contribution_guidelines.RmdSite built with pkgdown 2.0.7.9000.
+Site built with pkgdown 2.0.8.
diff --git a/articles/index.html b/articles/index.html index 4d42f27..813bf51 100644 --- a/articles/index.html +++ b/articles/index.html @@ -71,7 +71,7 @@scpdata.RmdExperimentHub<
the query function.
query(eh, "scpdata")
-#> ExperimentHub with 24 records
-#> # snapshotDate(): 2024-04-08
-#> # $dataprovider: MassIVE, PRIDE, SlavovLab website
+#> ExperimentHub with 26 records
+#> # snapshotDate(): 2024-04-11
+#> # $dataprovider: MassIVE, PRIDE, SlavovLab website, Dataverse
#> # $species: Homo sapiens, Mus musculus, Rattus norvegicus, Gallus gallus
#> # $rdataclass: QFeatures
#> # additional mcols(): taxonomyid, genome, description,
@@ -124,18 +124,18 @@ Load data from ExperimentHub<
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH3899"]]'
#>
-#> title
-#> EH3899 | specht2019v2
-#> EH3900 | specht2019v3
-#> EH3901 | dou2019_lysates
-#> EH3902 | dou2019_mouse
-#> EH3903 | dou2019_boosting
-#> ... ...
-#> EH8303 | woo2022_macrophage
-#> EH8304 | woo2022_lung
-#> EH9450 | gregoire2023_mixCTRL
-#> EH9477 | khan2023
-#> EH9487 | guise2024
Another way to get information about the available data sets is to
call scpdata(). This will retrieve all the available
metadata. For example, we can retrieve the data set titles along with
@@ -147,8 +147,8 @@
ExperimentHub<
| @@ -300,6 +300,18 @@ | Single-cell proteomics data of 108 postmortem CTL or ALS spinal moto neurons | |
|---|---|---|
| EH9497 | +petrosius2023_mES | +Mouse embryonic stem cells across ground-state (m2i) +and differentiation-permissive (m15) culture conditions. | +
| EH9498 | +petrosius2023_AstralAML | +Single-cell proteomics data of 4 cell types from the +OCI-AML8227 model. | +
To get one of the data sets (e.g. @@ -379,7 +391,7 @@
Site built with pkgdown 2.0.7.9000.
+Site built with pkgdown 2.0.8.
diff --git a/authors.html b/authors.html index c41fc23..78fda4f 100644 --- a/authors.html +++ b/authors.html @@ -106,7 +106,7 @@evidence.txt file containing the PSM identification and
quantification results. The sample annotation was inferred from
the samples names. The data were then converted to a QFeatures
-object using the scp::readSCP function.
+object using the scp::readSCP() function.
The peptide data were processed similarly from the peptides.txt
file. The quantitative column names were adpated to match the PSM
data. The peptide data were added to QFeatures object and link
@@ -178,7 +178,7 @@
;. The sample
annotation table was manually created based on the available
information provided in the article. The data were then converted
-to a QFeatures object using the scp::readSCP function.
+to a QFeatures object using the scp::readSCP() function.
We generated the peptide data. First, we removed PSM matched to contaminants or decoy peptides and ensured a 1% FDR. We aggregated the PSM to peptides based on the peptide (or peptide group) @@ -210,7 +210,7 @@
;. The sample
annotation table was manually created based on the available
information provided in the article. The data were then converted
-to a QFeatures object using the scp::readSCP function.
+to a QFeatures object using the scp::readSCP() function.
We generated the peptide data. First, we removed PSM matched to contaminants or decoy peptides and ensured a 1% FDR. We aggregated the PSM to peptides based on the peptide (or peptide group) @@ -199,7 +199,7 @@
;. The sample
annotation table was manually created based on the available
information provided in the article. The data were then converted
-to a QFeatures object using the scp::readSCP function.
+to a QFeatures object using the scp::readSCP() function.
We generated the peptide data. First, we removed PSM matched to contaminants or decoy peptides and ensured a 1% FDR. We aggregated the PSM to peptides based on the peptide (or peptide group) @@ -217,7 +217,7 @@
cbio and giga tables together and merged resulting
identification and quantification tables. Both annotation and
features tables are then combined in a single QFeatures object
-using the scp::readSCP function.
+using the scp::readSCP() function.
The QFeatures object was processed as described in the author's
manuscript (see source). Note that the imputed assays were used
in the paper for illustrative purposes only and have not been
@@ -192,7 +192,7 @@
Groups.txt.
