Search Results - "Gene Expression Profiling/statistics & numerical data"

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics by Luecken, Malte D., Sikkema, Lisa, Waghray, Avinash, Heimberg, Graham, Kobayashi, Yoshihiko, Vaishnav, Eeshit Dhaval, Subramanian, Ayshwarya, Smillie, Christopher, Jagadeesh, Karthik A., Duong, Elizabeth Thu, Torlai Triglia, Elena, Ansari, Meshal, Cai, Peiwen, Lin, Brian, Chen, Sijia, Shu, Jian, Haber, Adam L., Chung, Hattie, Montoro, Daniel T., Adams, Taylor, Aliee, Hananeh, Andrusivova, Zaneta, Angelidis, Ilias, Ashenberg, Orr, Bassler, Kevin, Bécavin, Christophe, Benhar, Inbal, Bergenstråhle, Joseph, Bergenstråhle, Ludvig, Braun, Emelie, Bui, Linh T., Chaffin, Mark, Chichelnitskiy, Evgeny, Chiou, Joshua, Conlon, Thomas M., Cuoco, Michael S., Cuomo, Anna S. E., Deprez, Marie, Duclos, Grant, Fischer, David S., Ghazanfar, Shila, Gillich, Astrid, Giotti, Bruno, Gould, Joshua, Guo, Minzhe, Gutierrez, Austin J., Habermann, Arun C., Harvey, Tyler, He, Peng, Hou, Xiaomeng, Hu, Yan, Jaiswal, Alok, Ji, Lu, Jiang, Peiyong, Kapellos, Theodoros S., Kuo, Christin S., Leney-Greene, Michael A., Lim, Kyungtae, Litviňuková, Monika, Ludwig, Leif S., Lukassen, Soeren, Luo, Wendy, Maatz, Henrike, Madissoon, Elo, Mamanova, Lira, Manakongtreecheep, Kasidet, Leroy, Sylvie, Mayr, Christoph H., McAdams, Alexi M., Nabhan, Ahmad N., Nyquist, Sarah K., Penland, Lolita, Poirion, Olivier B., Poli, Sergio, Qi, CanCan, Queen, Rachel, Reichart, Daniel, Rosas, Ivan, Schupp, Jonas C., Shea, Conor V., Shi, Xingyi, Sinha, Rahul, Slowikowski, Kamil, Smith, Neal P., Sountoulidis, Alex, Strunz, Maximilian, Sullivan, Travis B., Sun, Dawei, Talavera-López, Carlos, Tan, Peng, Tantivit, Jessica, Travaglini, Kyle J., Tucker, Nathan R., Vernon, Katherine A., Wadsworth, Marc H., Waldman, Julia, Wang, Xiuting, Yan, Wenjun, Zhao, William, Ziegler, Carly G. K.

“…Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)…”
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Salmon provides fast and bias-aware quantification of transcript expression by Patro, Rob, Duggal, Geet, Love, Michael I, Irizarry, Rafael A, Kingsford, Carl

“…Salmon is a computational tool that uses sample-specific models and a dual-phase inference procedure to correct biases in RNA-seq data and rapidly quantify…”
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Census and evaluation of p53 target genes by Fischer, M

“…The tumor suppressor p53 functions primarily as a transcription factor. Mutation of the TP53 gene alters its response pathway, and is central to the…”
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A review of methods and databases for metagenomic classification and assembly by Breitwieser, Florian P, Lu, Jennifer, Salzberg, Steven L

“…Abstract Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets…”
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Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing by Deshmukh, Abhijeet P, Vasaikar, Suhas V, Tomczak, Katarzyna, Tripathi, Shubham, den Hollander, Petra, Arslan, Emre, Chakraborty, Priyanka, Soundararajan, Rama, Jolly, Mohit Kumar, Rai, Kunal, Levine, Herbert, Mani, Sendurai A

“…The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling…”
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Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types by Finucane, Hilary K., Reshef, Yakir A., Anttila, Verneri, Slowikowski, Kamil, Gusev, Alexander, Byrnes, Andrea, Gazal, Steven, Loh, Po-Ru, Lareau, Caleb, Shoresh, Noam, Genovese, Giulio, Saunders, Arpiar, Macosko, Evan, Pollack, Samuela, Perry, John R. B., Buenrostro, Jason D., Bernstein, Bradley E., Raychaudhuri, Soumya, McCarroll, Steven, Neale, Benjamin M., Price, Alkes L.

“…We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study…”
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Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions by Urbut, Sarah M., Wang, Gao, Carbonetto, Peter, Stephens, Matthew

“…We introduce new statistical methods for analyzing genomic data sets that measure many effects in many conditions (for example, gene expression changes under…”
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Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model by Dixon, Eryn E, Wu, Haojia, Muto, Yoshiharu, Wilson, Parker C, Humphreys, Benjamin D

“…Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets…”
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Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data by Ching, Travers, Zhu, Xun, Garmire, Lana X.

“…Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to…”
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Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions by Evans, Ciaran, Hardin, Johanna, Stoebel, Daniel M

“…Abstract RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is…”
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A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION by DANAEE, PADIDEH, GHAEINI, REZA, HENDRIX, DAVID A.

“…Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research…”
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Microarray is an efficient tool for circRNA profiling by Li, Shasha, Teng, Shuaishuai, Xu, Junquan, Su, Guannan, Zhang, Yu, Zhao, Jianqing, Zhang, Suwei, Wang, Haiyan, Qin, Wenyan, Lu, Zhi John, Guo, Yong, Zhu, Qianyong, Wang, Dong

“…Abstract Circular RNAs (circRNAs) are emerging as a new class of endogenous and regulatory noncoding RNAs in latest years. With the widespread application of…”
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Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis by Cheng, Jun, Zhang, Jie, Han, Yatong, Wang, Xusheng, Ye, Xiufen, Meng, Yuebo, Parwani, Anil, Han, Zhi, Feng, Qianjin, Huang, Kun

“…In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with…”
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Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data by Yip, Shun H, Sham, Pak Chung, Wang, Junwen

“…Abstract Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially…”
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Computational challenges and opportunities in spatially resolved transcriptomic data analysis by Atta, Lyla, Fan, Jean

“…Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated…”
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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders by Way, Gregory P., Greene, Casey S.

“…The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels…”
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Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses by Stegle, Oliver, Parts, Leopold, Piipari, Matias, Winn, John, Durbin, Richard

“…We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and…”
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Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data by Zhao, Yifan, Cai, Huiyu, Zhang, Zuobai, Tang, Jian, Li, Yue

“…The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of…”
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Computational methods for transcriptome annotation and quantification using RNA-seq by Garber, Manuel, Grabherr, Manfred G, Guttman, Mitchell, Trapnell, Cole

“…High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all…”
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DNA methylation profiling in the clinic: applications and challenges by Heyn, Holger, Esteller, Manel

“…Key Points Alterations in epigenetic marks — specifically DNA methylation — are an emerging biomarker that may be used in the decision-making process for…”
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