , Luo R , Patterson J , Liu S , et al. SOAPdenovo-Trans: De novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014; 30 (12): 1660- 6.

[68] Bushmanova E , Antipov D , Lapidus A , Prjibelski AD. rnaSPAdes: a De novo transcriptome assembler and its application to RNA-Seq data. GigaScience. 2019; 8 (9).

[69] Liu J , Yu T , Mu Z . TransLiG: a De novo transcriptome assembler that uses line graph iteration. Genome Biol. 2019; 20 (1): 81.

[70] Nip KM , Chiu R , Yang C , Chu J , Mohamadi H , Warren RL , et al. RNA-Bloom enables reference-free and reference-guided sequence assembly for single-cell transcriptomes. Genome Res. 2020; 30 (8): 1191- 200.

[71] Nip KM , Hafezqorani S , Gagalova KK , Chiu R , Yang C , Warren RL , et al. Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2. Nat Commun. 2023; 14 (1): 2940.

[72] Fu S , Ma Y , Yao H , Xu Z , Chen S , Song J , et al. IDP-denovo: de novo transcriptome assembly and isoform annotation by hybrid sequencing. Bioinformatics. 2018; 34 (13): 2168- 76.

[73] Shen W , Le S , Li Y , Hu F . SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One. 2016; 11 (10): e0163962.

[74] Smith-Unna R , Boursnell C , Patro R , Hibberd JM , Kelly S . TransRate: reference-free quality assessment of De novo transcriptome assemblies. Genome Res. 2016; 26 (8): 1134- 44.

[75] Bushmanova E , Antipov D , Lapidus A , Suvorov V , Prjibelski AD . rnaQUAST: a quality assessment tool for de novo transcriptome assemblies. Bioinformatics. 2016; 32 (14): 2210- 2.

[76] Simão FA , Waterhouse RM , Ioannidis P , Kriventseva EV , Zdobnov EM . BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015; 31 (19): 3210- 2.

[77] Manni M , et al. BUSCO: assessing genomic data quality and beyond. Current Protocols; 2021.

[78] Fu L , Niu B , Zhu Z , Wu S , Li W . CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28 (23): 3150- 2.

[79] Langmead B , Salzberg SL . Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9 (4): 357- 9.

[80] Dobin A , Davis CA , Schlesinger F , Drenkow J , Zaleski C , Jha S , et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29 (1): 15- 21.

[81] Kim D , Langmead B , Salzberg SL . HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015; 12 (4): 357- 60.

[82] Pertea M , Pertea GM , Antonescu CM , Chang TC , Mendell JT , Salzberg SL . StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015; 33 (3): 290- 5.

[83] Anders S , Pyl PT , Huber W . HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015; 31 (2): 166- 9.

[84] Liao Y , Smyth GK , Shi W . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30 (7): 923- 30.

[85] Patro R , Duggal G , Love MI , Irizarry RA , Kingsford C . Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017; 14 (4): 417- 9.

[86] Love MI , Huber W , Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15 (12): 550.

[87] Robinson MD , McCarthy DJ , Smyth GK . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26 (1): 139- 40.

[88] Ritchie ME , Phipson B , Wu D , Hu Y , Law CW , Shi W , et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43 (7): e47.

[89] Yu G , Wang LG , Han Y , He QY . clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS A J Integr Biol. 2012; 16 (5): 284- 7.

[90] Ulgen E , Ozisik O , Sezerman OU . pathfindR: an R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front Genet. 2019; 10.

[91] Fang Z , Liu X , Peltz G . GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics. 2023; 39 (1).

[92] Langfelder P , Horvath S . WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008; 9: 559.

[93] Quan Q , Xiong X , Wu S , Yu M . Identification of immune-related key genes in ovarian cancer based on WGCNA. Front Genet. 2021; 12.

[94] Rezaie N , Reese F , Mortazavi A . PyWGCNA: a Python package for weighted gene co-expression network analysis. Bioinformatics. 2023; 39 (7).

[95] Compeau PEC , Pevzner PA , Tesler G . How to apply de Bruijn graphs to genome assembly. Nat Biotechnol. 2011; 29 (11): 987- 91.

[96] Robertson G , Schein J , Chiu R , Corbett R , Field M , Jackman SD , et al. De novo assembly and analysis of RNA-seq data. Nat Methods. 2010; 7 (11): 909- 12.

[97] Li X , Li Z , Li M , Li J , Wang Q , Wang S , et al. Transcriptomic and metabolomic analysis reveals the influence of carbohydrates on lignin degradation mediated by Bacillus amyloliquefaciens. Front Microbiol. 2024; 15.

[98] Wang WX , Yang C , Xiong W , Chen CY . Transcriptome-wide identification of ARF gene family in medicinal plant Polygonatum kingianum and expression analysis of PkARF members in different tissues. Mol Biol Rep. 2024; 51 (1): 648.

[99] Tsissios G , Sallese A , Perez-Estrada JR , Tangeman JA , Chen W , Smucker B , et al. Macrophages modulate fibrosis during newt lens regeneration. Stem Cell Res Ther. 2024; 15 (1): 141.

[100] Wang S , Gribskov M . Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis. Bioinformatics. 2017; 33 (3): 327- 33.