The PSM data were found in the
Biogen_TDP43_Round2_Reanalysis_10-13-2021_PSMs.txt file. The
-data were converted to a QFeatures object using the scp::readSCP
+data were converted to a QFeatures object using the scp::readSCP()
function. We could not find sample annotations for MS run ID:
F61, F34, F42, F88, F77, F8, F21, F5.
The peptide data were found in the @@ -186,7 +186,7 @@
liang2020_hela
Liang et al. 2020 (Anal. Chem.): HeLa cells (MaxQuant preprocessing)
Petrosius et al. 2023 (bioRxiv): AML hierarchy on Astral.
Petrosius et al, 2023 (Nat. Comm.): Mouse embryonic stem cell (mESC) in different culture conditions
scp::readSCP() function.
The peptide data were taken from the same google drive folder
(EpiToMesen.TGFB.nPoP_trial1_pepByCellMatrix_NSThreshDART_medIntCrNorm.txt).
-The data were formated to a SingleCellExperiment object and the sample
+The data were formatted to a SingleCellExperiment object and the sample
metadata were matched to the column names (mapping is retrieved
after running the SCoPE2 R script, EMTTGFB_singleCellProcessing.R) and
stored in the colData. The object is then added to the QFeatures object
@@ -148,7 +148,7 @@
AssayLink object.
The imputed protein data were taken from the same google drive folder
(EpiToMesen.TGFB.nPoP_trial1_ProtByCellMatrix_NSThreshDART_medIntCrNorm_imputedNotBC.csv).
-The data were formated to a SingleCellExperiment object and the sample
+The data were formatted to a SingleCellExperiment object and the sample
metadata were matched to the column names (mapping is retrieved
after running the SCoPE2 R script, EMTTGFB_singleCellProcessing.R) and
stored in the colData. The object is then added to the QFeatures object
@@ -156,7 +156,7 @@
AssayLink object.
The unimputed protein data were taken from the same google drive folder
(EpiToMesen.TGFB.nPoP_trial1_ProtByCellMatrix_NSThreshDART_medIntCrNorm_unimputed.csv).
-The data were formated and added exactly as imputed data.
t6.csv: the processed data table containing the
proteins_processed data
We combined the sample annotation and the batch annotation in -a single table. We also formated the quantification table so that +a single table. We also formatted the quantification table so that columns match with those of the annotations. Both annotation and quantification tables are then combined in a single QFeatures -object using the scp::readSCP function.
-The 4 CSV files were loaded and formated as SingleCellExperiment
+object using the scp::readSCP() function.
The 4 CSV files were loaded and formatted as SingleCellExperiment
objects and the sample metadata were matched to the column names
(mapping is retrieved after running the author's original R script)
and stored in the colData.
@@ -216,7 +216,7 @@
plexDIA_protein_imputed.csv: the processed data table
containing the protein data
We removed the failed runs as identified by the authors. We also
-formated the annotation and precuror quantification tables to
+formatted the annotation and precuror quantification tables to
facilitate matching between corresponding columns. Both annotation
and quantification tables are then combined in a single QFeatures
object using scp::readSCPfromDIANN().
The plexDIA_peptide.csv and plexDIA_protein_imputed.csv files
-were loaded and formated as SingleCellExperiment objects. The
+were loaded and formatted as SingleCellExperiment objects. The
columns names were adapted to match those in the QFeatures
object. The SingleCellExperiment objects were then added to the
QFeatures object and the rows of the peptide data are linked to
@@ -208,7 +208,7 @@
The sample annotations were collected from the methods section and from table S3 in the paper.
The PSM data were found in the evidence.txt file. The data were
-converted to a QFeatures object using the scp::readSCP
+converted to a QFeatures object using the scp::readSCP()
function.
The peptide data were found in the peptides.txt file. The column
names holding the quantitative data were adapted to match the
@@ -185,7 +185,7 @@
petrosius2023_AstralAML.RdSingle cell proteomics data from FACS sorted cells from the +OCI-AML8227 model. The dataset contains leukemic stem cells (LSC; +CD34+, CD38-), progenitor cells (CD34+, CD38+), CD38+ blasts +(CD34-, CD38+) and CD38- blasts (CD34-, CD38-). It contains +quantitative information at PSM, peptide and protein levels. Data +was acquired using an Orbitrap Astral mass spectrometer. Direct DIA +analysis was performed with Spectronaut version 17.
