Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.
{"title":"IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model","authors":"Dongjoo Lee, Jeongbin Park, Seungho Cook, Seongjin Yoo, Daeseung Lee, Hongyoon Choi","doi":"10.1186/s13059-024-03380-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03380-x","url":null,"abstract":"Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"409 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1186/s13059-024-03434-0
Leilei Wu, Shutan Jiang, Meisong Shi, Tanglong Yuan, Yaqin Li, Pinzheng Huang, Yingqi Li, Erwei Zuo, Changyang Zhou, Yidi Sun
The safety of CRISPR-based gene editing methods is of the utmost priority in clinical applications. Previous studies have reported that Cas9 cleavage induced frequent aneuploidy in primary human T cells, but whether cleavage-mediated editing of base editors would generate off-target structure variations remains unknown. Here, we investigate the potential off-target structural variations associated with CRISPR/Cas9, ABE, and CBE editing in mouse embryos and primary human T cells by whole-genome sequencing and single-cell RNA-seq analyses. The results show that both Cas9 and ABE generate off-target structural variations (SVs) in mouse embryos, while CBE induces rare SVs. In addition, off-target large deletions are detected in 32.74% of primary human T cells transfected with Cas9 and 9.17% of cells transfected with ABE. Moreover, Cas9-induced aneuploid cells activate the P53 and apoptosis pathways, whereas ABE-associated aneuploid cells significantly upregulate cell cycle-related genes and are arrested in the G0 phase. A percentage of 16.59% and 4.29% aneuploid cells are still observable at 3 weeks post transfection of Cas9 or ABE. These off-target phenomena in ABE are universal as observed in other cell types such as B cells and Huh7. Furthermore, the off-target SVs are significantly reduced in cells treated with high-fidelity ABE (ABE-V106W). This study shows both CRISPR/Cas9 and ABE induce off-target SVs in mouse embryos and primary human T cells, raising an urgent need for the development of high-fidelity gene editing tools.
{"title":"Adenine base editors induce off-target structure variations in mouse embryos and primary human T cells","authors":"Leilei Wu, Shutan Jiang, Meisong Shi, Tanglong Yuan, Yaqin Li, Pinzheng Huang, Yingqi Li, Erwei Zuo, Changyang Zhou, Yidi Sun","doi":"10.1186/s13059-024-03434-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03434-0","url":null,"abstract":"The safety of CRISPR-based gene editing methods is of the utmost priority in clinical applications. Previous studies have reported that Cas9 cleavage induced frequent aneuploidy in primary human T cells, but whether cleavage-mediated editing of base editors would generate off-target structure variations remains unknown. Here, we investigate the potential off-target structural variations associated with CRISPR/Cas9, ABE, and CBE editing in mouse embryos and primary human T cells by whole-genome sequencing and single-cell RNA-seq analyses. The results show that both Cas9 and ABE generate off-target structural variations (SVs) in mouse embryos, while CBE induces rare SVs. In addition, off-target large deletions are detected in 32.74% of primary human T cells transfected with Cas9 and 9.17% of cells transfected with ABE. Moreover, Cas9-induced aneuploid cells activate the P53 and apoptosis pathways, whereas ABE-associated aneuploid cells significantly upregulate cell cycle-related genes and are arrested in the G0 phase. A percentage of 16.59% and 4.29% aneuploid cells are still observable at 3 weeks post transfection of Cas9 or ABE. These off-target phenomena in ABE are universal as observed in other cell types such as B cells and Huh7. Furthermore, the off-target SVs are significantly reduced in cells treated with high-fidelity ABE (ABE-V106W). This study shows both CRISPR/Cas9 and ABE induce off-target SVs in mouse embryos and primary human T cells, raising an urgent need for the development of high-fidelity gene editing tools.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1186/s13059-024-03428-y
Eloise Withnell, Maria Secrier
Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.
{"title":"SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer","authors":"Eloise Withnell, Maria Secrier","doi":"10.1186/s13059-024-03428-y","DOIUrl":"https://doi.org/10.1186/s13059-024-03428-y","url":null,"abstract":"Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"71 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1186/s13059-024-03426-0
Nam D. Nguyen, Lorena Rosas, Timur Khaliullin, Peiran Jiang, Euxhen Hasanaj, Jose A. Ovando-Ricardez, Marta Bueno, Irfan Rahman, Gloria S. Pryhuber, Dongmei Li, Qin Ma, Toren Finkel, Melanie Königshoff, Oliver Eickelberg, Mauricio Rojas, Ana L. Mora, Jose Lugo-Martinez, Ziv Bar-Joseph
The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.
{"title":"scDOT: optimal transport for mapping senescent cells in spatial transcriptomics","authors":"Nam D. Nguyen, Lorena Rosas, Timur Khaliullin, Peiran Jiang, Euxhen Hasanaj, Jose A. Ovando-Ricardez, Marta Bueno, Irfan Rahman, Gloria S. Pryhuber, Dongmei Li, Qin Ma, Toren Finkel, Melanie Königshoff, Oliver Eickelberg, Mauricio Rojas, Ana L. Mora, Jose Lugo-Martinez, Ziv Bar-Joseph","doi":"10.1186/s13059-024-03426-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03426-0","url":null,"abstract":"The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"10 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1186/s13059-024-03429-x
Jiyuan Yang, Lu Wang, Lin Liu, Xiaoqi Zheng
The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.
