data Search Results


xenocs xsact software  (Xenocs Inc)
96
Xenocs Inc xenocs xsact software
Xenocs Xsact Software, supplied by Xenocs Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xenocs xsact software/product/Xenocs Inc
Average 96 stars, based on 1 article reviews
xenocs xsact software - by Bioz Stars, 2026-06
96/100 stars
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pbmc 4k  (10X Genomics)
86
10X Genomics pbmc 4k
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Pbmc 4k, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/pbmc 4k/product/10X Genomics
Average 86 stars, based on 1 article reviews
pbmc 4k - by Bioz Stars, 2026-06
86/100 stars
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gcq data processing  (Finnigan Corporation)
86
Finnigan Corporation gcq data processing
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Gcq Data Processing, supplied by Finnigan Corporation, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gcq data processing/product/Finnigan Corporation
Average 86 stars, based on 1 article reviews
gcq data processing - by Bioz Stars, 2026-06
86/100 stars
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data cut run data  (Jackson Laboratory)
86
Jackson Laboratory data cut run data
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Data Cut Run Data, supplied by Jackson Laboratory, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/data cut run data/product/Jackson Laboratory
Average 86 stars, based on 1 article reviews
data cut run data - by Bioz Stars, 2026-06
86/100 stars
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data safety monitoring  (Janssen)
86
Janssen data safety monitoring
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Data Safety Monitoring, supplied by Janssen, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/data safety monitoring/product/Janssen
Average 86 stars, based on 1 article reviews
data safety monitoring - by Bioz Stars, 2026-06
86/100 stars
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sumber hasil pengolahan data statistik  (Statistik Georg Ferber)
86
Statistik Georg Ferber sumber hasil pengolahan data statistik
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Sumber Hasil Pengolahan Data Statistik, supplied by Statistik Georg Ferber, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/sumber hasil pengolahan data statistik/product/Statistik Georg Ferber
Average 86 stars, based on 1 article reviews
sumber hasil pengolahan data statistik - by Bioz Stars, 2026-06
86/100 stars
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resolution apci data  (Advion)
86
Advion resolution apci data
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Resolution Apci Data, supplied by Advion, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/resolution apci data/product/Advion
Average 86 stars, based on 1 article reviews
resolution apci data - by Bioz Stars, 2026-06
86/100 stars
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data analyses  (Amgen)
86
Amgen data analyses
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Data Analyses, supplied by Amgen, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/data analyses/product/Amgen
Average 86 stars, based on 1 article reviews
data analyses - by Bioz Stars, 2026-06
86/100 stars
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pressure sensor  (Elveflow Inc)
93
Elveflow Inc pressure sensor
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Pressure Sensor, supplied by Elveflow Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/pressure sensor/product/Elveflow Inc
Average 93 stars, based on 1 article reviews
pressure sensor - by Bioz Stars, 2026-06
93/100 stars
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digidata 1550b  (Danaher Inc)
96
Danaher Inc digidata 1550b
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Digidata 1550b, supplied by Danaher Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/digidata 1550b/product/Danaher Inc
Average 96 stars, based on 1 article reviews
digidata 1550b - by Bioz Stars, 2026-06
96/100 stars
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matlab data acquisition toolbox  (MathWorks Inc)
94
MathWorks Inc matlab data acquisition toolbox
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Matlab Data Acquisition Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab data acquisition toolbox/product/MathWorks Inc
Average 94 stars, based on 1 article reviews
matlab data acquisition toolbox - by Bioz Stars, 2026-06
94/100 stars
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statistica  (TIBCO)
96
TIBCO statistica
a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the <t>pbmc_4k</t> dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.
Statistica, supplied by TIBCO, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/statistica/product/TIBCO
Average 96 stars, based on 1 article reviews
statistica - by Bioz Stars, 2026-06
96/100 stars
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a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the pbmc_4k dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the pbmc_4k dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Marker, Expressing

a Top: for estimating the coefficient of variation (CV), the plug-in estimates become more inflated as the sequencing depth becomes shallower (from right to left along the x axis), whereas the EB estimates are consistent. 3-std confidence intervals are provided for this panel. Middle: both brain_1k and brain_1.3m are from the mouse brain, and hence each gene should have a similar CV value between the two datasets. This is indeed the case for the EB estimator (right), which is adaptive to different sequencing depths. However, as brain_1k is twice deeper than brain_1.3m, the plug-in estimates are biased that most points are above the 45-degree line (red). Bottom: distribution recovery for the gene GZMA from a dataset that is subsampled to be five times shallower (left). The EB estimator provides a reasonable estimation for both the zero proportion and the tail shape, resulting in a small total variation error (right). b Feature selection and PCA. The task is to first select features (genes) based on CV, and then perform PCA on the selected features. The results on the full data (pbmc_4k) and a subsampled (three times shallower) are compared. EB estimates are more consistent between the full data and the subsampled data for both the CV ranks (top) and the PCA plots (bottom).