+petrosius2023_AstralAMLA QFeatures object with 217 assays, each assay being a +SingleCellExperiment object:
Assays 1-215: PSM data from the Spectronaut PEPQuant file with +LFQ quantities from the FG.MS1Quantity column.
peptides: Peptide data resulting from the PSM to peptide
+aggregation the 215 PSM assays. Resulting peptide assays were
+joined into a single assay.
proteins: Protein data from the Spectronaut PGQuant file with
+LFQ quantities from the PG.Quantity column.
The colData(petrosius2023_AstralAML()) contains cell type annotation, batch
+annotation and FACS data. The description of the rowData fields
+can be found in the Spectronaut user manual.
The PSM data, protein data and sample annotations can be +downloaded from the dataset 'Astral AML single-cell data from +Petrosius et al. 2023 preprint' in the +Dataverse.
+The data were acquired using the following setup. More information +can be found in the source article (see References).
Cell isolation: Cell sorting was done on a FACS Aria III or +Aria II instrument, controlled by the DIVA software package and +operated with a 100 μm nozzle. Cells were sorted at single-cell +resolution, into a 384-well Eppendorf LoBind PCR plate containing +1 μL of lysis buffer.
Sample preparation Single-cell protein lysates were digested +overnight at 37°C with 2 ng of Trypsin supplied in 1 μL of +digestion buffer. Digestion was stopped by the addition of 1 μL +1% (v/v) trifluoroacetic acid (TFA). All liquid dispensing was +done using an I-DOT One instrument.
Liquid chromatography: Chromatographic separation of peptides +was conducted on a vanquish Neo UHPLC system connected to a 50 cm +uPAC Neo Low-load and an EASY-spray. Autosampler and injection +valves were configured to perform direct injections from a 384 +well plate using a 25 uL injection loop on 11.8 min gradients.
Mass spectrometry: Acquisition was conducted with an Orbitrap +Astral mass spectrometer operated in positive mode with the +FAIMSPro interface compensation voltage set to −45 V. +MS1 scans were acquired with the Orbitrap at a resolution of +120,000 and a scan range of 400 to 900 m/z with normalized +automatic gain control (AGC) target of 300 % and maximum +injection time of 246 ms. Data independent acquisition of MS2 +spectra was performed in the Astral using loop control set to 0.7 +seconds per cycle with varying isolation window widths and +injection times. Fragmentation of precursor ions was performed +using higher energy collisional dissociation (HCD) using a +normalized collision energy (NCE) of 25 %. AGC target was set to +800 %.
Raw data processing: Raw files were processed using +Spectronaut version 17. Direct DIA analysis was performed in +pipeline mode. Pulsar searches were performed without fixed +modifications. N-terminal acetylation and methionine oxidation +were set as variable modifications. Quantification level was set +to MS1 and the quantity type set to area under the curve.
The data were provided by the authors and is accessible at the +Dataverse +The dataset ('Astral AML single-cell data from Petrosius et +al. 2023 preprint') contains the following files of interest:
20240201_130747_PEPQuant (Normal).tsv: the PSM level data
20240201_130747_PGQuant (Normal).tsv: the protein level data
index_map.csv: FACS data.
msRuns_overview.csv: Sample annotations.
We added the FACS data to the sample annotations in a single table.
+Both annotations and PSM features tables are then combined in a
+single QFeatures object using the scp::readSCP() function.
The peptide data were obtained by aggregation of the PSM data to +the peptide level. All of the resulting peptides assays were joined +into a single assays. Individual peptides assays were discarded.
+The protein data were formatted from the 20240201_130747_PGQuant (Normal).tsv
+to a SingleCellExperiment object and the sample metadata were
+matched to the column names and stored in the colData. The
+object is then added to the QFeatures object and the rows of the
+peptide data are linked to the rows of the protein data based on
+the protein sequence information through an AssayLink object.
Note that the QFeatures object has not been further processed and +has therefore not been normalized, log-transformed or +batch-corrected.
+Valdemaras Petrosius, Pedro Aragon-Fernandez, Tabiwang N. Arrey, +Nil Üresin, Benjamin Furtwängler, Hamish Stewart, Eduard Denisov, +Johannes Petzoldt, Amelia C. Peterson, Christian Hock, Eugen +Damoc, Alexander Makarov, Vlad Zabrouskov, Bo T. Porse and Erwin +M. Schoof. +2023. "Evaluating the capabilities of the Astral mass analyzer for single-cell proteomics." +biorxiv. https://doi.org/10.1101/2023.06.06.543943 +DOI:10.1101/2023.06.06.543943
+# \donttest{
+petrosius2023_AstralAML()
+#> see ?scpdata and browseVignettes('scpdata') for documentation
+#> loading from cache
+#> An instance of class QFeatures containing 217 assays:
+#> [1] CD38_neg_1: SingleCellExperiment with 4282 rows and 1 columns
+#> [2] CD38_neg_10: SingleCellExperiment with 3393 rows and 1 columns
+#> [3] CD38_neg_11: SingleCellExperiment with 4530 rows and 1 columns
+#> ...