[101] Hölzer M , Marz M . De novo transcriptome assembly:a comprehensive cross-species comparison of short-read RNA-Seq assemblers. GigaScience. 2019; 8 (5).

[102] Ahmadi H , Sheikh-Assadi M , Fatahi R , Zamani Z , Shokrpour M . Optimizing an efficient ensemble approach for high-quality De novo transcriptome assembly of Thymus daenensis. Sci Rep. 2023; 13 (1): 12415.

[103] LeGault LH , Dewey CN . Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs. Bioinformatics. 2013; 29 (18): 2300- 10.

[104] Kerkvliet J , de Fouchier A , van Wijk M , Groot AT . The Bellerophon pipeline, improving de novo transcriptomes and removing chimeras. Ecol Evol. 2019; 9 (18): 10513- 21.

[105] Behera S , et al. Plant transcriptome assembly:review and benchmarking. In:Bioinformatics (eds.Department of clinical and toxicological analyses, school of pharmaceutical sciences, university of Sao Paulo, Sao Paulo, Brazil and Nakaya, H.I.). Exon Publications; 2021. p. 109- 30.

[106] Madritsch S , Burg A , Sehr EM . Comparing de novo transcriptome assembly tools in di-and autotetraploid non-model plant species. BMC Bioinf. 2021; 22 (1): 146.

[107] Góngora-Castillo E , Buell CR . Bioinformatics challenges in De novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. Nat Prod Rep. 2013; 30 (4): 490.

[108] Raghavan V , Kraft L , Mesny F , Rigerte L . A simple guide to de novo transcriptome assembly and annotation. Briefings Bioinf. 2022; 23 (2).

[109] Kriventseva EV , Kuznetsov D , Tegenfeldt F , Manni M , Dias R , Simão FA , et al. OrthoDB v10:sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 2019; 47 (D1): D807- 11.

[110] The RGASP Consortium , et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013.

[111] Brüning RS , Tombor L , Schulz MH , Dimmeler S , John D . Comparative analysis of common alignment tools for single-cell RNA sequencing. GigaScience. 2022; 11.

[112] Andueza A , Kumar S , Kim J , Kang DW , Mumme HL , Perez JI , et al. Endothelial reprogramming by disturbed flow revealed by single-cell RNA and chromatin accessibility study. Cell Rep. 2020; 33 (11): 108491.

[113] Zhang L , He C , Lai Y , Wang Y , Kang L , Liu A , et al. Asymmetric gene expression and cell-type-specific regulatory networks in the root of bread wheat revealed by single-cell multiomics analysis. Genome Biol. 2023; 24 (1): 65.

[114] Libro P , Chiocchio A , De Rysky E , Di Martino J , Bisconti R , Castrignanò T , et al. De novo transcriptome assembly and annotation for gene discovery in Salamandra salamandra at the larval stage. Sci Data. 2023; 10 (1): 330.

[115] Kim D , Pertea G , Trapnell C , Pimentel H , Kelley R , Salzberg SL . TopHat2:accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013; 14 (4): R36.

[116] Trapnell C , Roberts A , Goff L , Pertea G , Kim D , Kelley DR , et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7 (3): 562- 78.

[117] Shao M , Kingsford C . Accurate assembly of transcripts through phase-preserving graph decomposition. Nat Biotechnol. 2017; 35 (12): 1167- 9.

[118] Srivastava A , Malik L , Smith T , Sudbery I , Patro R . Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol. 2019; 20 (1): 65.

[119] Li H , Durbin R . Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010; 26 (5): 589- 95.

[120] Musich R , Cadle-Davidson L , Osier MV . Comparison of short-read sequence aligners indicates strengths and weaknesses for biologists to consider. Front Plant Sci. 2021; 12.

[121] Kaminow B , et al. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data (2021).

[122] Du Y , Huang Q , Arisdakessian C , Garmire LX . Evaluation of STAR and Kallisto on single cell RNA-seq data alignment. G3 Genes|Genomes|Genetics. 2020; 10 (5): 1775- 83.

[123] He D , Zakeri M , Sarkar H , Soneson C , Srivastava A , Patro R . Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data. Nat Methods. 2022; 19 (3): 316- 22.

[124] Bray NL , Pimentel H , Melsted P , Pachter L . Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016; 34 (5): 525- 7.

[125] Smith T , Heger A , Sudbery I . UMI-tools:modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 2017; 27 (3): 491- 9.

[126] Tian L , Su S , Dong X , Amann-Zalcenstein D , Biben C , Seidi A , et al. scPipe:a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput Biol. 2018; 14 (8): e1006361.

[127] Parekh S , Ziegenhain C , Vieth B , Enard W , Hellmann I . zUMIs -a fast and flexible pipeline to process RNA sequencing data with UMIs. GigaScience. 2018; 7 (6).

[128] Wang Z , Hu J , Johnson WE , Campbell JD . scruff:an R/Bioconductor package for preprocessing single-cell RNA-sequencing data. BMC Bioinf. 2019; 20 (1): 222.

[129] Melsted P , Booeshaghi AS , Liu L , Gao F , Lu L , Min KH , et al. Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat Biotechnol. 2021; 39 (7): 813- 8.

[130] Lin P , Troup M , Ho JWK . CIDR:ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 2017; 18 (1): 59.