{"title":"GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data","authors":"Jiyuan Yang, Lu Wang, Lin Liu, Xiaoqi Zheng","doi":"10.1186/s13059-024-03429-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03429-x","url":null,"abstract":"The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"3 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1186/s13059-024-03417-1
Kazimierz Oksza-Orzechowski, Edwin Quinten, Shadi Shafighi, Szymon M. Kiełbasa, Hugo W. van Kessel, Ruben A. L. de Groen, Joost S. P. Vermaat, Julieta H. Sepúlveda Yáñez, Marcelo A. Navarrete, Hendrik Veelken, Cornelis A. M. van Bergen, Ewa Szczurek
Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.
{"title":"CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants","authors":"Kazimierz Oksza-Orzechowski, Edwin Quinten, Shadi Shafighi, Szymon M. Kiełbasa, Hugo W. van Kessel, Ruben A. L. de Groen, Joost S. P. Vermaat, Julieta H. Sepúlveda Yáñez, Marcelo A. Navarrete, Hendrik Veelken, Cornelis A. M. van Bergen, Ewa Szczurek","doi":"10.1186/s13059-024-03417-1","DOIUrl":"https://doi.org/10.1186/s13059-024-03417-1","url":null,"abstract":"Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"35 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oxford Nanopore Technologies (ONT) offers ultrahigh-throughput multi-sample sequencing but only provides barcode kits that enable up to 96-sample multiplexing. We present TDFPS-Designer, a new toolkit for nanopore sequencing barcode design, which creates significantly more barcodes: 137 with a length of 20 base pairs, 410 at 24 bp, and 1779 at 30 bp, far surpassing ONT’s offerings. It includes GPU-based acceleration for ultra-fast demultiplexing and designs robust barcodes suitable for high-error ONT data. TDFPS-Designer outperforms current methods, improving the demultiplexing recall rate by 20% relative to Guppy, without a reduction in precision.
牛津纳米孔技术公司（ONT）提供超高通量多样品测序服务，但只提供最多可复用 96 个样品的条形码工具包。我们介绍的 TDFPS-Designer 是一种用于纳米孔测序条形码设计的新工具包，它能创建更多的条形码：137 个长度为 20 碱基对的条形码、410 个长度为 24 bp 的条形码和 1779 个长度为 30 bp 的条形码，远远超过了牛津纳米孔技术公司的产品。它包括基于 GPU 的加速功能，可实现超快的解复用，并设计出适用于高误差 ONT 数据的强大条形码。TDFPS-Designer 的性能优于当前的方法，与 Guppy 相比，它将解复用召回率提高了 20%，而精度却没有降低。
{"title":"TDFPS-Designer: an efficient toolkit for barcode design and selection in nanopore sequencing","authors":"Junhai Qi, Zhengyi Li, Yao-zhong Zhang, Guojun Li, Xin Gao, Renmin Han","doi":"10.1186/s13059-024-03423-3","DOIUrl":"https://doi.org/10.1186/s13059-024-03423-3","url":null,"abstract":"Oxford Nanopore Technologies (ONT) offers ultrahigh-throughput multi-sample sequencing but only provides barcode kits that enable up to 96-sample multiplexing. We present TDFPS-Designer, a new toolkit for nanopore sequencing barcode design, which creates significantly more barcodes: 137 with a length of 20 base pairs, 410 at 24 bp, and 1779 at 30 bp, far surpassing ONT’s offerings. It includes GPU-based acceleration for ultra-fast demultiplexing and designs robust barcodes suitable for high-error ONT data. TDFPS-Designer outperforms current methods, improving the demultiplexing recall rate by 20% relative to Guppy, without a reduction in precision.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"142 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1186/s13059-024-03424-2
Xiaoting Li, Lucas A. N. Melo, Harmen J. Bussemaker
Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed. We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts. Our work provides new strategies for predicting the functional impact of non-coding variants.
{"title":"Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data","authors":"Xiaoting Li, Lucas A. N. Melo, Harmen J. Bussemaker","doi":"10.1186/s13059-024-03424-2","DOIUrl":"https://doi.org/10.1186/s13059-024-03424-2","url":null,"abstract":"Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed. We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts. Our work provides new strategies for predicting the functional impact of non-coding variants.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"8 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1186/s13059-024-03232-8
Xinzhou Ge, Yumei Li, Wei Li, Jingyi Jessica Li
Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences.
有两封来信对我们关于差异表达（DE）方法发现的夸大假阳性的分析提出了担忧或意见。在此，我们讨论了他们提出的观点，并解释了我们同意或不同意这些观点的原因。我们添加了新的分析，以确认在考虑了归一化和赢家化（即两封来信中讨论的数据预处理步骤）之后，在双条件 DE 分析中，与其他五种 DE 方法（DESeq2、edgeR、limma-voom、dearseq 和 NOISeq）相比，Wilcoxon 秩和检验仍然是最稳健的方法。
{"title":"Response to \"Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives\" and \"Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples\"","authors":"Xinzhou Ge, Yumei Li, Wei Li, Jingyi Jessica Li","doi":"10.1186/s13059-024-03232-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03232-8","url":null,"abstract":"Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"15 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1186/s13059-024-03231-9
Boris P. Hejblum, Kalidou Ba, Rodolphe Thiébaut, Denis Agniel
A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.
{"title":"Neglecting the impact of normalization in semi-synthetic RNA-seq data simulations generates artificial false positives","authors":"Boris P. Hejblum, Kalidou Ba, Rodolphe Thiébaut, Denis Agniel","doi":"10.1186/s13059-024-03231-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03231-9","url":null,"abstract":"A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"66 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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