+#> [215] prog_9: SingleCellExperiment with 4560 rows and 1 columns
+#> [216] peptides: SingleCellExperiment with 11549 rows and 215 columns
+#> [217] proteins: SingleCellExperiment with 2904 rows and 215 columns
+# }
+
+petrosius2023_mES.RdProfiling mouse embryonic stem cells across ground-state (m2i) and +differentiation-permissive (m15) culture conditions. The data were +acquired using orbitrap-based data-independent acquisition (DIA). +The objective was to demonstrate the capability of their approach +by profiling mouse embryonic stem cell culture conditions, showcasing +heterogeneity in global proteomes, and highlighting differences in +the expression of key metabolic enzymes in distinct cell subclusters.
+petrosius2023_mESA QFeatures object with 605 assays, each assay being a +SingleCellExperiment object:
Assay 1-603: PSM data acquired with an orbitrap-based data-independent +acquisition (DIA) protocol, hence those assays contain single column +that contains the quantitative information.
peptides: peptide data containing quantitative data for 9884
+peptides and 603 single-cells.
proteins: protein data containing quantitative data for 4270
+proteins and 603 single-cells.
Sample annotation is stored in colData(petrosius2023_mES()).
The peptide and protein data can be downloaded from the
+Dataverse
+The raw data and the quantification data can also be found in
+the MassIVE repository MSV000092429:
+ftp://MSV000092429@massive.ucsd.edu/.
The data were acquired using the following setup. More information
+can be found in the source article (see References).
Sample isolation: Cell sorting was done on a Sony MA900 cell sorter +using a 130 μm sorting chip. Cells were sorted at single-cell resolution, +into a 384-well Eppendorf LoBind PCR plate (Eppendorf AG) containing 1 μL +of lysis buffer.
Sample preparation: Single-cell protein lysates were digested with +2 ng of Trypsin (Sigma cat. Nr. T6567) supplied in 1 μL of digestion +buffer (100mM TEAB pH 8.5, 1:5000 (v/v) benzonase (Sigma cat. Nr. E1014)). +The digestion was carried out overnight at 37 °C, and subsequently +acidified by the addition of 1 μL 1% (v/v) trifluoroacetic acid (TFA). +All liquid dispensing was done using an I-DOT One instrument (Dispendix).
Liquid chromatography: The Evosep one liquid chromatography system was +used for DIA isolation window survey and HRMS1-DIA experiments.The standard +31 min or 58min pre-defined Whisper gradients were used, where peptide +elution is carried out with 100 nl/min flow rate. A 15 cm × 75 μm +ID column (PepSep) with 1.9 μm C18 beads (Dr. Maisch, Germany) and a 10 +μm ID silica electrospray emitter (PepSep) was used. Both LC systems were +coupled online to an orbitrap Eclipse TribridMass Spectrometer +(ThermoFisher Scientific) via an EasySpray ion source connected to a +FAIMSPro device.
Mass spectrometry: The mass spectrometer was operated in positive +mode with the FAIMSPro interface compensation voltage set to −45 V. +MS1 scans were carried out at 120,000 resolution with an automatic gain +control (AGC) of 300% and maximum injection time set to auto. For the DIA +isolation window survey a scan range of 500–900 was used and 400–1000 +rest of the experiments. Higher energy collisional dissociation (HCD) was +used for precursor fragmentation with a normalized collision energy (NCE) +of 33% and MS2 scan AGC target was set to 1000%.
Raw data processing: The mESC raw data files were processed with +Spectronaut 17 and protein abundance tables exported and analyzed further +with python.
The data were provided by the Author and is accessible at the +Dataverse +The folder ('20240205_111248_mESC_SNEcombine_m15-m2i/') contains +the following files of interest:
20240205_111251_PEPQuant (Normal).tsv: the PSM level data
20240205_111251_Peptide Quant (Normal).tsv: the peptide level data
20240205_111251_PGQuant (Normal).tsv: the protein level data
The metadata were downloaded from the Zenodo repository.
sample_facs.csv: the metadata
We formatted the quantification table so that columns match with the
+metadata. Then, both tables are then combined in a single
+QFeatures object using the scp::readSCP() function.