[131] Lin C , Jain S , Kim H , Bar-Joseph Z . Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res. 2017; 45 (17): e156.

[132] Herman JS , Sagar , Grün D . FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat Methods. 2018; 15 (5): 379- 86.

[133] Li X , Wang K , Lyu Y , Pan H , Zhang J , Stambolian D , et al. Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nat Commun. 2020; 11 (1): 2338.

[134] Miao Z , Deng K , Wang X , Zhang X . DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics. 2018; 34 (18): 3223- 4.

[135] Finak G , McDavid A , Yajima M , Deng J , Gersuk V , Shalek AK , et al. MAST:a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015; 16 (1): 278.

[136] Guo M , Wang H , Potter SS , Whitsett JA , Xu Y . SINCERA:a pipeline for single-cell RNA-seq profiling analysis. PLoS Comput Biol. 2015; 11 (11): e1004575.

[137] Street K , Risso D , Fletcher RB , Das D , Ngai J , Yosef N , et al. Slingshot:cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 2018; 19 (1): 477.

[138] Wolf FA , Hamey FK , Plass M , Solana J , Dahlin JS , Göttgens B , et al. PAGA:graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019; 20 (1): 59.

[139] Cannoodt R , et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. (2016).

[140] Butler A , Hoffman P , Smibert P , Papalexi E , Satija R . Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018; 36 (5): 411- 20.

[141] Wolf FA , Angerer P , Theis FJ . SCANPY:large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19 (1): 15.

[142] Cao J , Spielmann M , Qiu X , Huang X , Ibrahim DM , Hill AJ , et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019; 566 (7745): 496- 502.

[143] Qiu X , Hill A , Packer J , Lin D , Ma YA , Trapnell C . Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017; 14 (3): 309- 15.

[144] Zhang Q , Shi Q , Shao M . Accurate assembly of multi-end RNA-seq data with Scallop2. Nature Computational Science. 2022; 2 (3): 148- 52.

[145] Huang L , Qiao Y , Xu W , Gong L , He R , Qi W , et al. Full-length transcriptome:a reliable alternative for single-cell RNA-seq analysis in the spleen of teleost without reference genome. Front Immunol. 2021; 12.

[146] You Y , Tian L , Su S , Dong X , Jabbari JS , Hickey PF , et al. Benchmarking UMI-based single-cell RNA-seq preprocessing workflows. Genome Biol. 2021; 22 (1): 339.

[147] Osorio D , Cai JJ . Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics. 2021; 37 (7): 963- 7.

[148] García-Ortega LF , Martínez O . How many genes are expressed in a transcriptome?Estimation and results for RNA-seq. PLoS One. 2015; 10 (6): e0130262.

[149] Slovin S , Carissimo A , Panariello F , Bouché V , Gambardella G , et al. Single-cell RNA sequencing analysis:a step-by-step overview. In:RNA bioinformatics, 2284. Springer US; 2021. p. 343- 65.

[150] Liu X , Liu S , Wang J , Zhang N , Zheng X , et al. Normalization methods for the analysis of unbalanced transcriptome data:a review. Front Bioeng Biotechnol. 2019; 7.

[151] Evans C , Hardin J , Stoebel DM . Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Briefings Bioinf. 2018; 19 (5): 776- 92.

[152] Abrams ZB , Johnson TS , Huang K , Payne PRO , Coombes K . A protocol to evaluate RNA sequencing normalization methods. BMC Bioinf. 2019; 20 (S24): 679.

[153] Zhao Y , Konaté MM , Chen L , Das B , Karlovich C , et al. TPM, FPKM, or normalized counts?A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository. J Transl Med. 2021; 19 (1): 269.

[154] Costa-Silva J , Domingues D , Lopes FM . RNA-Seq differential expression analysis:an extended review and a software tool. PLoS One. 2017; 12 (12): e0190152.

[155] Tarazona S , García-Alcalde F , Dopazo J , Ferrer A , Conesa A . Differential expression in RNA-seq:a matter of depth. Genome Res. 2011; 21 (12): 2213- 23.

[156] Tarazona S , Furió-Tarí P , Turrà D , Pietro AD , Nueda MJ , Ferrer A , et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015: gkv711.

[157] Baik B , Yoon S , Nam D . Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data. PLoS One. 2020; 15 (4): e0232271.

[158] The Gene Ontology Consortium . Expansion of the gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017; 45 (D1): D331- 8.

[159] Kanehisa M , Goto S . KEGG:kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28 (1): 27- 30.

[160] Kanehisa M , Sato Y , Kawashima M , Furumichi M , Tanabe M . KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016; 44 (D1): D457- 62.

[161] Kanehisa M , Furumichi M , Sato Y , Kawashima M , Ishiguro-Watanabe M . KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023; 51 (D1): D587- 92.

[162] Kolberg L , Raudvere U , Kuzmin I , Adler P , Vilo J , Peterson H . g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 2023; 51 (W1): W207- 12.

[163] Merico D , Isserlin R , Stueker O , Emili A , Bader GD . Enrichment map:a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010; 5 (11): e13984.

[164] Gaudet P , Dessimoz C . Gene Ontology: pitfalls, biases, and remedies. In: The gene Ontology handbook, 1446. Springer New York; 2017. p. 189- 205.

