SCnorm protocols

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SCnorm specifications


Unique identifier OMICS_13863
Name SCnorm
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input format UTF-8
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.1.1
Stability Stable
Source code URL
Maintained Yes



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  • person_outline Christina Kendziorski <>
  • person_outline Rhonda Bacher <>

Publication for SCnorm

SCnorm in pipeline

PMCID: 5746276
PMID: 29240790
DOI: 10.1371/journal.pbio.2004050

[…] them as missing. the wavecrest algorithm was also used as above to obtain a cyclic order on the set of unstressed single cells based on five rp genes ()., length-normalized read counts were taken as scnorm-normalized read counts per transcript divided by transcript length, and the mean (or median) for each transcript across all unstressed or stressed cells was calculated; mean and median values […]

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SCnorm in publications

PMCID: 5746276
PMID: 29240790
DOI: 10.1371/journal.pbio.2004050

[…] 1.5.0. sequenced wells were removed from the analysis if they had <1,000 total mapped reads or if the proportion of ercc spike-ins to total-mapped reads was > 0.2 []. data were normalized by scnorm [] in r version 3.3.1; ercc spike-in samples were not used in the normalization. normalized read counts for each gene were logged and then centered by subtracting the mean log2(read counts) […]

PMCID: 5815497
PMID: 28953884
DOI: 10.1038/nature24033

[…] was applied to the matrix, ensuring >50,000 total transcript reads per cell and >5 reads in at least 5 samples. the raw transcript counts were corrected for read-count depth effects using the scnorm package with a single-group design matrix. the ruvseq (version 1.10.0) was used for between-sample normalization by applying the ‘betweenlanenormalization’ function with ‘full’ quantile […]

PMCID: 1622760
PMID: 17042958
DOI: 10.1186/1471-2105-7-456

[…] label. we then go down the list and find the next structure that has a different label from c1. let us call this entry n2, and let c2 denote the label for n2 . next, we compute the scores, scnorm(q, n1) and scnorm(q, n2)., then we use these scores to compute f1 and f2 as: f1 = scnorm(q, n1) and f2 = scnorm(q, n1)/scnorm(q, n2). finally, we return the values f1, f2 and c1., intuitively, […]

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SCnorm institution(s)
Department of Statistics, University of Wisconsin, Madison, WI, USA; Morgridge Institute for Research, Madison, WI, USA; Laboratory of Genetics, University of Wisconsin, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
SCnorm funding source(s)
Supported by NIH GM102756, NIH U54 AI117924, 1T32LM012413-01A1, NIH 5U01HL099773 and the Morgridge Institute for Research.

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