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10.1016/j.cels.2016.04.001 http://scihub22266oqcxt.onion/10.1016/j.cels.2016.04.001 C4856162!4856162!27135536     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cell+Syst 2016 ; 2 (4): 239-50 Nephropedia Template TP {{tp|p=27135536|t=2016. Low-dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing |pdf=|usr=}}{{27135536}} gab.com Text Low-dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing JO: Cell Syst http://www.kidney.de/pget.php?&tw=1&pmid=27135536 #FOAvip A tradeoff between precision and throughput constrains all biological measurements, including sequencing-based technologies. Here, we develop a mathematical framework that defines this tradeoff between mRNA-sequencing depth and error in the extraction of biological information. We find that transcriptional programs can be reproducibly identified at 1% of conventional read depths. We demonstrate that this resilience to noise of ?shallow? sequencing derives from a natural property, low-dimensionality, that is a fundamental feature of gene expression data. Accordingly, our conclusions hold for ~350 single cell and bulk gene expression datasets across yeast, mouse and human. In total, our approach provides quantitative guidelines for the choice of sequencing depth necessary to achieve a desired level of analytical resolution; we codify these guidelines in an open source ?Read Depth Calculator.? This work demonstrates that the structure inherent in biological networks can be productively exploited to increase measurement throughput, an idea that is now common in many branches of science such as image processing. Twit Text FOAVip Low-dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing ????Cell Syst http://www.kidney.de/pget.php?&tw=1&pmid=27135536 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27135536 #FOAvip English Wikipedia <ref>{{Cite pmid|27135536}}</ref> Low-dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing #MMPMID27135536 Heimberg G; Bhatnagar R; El-Samad H; Thomson M Cell Syst 2016[Apr]; 2 (4): 239-50 PMID27135536show ga A tradeoff between precision and throughput constrains all biological measurements, including sequencing-based technologies. Here, we develop a mathematical framework that defines this tradeoff between mRNA-sequencing depth and error in the extraction of biological information. We find that transcriptional programs can be reproducibly identified at 1% of conventional read depths. We demonstrate that this resilience to noise of ?shallow? sequencing derives from a natural property, low-dimensionality, that is a fundamental feature of gene expression data. Accordingly, our conclusions hold for ~350 single cell and bulk gene expression datasets across yeast, mouse and human. In total, our approach provides quantitative guidelines for the choice of sequencing depth necessary to achieve a desired level of analytical resolution; we codify these guidelines in an open source ?Read Depth Calculator.? This work demonstrates that the structure inherent in biological networks can be productively exploited to increase measurement throughput, an idea that is now common in many branches of science such as image processing. äLow-dimensionality in gene expression data enables the accurate extrac(epmc).pdf DeepDyve Pubget Overpricing