[165] Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ . Basic local alignment search tool. J Mol Biol. 1990; 215 (3): 403- 10.

[166] Grewal JK , Krzywinski M , Altman N . Markov models-hidden Markov models. Nat Methods. 2019; 16 (9): 795- 6.

[167] Mistry J , Chuguransky S , Williams L , Qureshi M , Salazar G , Sonnhammer ELL , et al. Pfam:the protein families database in 2021. Nucleic Acids Res. 2021; 49 (D1): D412- 9.

[168] Thomas PD , Ebert D , Muruganujan A , Mushayahama T , Albou L , Mi H . Panther:making genome-scale phylogenetics accessible to all. Protein Sci. 2022; 31 (1): 8- 22.

[169] Jamail I , Moussa A . Current state-of-the-art of clustering methods for gene expression data with RNA-seq. In: Travieso-Gonzalez M, editor. Applications of pattern recognition. IntechOpen; 2021.

[170] Liu P , Si Y . Cluster analysis of RNA-sequencing data. In: Datta S, Nettleton D, editors. Statistical analysis of next generation sequencing data. Springer International Publishing; 2014. p. 191- 217.

[171] Makino M , Shimizu K , Kadota K . Enhanced clustering-based differential expression analysis method for RNA-seq data. MethodsX. 2024; 12: 102518.

[172] Montenegro JD . Gene Co-expression network analysis. In: Plant bioinformatics, 2443. Springer US; 2022. p. 387- 404.

[173] Zhang LY , Xing ZT , Chen LQ , Zhang XJ , Fan SJ . Comprehensive time-course transcriptome and Co-expression network analyses identify salt stress responding mechanisms in Chlamydomonas reinhardtii strain GY-D55. Front Plant Sci. 2022; 13.

[174] Yang X , Kui L , Tang M , Li D , Wei K , Chen W , et al. High-throughput transcriptome profiling in drug and biomarker discovery. Front Genet. 2020; 11.

[175] Paananen J , Fortino V . An omics perspective on drug target discovery platforms. Briefings Bioinf. 2020; 21 (6): 1937- 53.

[176] Qie J , Liu Y , Wang Y , Zhang F , Qin Z , Tian S , et al. Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues. Nat Commun. 2022; 13 (1): 7389.

[177] Nugroho ABD , Kim S , Lee SW , Kim DH . Transcriptomic and epigenomic analyses revealed that polycomb repressive complex 2 regulates not only developmental but also stress responsive metabolism in Brassica rapa. Front Plant Sci. 2023; 14.

[178] Destras G , Sabatier M , Bal A , Simon B , Semanas Q , Regue H , et al. Comparison of metatranscriptomics and targeted-sequencing methods for integrative analysis of the whole microbiome. 2024. Preprint at Research Square: rs.3.rs-3961672.

[179] Vallejos CA , Risso D , Scialdone A , Dudoit S , Marioni JC . Normalizing single-cell RNA sequencing data:challenges and opportunities. Nat Methods. 2017; 14 (6): 565- 71.

[180] Luecken MD , Theis FJ . Current best practices in single-cell RNA-seq analysis:a tutorial. Mol Syst Biol. 2019; 15 (6).

[181] Zhang S , Li X , Lin J , Lin Q , Wong KC . Review of single-cell RNA-seq data clustering for cell-type identification and characterization. RNA. 2023; 29 (5): 517- 30.

[182] Lun ATL , McCarthy DJ , Marioni JC . A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research. 2016; 5: 2122.

[183] Lytal N , Ran D , An L . Normalization methods on single-cell RNA-seq data:an empirical survey. Front Genet. 2020; 11.

[184] Heumos L , Schaar AC , Lance C , Litinetskaya A , Drost F , Zappia L , et al. Best practices for single-cell analysis across modalities. Nat Rev Genet. 2023; 24 (8): 550- 72.

[185] Yip SH , Sham PC , Wang J . Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Briefings Bioinf. 2019; 20 (4): 1583- 9.

[186] Xiang R , Wang W , Yang L , Wang S , Xu C , Chen X . A comparison for dimensionality reduction methods of single-cell RNA-seq data. Front Genet. 2021; 12.

[187] Deconinck L , Cannoodt R , Saelens W , Deplancke B , Saeys Y . Recent advances in trajectory inference from single-cell omics data. Current Opinion in Systems Biology. 2021; 27: 100344.

[188] Liu W , Liao X , Yang Y , Lin H , Yeong J , Zhou X , et al. Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data. Nucleic Acids Res. 2022; 50 (12): e72.

[189] Kiselev VY , Kirschner K , Schaub MT , Andrews T , Yiu A , Chandra T , et al. SC3:consensus clustering of single-cell RNA-seq data. Nat Methods. 2017; 14 (5): 483- 6.

[190] Kiselev VY , Andrews TS , Hemberg M . Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet. 2019; 20 (5): 273- 82.

[191] Ding J , Condon A , Shah SP . Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun. 2018; 9 (1): 2002.

[192] Wang D , Gu J . VASC:dimension reduction and visualization of single-cell RNA-seq data by deep variational autoencoder. Dev Reprod Biol. 2018; 16 (5): 320- 31.

[193] Sun S , Zhu J , Ma Y , Zhou X . Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol. 2019; 20 (1): 269.

