The Single-cell proteomics data analysis using QFeatures and
scp workshop
is provided as two vignettes. The first one provides a general
introduction to the QFeatures class in the general context of mass
spectrometry-based proteomics data manipulation. The second vignette
focuses on single-cell application and introduces the scp package
(Vanderaa and Gatto 2021Vanderaa, Christophe, and Laurent Gatto. 2021. “Replication of Single-Cell Proteomics Data Reveals Important Computational Challenges.” Expert Rev. Proteomics, October.) as an extension of QFeatures. This second
vignette also provides exercises that give the attendee the
opportunity to apply the learned concepts to reproduce a published
analysis on a subset of a real data set. A recent
workshop, offered
at the 2024 EuBIC winter school, provides teaching material for the
new scplainer analysis workflow.
A tutorial presenting Use Cases and Examples for Annotation of
Untargeted Metabolomics
Data using
the MetaboAnnotation and MetaboCoreUtils packages
(Rainer et al. 2022Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M Badia, Steffen Neumann, Michael A Stravs, et al. 2022. “A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.” Metabolites 12 (2): 173.).
Exploring and analyzing LC-MS data with Spectra and xcms provides an overview of recent developments in Bioconductor to work with mass spectrometry (MsExperiment, Spectra) and specifically LC-MS data (xcms) and walks through the preprocessing of a small data set emphasizing on selection of data-dependent settings for the individual pre-processing steps.
The SpectraTutorials package provides three different vignettes:
For questions about specific software or their usage, please refer to the software’s github issue page, or use the Bioconductor support site.
The following packages have been used to generate this document.
sessionInfo()## R version 4.4.0 (2024-04-24)
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## attached base packages:
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## [8] base
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## other attached packages:
## [1] mzID_1.42.0 patchwork_1.2.0
## [3] factoextra_1.0.7 gplots_3.1.3.1
## [5] limma_3.60.3 lubridate_1.9.3
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## [7] rstatix_0.7.2 doParallel_1.0.17 lattice_0.22-6
## [10] MASS_7.3-61 backports_1.5.0 sass_0.4.9
## [13] rmarkdown_2.27 jquerylib_0.1.4 yaml_2.3.8
## [16] DBI_1.2.3 RColorBrewer_1.1-3 abind_1.4-5
## [19] zlibbioc_1.50.0 R.cache_0.16.0 R.utils_2.12.3
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## [40] tools_4.4.0 glue_1.7.0 SparseArray_1.4.8
## [43] BiocBaseUtils_1.6.0 xfun_0.45 withr_3.0.0
## [46] BiocManager_1.30.23 fastmap_1.2.0 rhdf5filters_1.16.0
## [49] fansi_1.0.6 caTools_1.18.2 digest_0.6.36
## [52] timechange_0.3.0 R6_2.5.1 colorspace_2.1-0
## [55] gtools_3.9.5 RSQLite_2.3.7 R.methodsS3_1.8.2
## [58] utf8_1.2.4 generics_0.1.3 data.table_1.15.4
## [61] PSMatch_1.8.0 httr_1.4.7 S4Arrays_1.4.1
## [64] pkgconfig_2.0.3 gtable_0.3.5 blob_1.2.4
## [67] impute_1.78.0 XVector_0.44.0 htmltools_0.5.8.1
## [70] carData_3.0-5 bookdown_0.39.1 MALDIquant_1.22.2
## [73] clue_0.3-65 scales_1.3.0 png_0.1-8
## [76] knitr_1.47 MetaboCoreUtils_1.12.0 rstudioapi_0.16.0
## [79] tzdb_0.4.0 reshape2_1.4.4 curl_5.2.1
## [82] cachem_1.1.0 rhdf5_2.48.0 BiocVersion_3.19.1
## [85] KernSmooth_2.23-24 parallel_4.4.0 AnnotationDbi_1.66.0
## [88] vsn_3.72.0 msmbstyle_0.0.22 pillar_1.9.0
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