The peptide data were formated to a SingleCellExperiment object and the
+sample metadata were matched to the column names and stored in the colData.
+The object is then added to the QFeatures object and the rows of the PSM
+data are linked to the rows of the peptide data based on the peptide sequence
+information through an AssayLink object.
The protein data were formated to a SingleCellExperiment object and
+the sample metadata were matched to the column names and stored in the
+colData. The object is then added to the QFeatures object and the rows
+of the peptide data are linked to the rows of the protein data based on the
+protein sequence information through an AssayLink object.
Source article: Petrosius, V., Aragon-Fernandez, P., Üresin, N. et al. +"Exploration of cell state heterogeneity using single-cell proteomics +through sensitivity-tailored data-independent acquisition." +Nat Commun 14, 5910 (2023). +(link to article).
+# \donttest{
+petrosius2023_mES()
+#> see ?scpdata and browseVignettes('scpdata') for documentation
+#> loading from cache
+#> An instance of class QFeatures containing 605 assays:
+#> [1] 20230421_EV_PAF_FAIMS_U3000_uPAC_wishDIA_plate1_scMESC_m15_1: SingleCellExperiment with 633 rows and 1 columns
+#> [2] 20230421_EV_PAF_FAIMS_U3000_uPAC_wishDIA_plate1_scMESC_m15_10: SingleCellExperiment with 2951 rows and 1 columns
+#> [3] 20230421_EV_PAF_FAIMS_U3000_uPAC_wishDIA_plate1_scMESC_m15_100: SingleCellExperiment with 918 rows and 1 columns
+#> ...
+#> [603] 20230421_EV_PAF_FAIMS_U3000_uPAC_wishDIA_plate1_scMESC_m2i_99: SingleCellExperiment with 3264 rows and 1 columns
+#> [604] peptides: SingleCellExperiment with 9884 rows and 603 columns
+#> [605] proteins: SingleCellExperiment with 4270 rows and 603 columns
+# }
+
+The PSM data were found in the bulk_PSMs.txt file. Contaminants
were defined based on the protein accessions listed in
contaminant.txt. The data were converted to a QFeatures
-object using the scp::readSCP function.
scp::readSCP() function.
The protein data were found in the bulk_Proteins.txt file.
Contaminants were defined based on the protein accessions listed
in contaminant.txt.The column names holding the quantitative
@@ -212,7 +212,7 @@
## List available datasets and their metadata
scpdata()
-#> DataFrame with 24 rows and 15 columns
+#> DataFrame with 26 rows and 15 columns
#> title dataprovider species taxonomyid genome
#> <character> <character> <character> <integer> <character>
#> EH3899 specht2019... SlavovLab ... Homo sapie... 9606 NA
@@ -119,11 +119,11 @@ Examples
#> EH3902 dou2019_mo... MassIVE Mus muscul... 10090 NA
#> EH3903 dou2019_bo... MassIVE Mus muscul... 10090 NA
#> ... ... ... ... ... ...
-#> EH8303 woo2022_ma... MassIVE Homo sapie... 9606 NA
-#> EH8304 woo2022_lu... MassIVE Homo sapie... 9606 NA
#> EH9450 gregoire20... PRIDE Homo sapie... 9606 NA
#> EH9477 khan2023 MassIVE Homo sapie... 9606 NA
#> EH9487 guise2024 MassIVE Homo sapie... 9606 NA
+#> EH9497 petrosius2... Dataverse Homo sapie... 9606 NA
+#> EH9498 petrosius2... Dataverse Homo sapie... 9606 NA
#> description coordinate_1_based maintainer rdatadateadded
#> <character> <integer> <character> <character>
#> EH3899 SCP expres... 1 Christophe... 2020-11-05
@@ -132,11 +132,11 @@ Examples
#> EH3902 SCP expres... 1 Christophe... 2020-11-05
#> EH3903 SCP expres... 1 Christophe... 2020-11-05
#> ... ... ... ... ...