[194] Lloyd S . Least squares quantization in PCM. IEEE Trans Inf Theor. 1982; 28 (2): 129- 37.

[195] Žurauskienė J , Yau C . pcaReduce:hierarchical clustering of single cell transcriptional profiles. BMC Bioinf. 2016; 17 (1): 140.

[196] Blondel VD , Guillaume JL , Lambiotte R , Lefebvre E . Fast unfolding of communities in large networks. J Stat Mech Theor Exp. 2008; 2008 (10): P10008.

[197] Traag VA , Waltman L , van Eck NJ . From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019; 9 (1): 5233.

[198] Chen L , Wang W , Zhai Y , Deng M . Deep soft K-means clustering with self-training for single-cell RNA sequence data. NAR Genomics and Bioinformatics. 2020; 2 (2).

[199] Grønbech CH , Vording MF , Timshel PN , Sønderby CK , Pers TH , Winther O . scVAE:variational auto-encoders for single-cell gene expression data. Bioinformatics. 2020; 36 (16): 4415- 22.

[200] Das S , Rai A , Rai SN . Differential expression analysis of single-cell RNA-seq data:current statistical approaches and outstanding challenges. Entropy. 2022; 24 (7): 995.

[201] Wang T , Li B , Nelson CE , Nabavi S . Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinf. 2019; 20 (1): 40.

[202] Das S , Rai A , Merchant ML , Cave MC , Rai SN . A comprehensive survey of statistical approaches for differential expression analysis in single-cell RNA sequencing studies. Genes. 2021; 12 (12): 1947.

[203] Ma Y , Sun S , Shang X , Keller ET , Chen M , Zhou X . Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nat Commun. 2020; 11 (1): 1585.

[204] Saelens W , Cannoodt R , Todorov H , Saeys Y . A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019; 37 (5): 547- 54.

[205] Van Den Berge K , Roux de Bézieux H , Street K , Saelens W , Cannoodt R , Saeys Y , et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat Commun. 2020; 11 (1): 1201.

[206] Roux De Bézieux H , Van den Berge K , Street K , Dudoit S . Trajectory inference across multiple conditions with condiments. Nat Commun. 2024; 15 (1): 833.

[207] Bawa G , Liu Z , Yu X , Qin A , Sun X . Single-cell RNA sequencing for plant research:insights and possible benefits. Int J Mol Sci. 2022; 23 (9): 4497.

[208] Zhang Y , Fang X , Zhai N , Yang Z , et al.et al. Transcriptional landscapes of De novo root regeneration from detached Arabidopsis leaves revealed by time-lapse and single-cell RNA sequencing analyses. Plant Communications. 2022; 3 (4): 100306.

[209] Liu Q , Liang Z , Feng D , Jiang S , Wang Y , Du Z , et al. Transcriptional landscape of rice roots at the single-cell resolution. Mol Plant. 2021; 14 (3): 384- 94.

[210] Nguyen TH , Vicidomini R , Choudhury SD , Han TH , Maric D , Brody T , et al. scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development. Dev Cell. 2024; 59 (9): 1210- 30.e9.

[211] Moon J-H , Roh DH , Kwack KH , Lee JH . Bacterial single-cell transcriptomics:recent technical advances and future applications in dentistry. Japanese Dental Science Review. 2023; 59: 253- 62.

[212] Kuchina A , Brettner LM , Paleologu L , Roco CM , Rosenberg AB , Carignano A , et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science. 2021; 371 (6531).

[213] Katz K , Shutov O , Lapoint R , Kimelman M , Brister J , O'Sullivan C . The Sequence Read Archive:a decade more of explosive growth. Nucleic Acids Res. 2022; 50 (D1): D387- 90.

[214] Gan D , Li J . SCIBER:a simple method for removing batch effects from single-cell RNA-sequencing data. Bioinformatics. 2023; 39 (1).

[215] Tran HTN , Ang KS , Chevrier M , Zhang X , Lee NYS , Goh M , et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020; 21 (1): 12.

[216] Haghverdi L , Lun ATL , Morgan MD , Marioni JC . Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018; 36 (5): 421- 7.

[217] Zhang F , Wu Y , Tian W . A novel approach to remove the batch effect of single-cell data. Cell Discovery. 2019; 5 (1): 46.

[218] Hie B , Bryson B , Berger B . Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 2019; 37 (6): 685- 91.

[219] Lin Y , et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. In: Proceedings of the national academy of sciences; 2019.

[220] Barkas N , Petukhov V , Nikolaeva D , Lozinsky Y , Demharter S , Khodosevich K , et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods. 2019; 16 (8): 695- 8.

[221] Polański K , Young MD , Miao Z , Meyer KB , Teichmann SA , Park JE . BBKNN:fast batch alignment of single cell transcriptomes. Bioinformatics. 2020; 36 (3): 964- 5.

[222] Welch JD , Kozareva V , Ferreira A , Vanderburg C , Martin C , Macosko EZ . Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell. 2019; 177 (7): 1873- 87.e17.

[223] Shaham U , Stanton KP , Zhao J , Li H , Raddassi K , Montgomery R , et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics. 2017; 33 (16): 2539- 46.

[224] Lotfollahi M , Wolf FA , Theis FJ . scGen predicts single-cell perturbation responses. Nat Methods. 2019; 16 (8): 715- 21.