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: for estimating the coefficient of variation (CV), the plug-in estimates become more inflated as the sequencing depth becomes shallower (from right to left along the x axis), whereas the EB estimates are consistent. 3-std confidence intervals are provided for this panel. Middle: both brain_1k and brain_1.3m are from the mouse brain, and hence each gene should have a similar CV value between the two datasets. This is indeed the case for the EB estimator (right), which is adaptive to different sequencing depths. However, as brain_1k is twice deeper than brain_1.3m, the plug-in estimates are biased that most points are above the 45-degree line (red). Bottom: distribution recovery for the gene GZMA from a dataset that is subsampled to be five times shallower (left). The EB estimator provides a reasonable estimation for both the zero proportion and the tail shape, resulting in a small total variation error (right). b Feature selection and PCA. The task is to first select features (genes) based on CV, and then perform PCA on the selected features. The results on the full data (pbmc_4k) and a subsampled (three times shallower) are compared. EB estimates are more consistent between the full data and the subsampled data for both the CV ranks (top) and the PCA plots (bottom).

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Selection

a Top: the EB-estimated Pearson correlation for some marker genes in pbmc_4k are visualized, ordered by different cell populations (top). The clear block-diagonal structure implies that the EB estimator is capable of capturing the gene functional groups. As a comparison, the plug-in estimator also recovers those modules but with a weaker contrast (bottom left panel, plug-in with 100%). Bottom: a subsample experiment further shows that the EB estimator can recover the module with 5% of the data. For the plug-in estimator, the first block (T cells) is blurred with 25% of the data, and the entire structure vanishes with 10% of the data. b Gene network based on the EB-estimated Pearson correlation using the pbmc_4k dataset. Most gene modules correspond to important cell types or functions, including T cells, B cells, NK-cells, myeloid-derived cells, megakaryocytes/platelets, ribosomal protein genes, and mitochondrially encoded protein-coding genes. c Left: the estimated Pearson correlations between all genes and LCK (1st panel) and CD3D (2nd panel), two known T-cell markers. There are three modes for the EB-estimated values, where the positive mode, the zero mode, and the negative mode correspond to genes in the same module, different modules, and irrelevant genes, respectively. The plug-in estimated values are nonetheless much closer to zero even for the truly correlated ones, indicating an artificial shrinkage of the estimated values. Right: two instances where the EB estimates are significantly different from the plug-in estimates. The axes represent read counts, and the color codes the number of cells. Both gene pairs are biologically validated (see Gene network analysis in Methods). See also Supplementary Figs. – for more examples.

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: the EB-estimated Pearson correlation for some marker genes in pbmc_4k are visualized, ordered by different cell populations (top). The clear block-diagonal structure implies that the EB estimator is capable of capturing the gene functional groups. As a comparison, the plug-in estimator also recovers those modules but with a weaker contrast (bottom left panel, plug-in with 100%). Bottom: a subsample experiment further shows that the EB estimator can recover the module with 5% of the data. For the plug-in estimator, the first block (T cells) is blurred with 25% of the data, and the entire structure vanishes with 10% of the data. b Gene network based on the EB-estimated Pearson correlation using the pbmc_4k dataset. Most gene modules correspond to important cell types or functions, including T cells, B cells, NK-cells, myeloid-derived cells, megakaryocytes/platelets, ribosomal protein genes, and mitochondrially encoded protein-coding genes. c Left: the estimated Pearson correlations between all genes and LCK (1st panel) and CD3D (2nd panel), two known T-cell markers. There are three modes for the EB-estimated values, where the positive mode, the zero mode, and the negative mode correspond to genes in the same module, different modules, and irrelevant genes, respectively. The plug-in estimated values are nonetheless much closer to zero even for the truly correlated ones, indicating an artificial shrinkage of the estimated values. Right: two instances where the EB estimates are significantly different from the plug-in estimates. The axes represent read counts, and the color codes the number of cells. Both gene pairs are biologically validated (see Gene network analysis in Methods). See also Supplementary Figs. – for more examples.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Marker, Blocking Assay, Functional Assay, Derivative Assay