-#> EH8303 Single-cel... 1 Christophe... 2023-07-03
-#> EH8304 Single-cel... 1 Christophe... 2023-07-03
#> EH9450 Single-cel... 1 Samuel Gre... 2024-02-12
#> EH9477 Single-cel... 1 Enes Sefa ... 2024-03-08
#> EH9487 Single-cel... 1 Christophe... 2024-04-08
+#> EH9497 Mouse embr... 1 Enes Sefa ... 2024-04-11
+#> EH9498 Single-cel... 1 Samuel Gre... 2024-04-11
#> preparerclass tags rdataclass
#> <character> <AsIs> <character>
#> EH3899 scpdata Experiment...,Expression...,Experiment...,... QFeatures
@@ -145,11 +145,11 @@ Examples
#> EH3902 scpdata Experiment...,Expression...,Experiment...,... QFeatures
#> EH3903 scpdata Experiment...,Expression...,Experiment...,... QFeatures
#> ... ... ... ...
-#> EH8303 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
-#> EH8304 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
#> EH9450 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
#> EH9477 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
#> EH9487 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
+#> EH9497 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
+#> EH9498 scpdata Expression...,MassSpectr...,Proteome,... QFeatures
#> rdatapath sourceurl sourcetype
#> <character> <character> <character>
#> EH3899 scpdata/sp... https://sc... CSV
@@ -158,11 +158,11 @@ Examples
#> EH3902 scpdata/do... ftp://mass... XLS/XLSX
#> EH3903 scpdata/do... ftp://mass... XLS/XLSX
#> ... ... ... ...
-#> EH8303 scpdata/wo... ftp://mass... TXT
-#> EH8304 scpdata/wo... ftp://mass... TXT
#> EH9450 scpdata/gr... https://ww... TXT
#> EH9477 scpdata/kh... https://dr... TXT
#> EH9487 scpdata/gu... ftp://mass... TXT
+#> EH9497 scpdata/pe... https://da... TXT
+#> EH9498 scpdata/pe... https://da... TXT
## Load data using the ExperimentHub interface
hub <- ExperimentHub()
@@ -188,7 +188,7 @@ Examples
annotation_fp60-97.csv: sample annotation
batch_fp60-97.csv: batch annotation
We combined the sample annotation and the batch annotation in -a single table. We also formated the quantification table so that +a single table. We also formatted the quantification table so that columns match with those of the annotation and filter only for single-cell runs. Both table are then combined in a single -QFeatures object using the scp::readSCP function.
+QFeatures object using thescp::readSCP() function.
The peptide data were taken from the Slavov lab directly
(Peptides-raw.csv). It is provided as a spreadsheet. The data
-were formated to a SingleCellExperiment object and the sample
+were formatted to a SingleCellExperiment object and the sample
metadata were matched to the column names (mapping is retrieved
after running the SCoPE2 R script) and stored in the colData.
The object is then added to the QFeatures object (containing the
PSM assays) and the rows of the peptide data are linked to the
rows of the PSM data based on the peptide sequence information
through an AssayLink object.
The protein data (Proteins-processed.csv) is formated similarly
+
The protein data (Proteins-processed.csv) is formatted similarly
to the peptide data, and the rows of the proteins were mapped onto
the rows of the peptide data based on the protein sequence
information.
annotation_fp60-97.csv: sample annotation
batch_fp60-97.csv: batch annotation
We combined the sample annotation and the batch annotation in -a single table. We also formated the quantification table so that +a single table. We also formatted the quantification table so that columns match with those of the annotation and filter only for single-cell runs. Both table are then combined in a single -QFeatures object using the scp::readSCP function.
+QFeatures object using thescp::readSCP() function.
The peptide data were taken from the Slavov lab directly
(Peptides-raw.csv). It is provided as a spreadsheet. The data
-were formated to a SingleCellExperiment object and the sample
+were formatted to a SingleCellExperiment object and the sample
metadata were matched to the column names (mapping is retrieved
after running the SCoPE2 R script) and stored in the colData.
The object is then added to the QFeatures object (containing the
PSM assays) and the rows of the peptide data are linked to the
rows of the PSM data based on the peptide sequence information
through an AssayLink object.
The protein data (Proteins-processed.csv) is formated similarly
+
The protein data (Proteins-processed.csv) is formatted similarly
to the peptide data, and the rows of the proteins were mapped onto
the rows of the peptide data based on the protein sequence
information.
The PSM data were found in the evidence.txt (in the
Experiment 1+ 2) folder. The PSM data were filtered so that it
contains only samples that are annotated. The data were then
-converted to a QFeatures object using the scp::readSCP
+converted to a QFeatures object using the scp::readSCP()
function.
The peptide data were found in the peptides.txt file. The column
names holding the quantitative data were adapted to match the
@@ -190,7 +190,7 @@