[225] Johnson WE , Li C , Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007; 8 (1): 118- 27.

[226] Wang X , Park J , Susztak K , Zhang NR , Li M . Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019; 10 (1): 380.

[227] Racle J , Gfeller D . EPIC: a tool to estimate the proportions of different cell types from bulk gene expression data. In: Bioinformatics for cancer immunotherapy, 2120. New York: Springer US; 2020. 56: p. 233- 48.

[228] Andrade Barbosa B , van Asten SD , Oh JW , Farina-Sarasqueta A , Verheij J , Dijk F , et al. Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data. Nat Commun. 2021; 12 (1): 6106.

[229] Chen C , Leung YY , Ionita M , Wang LS , Li M . Omnibus and robust deconvolution scheme for bulk RNA sequencing data integrating multiple single-cell reference sets and prior biological knowledge. Bioinformatics. 2022; 38 (19): 4530- 6.

[230] Newman AM , Steen CB , Liu CL , Gentles AJ , Chaudhuri AA , Scherer F , et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019; 37 (7): 773- 82.

[231] Sweeney MD , Torre-Healy LA , Hall MA , Chrastecka L , Yurovsky A , et al. FaStaNMF:a fast and stable non-negative matrix factorization for gene expression. IEEE ACM Trans Comput Biol Bioinf. 2024: 1- 12.

[232] Peng XL , Moffitt RA , Torphy RJ , Volmar KE , Yeh JJ . De novo compartment deconvolution and weight estimation of tumor samples using DECODER. Nat Commun. 2019; 10 (1): 4729.

[233] Kang K , Meng Q , Shats I , Umbach DM , Li M , Li Y , et al. CDSeq:a novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. PLoS Comput Biol. 2019; 15 (12): e1007510.

[234] Vandenbon A . Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data. PLoS One. 2022; 17 (1): e0263344.

[235] Soto-Cardinault C , Childs KL , Góngora-Castillo E . Network analysis of publicly available RNA-seq provides insights into the molecular mechanisms of plant defense against multiple fungal pathogens in Arabidopsis thaliana. Genes. 2023; 14 (12): 2223.

[236] Dong L , Kollipara A , Darville T , Zou F , Zheng X . Semi-CAM:a semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information. Sci Rep. 2020; 10 (1): 5434.

[237] Gong T , Szustakowski JD . DeconRNASeq:a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013; 29 (8): 1083- 5.

[238] Jin H , Liu Z . A benchmark for RNA-seq deconvolution analysis under dynamic testing environments. Genome Biol. 2021; 22 (1): 102.

[239] Im Y , Kim Y . A comprehensive overview of RNA deconvolution methods and their application. Mol Cell. 2023; 46 (2): 99- 105.

[240] Moiso E , Farahani A , Marble HD , Hendricks A , Mildrum S , Levine S , et al. Developmental deconvolution for classification of cancer origin. Cancer Discov. 2022; 12 (11): 2566- 85.

[241] Luo Z , Cai Y , Chou CH , Yao M , Pei F , et al.et al. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing. Osteoarthritis Cartilage. 2022; 30 (3): 475- 80.

[242] Sutton GJ , Poppe D , Simmons RK , Walsh K , Nawaz U , Lister R , et al. Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun. 2022; 13 (1): 1358.

[243] Moreno SR . Decrypting plant tissues:from bulk to cell-type transcriptional profiles. Plant Physiol. 2024; 195 (3): 1754- 6.

[244] Vong GYW , McCarthy K , Claydon W , Davis SJ , Redmond EJ , Ezer D . AraLeTA:an Arabidopsis leaf expression atlas across diurnal and developmental scales. Plant Physiol. 2024; 195 (3): 1941- 53.

[245] Marco-Puche G , Lois S , Benítez J , Trivino JC . RNA-seq perspectives to improve clinical diagnosis. Front Genet. 2019; 10.

[246] Yépez VA , Gusic M , Kopajtich R , Mertes C , Smith NH , Alston CL , et al. Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med. 2022; 14 (1): 38.

[247] Zhang W , Yang Y , Zhu X , Yang S , Liao X , Li H , et al. Integrated analyses of metabolomics and transcriptomics reveal the potential regulatory roles of long non-coding RNAs in gingerol biosynthesis. BMC Genom. 2023; 24 (1): 490.

[248] Pardini B , Ferrero G , Tarallo S , Gallo G , Francavilla A , Licheri N , et al. A fecal MicroRNA signature by small RNA sequencing accurately distinguishes colorectal cancers:results from a multicenter study. Gastroenterology. 2023; 165 (3): 582- 99.e8.

[249] Ho PTB , Clark IM , Le LTT . MicroRNA-based diagnosis and therapy. Int J Mol Sci. 2022; 23 (13): 7167.

[250] Alseekh S , Karakas E , Zhu F , Wijesingha Ahchige M , Fernie AR . Plant biochemical genetics in the multiomics era. J Exp Bot. 2023; 74 (15): 4293- 307.

[251] Babu M , Snyder M . Multi-omics profiling for health. Mol Cell Proteomics. 2023; 22 (6): 100561.

