leduc_minimal: Minimally processed single-cell proteomics data set
In UCLouvain-CBIO/scp: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis
leduc_minimal R Documentation
Minimally processed single-cell proteomics data set
Description
A SingleCellExperiment object that has been minimally processed.
The data set is published by Leduc et al. 2022 (see references)
and retrieved using scpdata::leduc2022_pSCoPE(). The data
processing was conducted with QFeatures and scp. Quality control
was performed, followed by building the peptide data and
log2-transformation. To limit the size of the data, only cells
associated to the 3 first and 3 last MS acquisition runs were
kept. For the same reason, 200 peptides were randomly
sampled. Therefore, the data set consists of 200 peptides and
73 cells. Peptide annotations can be retrieved from the rowData
and cell annotations can be retrieved from the colData.
Usage
data("leduc_minimal")
Format
An object of class SingleCellExperiment with 200 rows and 73 columns.
Quality control
Any zero value has been replaced by NA.
A peptide was removed from the data set if:
it matched to a decoy or contaminant peptide
it had an parental ion fraction below 60 \
it had a DART-ID adjusted q-value superior to 1\
it had an average sample to carrier ratio above 0.05
A cell was removed from the data set if:
it had a median coefficient of variation superior to 0.6
it had a log2 median intensity outside (6, 8)
it contained less than 750 peptides
Building the peptide matrix
PSMs belonging to the same peptide were aggregating using the
median value. Some peptides were mapped to a different protein
depending on the MS acquisition run. To solve this issue, a
majority vote was applied to assign a single protein to each
peptide. Protein IDs were translated into gene symbols using the
ensembldb package.
Author(s)
Christophe Vanderaa, Laurent Gatto
References
Leduc, Andrew, R. Gray Huffman, Joshua Cantlon, Saad Khan, and
Nikolai Slavov. 2022. “Exploring Functional Protein Covariation
across Single Cells Using nPOP.” Genome Biology 23 (1): 261.
UCLouvain-CBIO/scp documentation built on Oct. 12, 2024, 2:37 a.m.
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| leduc_minimal | R Documentation |
Minimally processed single-cell proteomics data set
Description
A SingleCellExperiment object that has been minimally processed.
The data set is published by Leduc et al. 2022 (see references)
and retrieved using scpdata::leduc2022_pSCoPE(). The data
processing was conducted with QFeatures and scp. Quality control
was performed, followed by building the peptide data and
log2-transformation. To limit the size of the data, only cells
associated to the 3 first and 3 last MS acquisition runs were
kept. For the same reason, 200 peptides were randomly
sampled. Therefore, the data set consists of 200 peptides and
73 cells. Peptide annotations can be retrieved from the rowData
and cell annotations can be retrieved from the colData.
Usage
data("leduc_minimal")
Format
An object of class SingleCellExperiment with 200 rows and 73 columns.
Quality control
Any zero value has been replaced by NA.
A peptide was removed from the data set if:
it matched to a decoy or contaminant peptide
it had an parental ion fraction below 60 \
it had a DART-ID adjusted q-value superior to 1\
it had an average sample to carrier ratio above 0.05
A cell was removed from the data set if:
it had a median coefficient of variation superior to 0.6
it had a log2 median intensity outside (6, 8)
it contained less than 750 peptides
Building the peptide matrix
PSMs belonging to the same peptide were aggregating using the
median value. Some peptides were mapped to a different protein
depending on the MS acquisition run. To solve this issue, a
majority vote was applied to assign a single protein to each
peptide. Protein IDs were translated into gene symbols using the
ensembldb package.
Author(s)
Christophe Vanderaa, Laurent Gatto
References
Leduc, Andrew, R. Gray Huffman, Joshua Cantlon, Saad Khan, and Nikolai Slavov. 2022. “Exploring Functional Protein Covariation across Single Cells Using nPOP.” Genome Biology 23 (1): 261.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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