[252] Rathour R , Kaushik S , et al. Recent advances in transcriptomics:an assessment of recent progress in fruit plants. In: Kumar A, editor. Omics technologies for sustainable agriculture and global food security, II. Singapore: Springer Singapore; 2021. 10 (1): p. 95- 122.

[253] Sun B , Xun Z , Zhang N , Liu K , Chen X , Zhao H . Single-cell RNA sequencing in cancer research:discovering novel biomarkers and therapeutic targets for immune checkpoint blockade. Cancer Cell Int. 2023; 23 (1): 313.

[254] Xu Z , Wang Y , Sheng K , Rosenthal R , Liu N , Hua X , et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat Commun. 2023; 14 (1): 5130.

[255] Ma P , Amemiya HM , He LL , Gandhi SJ , Nicol R , Bhattacharyya RP , et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell. 2023; 186 (4): 877- 91.e14.

[256] Bossel Ben-Moshe N , et al. Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells. Nat Commun. 2019.

[257] Huang W , Wang D , Yao YF . Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing. Microbial Cell. 2021; 8 (9): 208- 22.

[258] Zhang Y , Gao J , Huang Y , Wang J . Recent developments in single-cell RNA-seq of microorganisms. Biophys J. 2018; 115 (2): 173- 80.

[259] Zhang Z , Cui F , Wang C , Zhao L , Zou Q . Goals and approaches for each processing step for single-cell RNA sequencing data. Briefings Bioinf. 2021; 22 (4): bbaa314.

[260] Elmentaite R , Domínguez Conde C , Yang L , Teichmann SA . Single-cell atlases:shared and tissue-specific cell types across human organs. Nat Rev Genet. 2022; 23 (7): 395- 410.

[261] Cao J , Packer JS , Ramani V , Cusanovich DA , Huynh C , Daza R , et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017; 357 (6352): 661- 7.

[262] Cusanovich DA , Hill AJ , Aghamirzaie D , Daza RM , Pliner HA , Berletch JB , et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell. 2018; 174 (5): 1309- 24.e18.

[263] Sikkema L , Ramírez-Suástegui C , Strobl DC , Gillett TE , Zappia L , Madissoon E , et al. An integrated cell atlas of the lung in health and disease. Nat Med. 2023; 29 (6): 1563- 77.

[264] Zappia L , Theis FJ . Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape. Genome Biol. 2021; 22 (1): 301.

[265] Macaulay IC , Haerty W , Kumar P , Li YI , Hu TX , Teng MJ , et al. G&T-seq:parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015; 12 (6): 519- 22.

[266] Dey SS , Kester L , Spanjaard B , Bienko M , van Oudenaarden A . Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol. 2015; 33 (3): 285- 9.

[267] Rodriguez-Meira A , O'Sullivan J , Rahman H , Mead AJ . TARGET-seq:a protocol for high-sensitivity single-cell mutational analysis and parallel RNA sequencing. STAR Protocols. 2020; 1 (3): 100125.

[268] Hu Y , An Q , Guo Y , Zhong J , Fan S , Rao P , et al. Simultaneous profiling of mRNA transcriptome and DNA methylome from a single cell. In: Single cell methods, 1979. New York: Springer New York; 2019. p. 363- 77.

[269] Adey AC . Integration of single-cell genomics datasets. Cell. 2019; 177 (7): 1677- 9.

[270] Song Y , Xu X , Wang W , Tian T , Zhu Z , Yang C . Single cell transcriptomics:moving towards multi-omics. The Analyst. 2019; 144 (10): 3172- 89.

[271] Ma A , McDermaid A , Xu J , Chang Y , Ma Q . Integrative methods and practical challenges for single-cell multi-omics. Trends Biotechnol. 2020; 38 (9): 1007- 22.

[272] Grandi FC , Modi H , Kampman L , Corces MR . Chromatin accessibility profiling by ATAC-seq. Nat Protoc. 2022; 17 (6): 1518- 52.

[273] Ranzoni AM , Tangherloni A , Berest I , Riva SG , Myers B , Strzelecka PM , et al. Integrative single-cell RNA-seq and ATAC-seq analysis of human developmental hematopoiesis. Cell Stem Cell. 2021; 28 (3): 472- 87.e7.

[274] Xing QR , Farran CAE , Zeng YY , Yi Y , Warrier T , Gautam P , et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 2020; 30 (7): 1027- 39.

[275] Stoeckius M , Hafemeister C , Stephenson W , Houck-Loomis B , Chattopadhyay PK , Swerdlow H , et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017; 14 (9): 865- 8.

[276] Peterson VM , Zhang KX , Kumar N , Wong J , Li L , Wilson DC , et al. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017; 35 (10): 936- 9.

[277] Baysoy A , Bai Z , Satija R , Fan R . The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol. 2023; 24 (10): 695- 713.

[278] Sun G , Li Z , Rong D , Zhang H , Shi X , Yang W , et al. Single-cell RNA sequencing in cancer:applications, advances, and emerging challenges. Molecular Therapy-Oncolytics. 2021; 21: 183- 206.

[279] Gao GF , Parker JS , Reynolds SM , Silva TC , Wang LB , Zhou W , et al. Before and after:comparison of legacy and harmonized TCGA genomic data commons'data. Cell Systems. 2019; 9 (1): 24- 34.e10.

[280] Pan L , Parini P , Tremmel R , Loscalzo J , Lauschke VM , Maron BA , et al. Single Cell Atlas:a single-cell multi-omics human cell encyclopedia. Genome Biol. 2024; 25 (1): 104.

[281] Wang R , Zhang P , Wang J , Ma L , E W , Suo S , et al. Construction of a cross-species cell landscape at single-cell level. Nucleic Acids Res. 2023; 51 (2): 501- 16.

[282] Li H , Janssens J , De Waegeneer M , Kolluru SS , Davie K , Gardeux V , et al. Fly Cell Atlas:a single-nucleus transcriptomic atlas of the adult fruit fly. Science. 2022; 375 (6584).

[283] Marx V . Method of the Year:spatially resolved transcriptomics. Nat Methods. 2021; 18 (1): 9- 14.

[284] Zhuang X . Spatially resolved single-cell genomics and transcriptomics by imaging. Nat Methods. 2021; 18 (1): 18- 22.

[285] Larsson L , Frisén J , Lundeberg J . Spatially resolved transcriptomics adds a new dimension to genomics. Nat Methods. 2021; 18 (1): 15- 8.

[286] Rao A , Barkley D , França GS , Yanai I . Exploring tissue architecture using spatial transcriptomics. Nature. 2021; 596 (7871): 211- 20.

[287] Moriel N , Senel E , Friedman N , Rajewsky N , Karaiskos N , Nitzan M . NovoSpaRc:flexible spatial reconstruction of single-cell gene expression with optimal transport. Nat Protoc. 2021; 16 (9): 4177- 200.

[288] Biancalani T , Scalia G , Buffoni L , Avasthi R , Lu Z , Sanger A , et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods. 2021; 18 (11): 1352- 62.

[289] Wei R , He S , Bai S , Sei E , Hu M , Thompson A , et al. Spatial charting of single-cell transcriptomes in tissues. Nat Biotechnol. 2022; 40 (8): 1190- 9.

[290] Vahid MR , Brown EL , Steen CB , Zhang W , Jeon HS , Kang M , et al. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat Biotechnol. 2023; 41 (11): 1543- 8.

[291] Kleshchevnikov V , Shmatko A , Dann E , Aivazidis A , King HW , Li T , et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022; 40 (5): 661- 71.

[292] Elosua-Bayes M , Nieto P , Mereu E , Gut I , Heyn H . SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 2021; 49 (9): e50.

[293] Wang X , Allen WE , Wright MA , Sylwestrak EL , Samusik N , Vesuna S , et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018; 361 (6400).

[294] Kebschull JM , Richman EB , Ringach N , Friedmann D , Albarran E , Kolluru SS , et al. Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set. Science. 2020; 370 (6523).

[295] Alon S , Goodwin DR , Sinha A , Wassie AT , Chen F , Daugharthy ER , et al. Expansion sequencing:spatially precise in situ transcriptomics in intact biological systems. Science. 2021; 371 (6528).

[296] Maynard, KR , Collado-Torres, L , Weber, LM , Uytingco, C , Barry, BK , Williams, SR , et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neuosci. 2021; 24 (3): 425- 436.

[297] Chen, H , Murray, E , Laumas, A , Li, J , Nie, X , Hotaling, J , et al. Dissecting mammalian spermatogenesis using spatial transcriptomics. Cell Rep. 2021; 37 (5): 109915.

[298] Asp M , Giacomello S , Larsson L , Wu C , Fürth D , Qian X , et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. 2019; 179 (7): 1647- 60.e19.

[299] Fawkner-Corbett D , Antanaviciute A , Parikh K , Jagielowicz M , Gerós AS , Gupta T , et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell. 2021; 184 (3): 810- 26.e23.

[300] Giacomello S , Salmén F , Terebieniec BK , Vickovic S , Navarro JF , Alexeyenko A , et al. Spatially resolved transcriptome profiling in model plant species. Nat Plants. 2017; 3 (6): 17061.

[301] Cillo AR , Kürten CH , Tabib T , Qi Z , Onkar S , Wang T , et al. Immune landscape of viral-and carcinogen-driven head and neck cancer. Immunity. 2020; 52 (1): 183- 99.e9.

[302] Efremova M , Vento-Tormo M , Teichmann SA , Vento-Tormo R . CellPhoneDB:inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020; 15 (4): 1484- 506.

[303] Jin S , Guerrero-Juarez CF , Zhang L , Chang I , Ramos R , Kuan CH , et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021; 12 (1): 1088.

[304] Wilk AJ , Shalek AK , Holmes S , Blish CA . Comparative analysis of cell-cell communication at single-cell resolution. Nat Biotechnol. 2024; 42 (3): 470- 83.

[305] Armingol E , Officer A , Harismendy O , Lewis NE . Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet. 2021; 22 (2): 71- 88.

[306] Rustici G , Williams E , Barzine M , Brazma A , Bumgarner R , Chierici M , et al. Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing. 2021. Preprint at bioRxiv:2020.12.31.425022.

[307] Simoneau J , Dumontier S , Gosselin R , Scott MS . Current RNA-seq methodology reporting limits reproducibility. Briefings Bioinf. 2021; 22 (1): 140- 5.

RIGHTS & PERMISSIONS

The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.