Introduction | Contribution via an external source | Contribution via a pull request | Getting started with GitHub | What do we expect? | QFeatures object | Feature data | Sample annotations | Experiment description | Data source information | Folder structure | inst/scripts/ | R/ | man/ | Workflow | 1. Collect data | 2. Create the QFeatures object | 3. Document the dataset | 4. Update metadata | 5. Create a pull request | 6. Almost done!

Introduction | Contribution via an external source | Contribution via a pull request | Getting started with GitHub | What do we expect? | QFeatures object | Feature data | Sample annotations | Experiment description | Data source information | Folder structure | inst/scripts/ | R/ | man/ | Workflow | 1. Collect data | 2. Create the QFeatures object | 3. Document the dataset | 4. Update metadata | 5. Create a pull request | 6. Almost done!

The scpdata package | Load data from ExperimentHub | Data sets information | Data manipulation | Session information | License

The scpdata package | Load data from ExperimentHub | Data sets information | Data manipulation | Session information | License

Installation | Foreword - Preparation of pipeline results to be visualized | Introduction | Example dataset (more details in CytoPipeline vignette) | Example of pre-processing and QC pipelines (more details in CytoPipeline vignette) | Interactive visualizations | Visualizing pipeline runs at different steps | Visualization of scale transformations | Session information | References

Introduction | Installation and loading dependencies | Dataset | Generation of input files for the Shiny application | Visualization of the MDS projection | fsap dataset methods | Data mining and assembly of a reference dataset | Pairwise alignments using primary structure and hydrophobicity | Physicochemical parameters distinguishing FSAP | Acknowledgement | Session information | References

Installation and loading dependencies | Introduction | Illustrative dataset | Generation of input files | Visualization of the MDS projection | General settings | Session information | References

Introduction | Example data set | Data modelling | Peptide filtering | Model exploration: analysis of variance | Model exploration: differential abundance analysis | Model exploration: component analysis | Batch correction | Session information | License | Reference

Introduction | Example data set | Data modelling | Peptide filtering | Model exploration: analysis of variance | Model exploration: differential abundance analysis | Model exploration: component analysis | Batch correction | Session information | License | Reference

The scp data framework | Run identifier column (runCol) | Feature annotations | colData table | readSCP() | Sample names | Special case: empty samples | Running readSCP | Under the hood | License | Reference

The scp package | Before you start | Read in SCP data | Clean missing data | Filter PSMs | Filter features based on feature annotations | Filter assays based on detected features | Filter features based on SCP metrics | Filter features to control for FDR | Process the PSM data | Relative reporter ion intensity | Aggregate PSM data to peptide data | Join the SCoPE2 sets in one assay | Filter single-cells | Filter samples of interest | Filter based on the median relative intensity | Filter based on the median CV | Process the peptide data | Normalization | Remove peptides with high missing rate | Log-transformation | Aggregate peptide data to protein data | Process the protein data | Imputation | Batch correction | Dimension reduction | PCA | UMAP | Monitoring data processing | Session information | License | Reference

The scp data framework | Run identifier column (runCol) | Feature annotations | colData table | readSCP() | Sample names | Special case: empty samples | Running readSCP | Under the hood | License | Reference

The scp package | Before you start | Read in SCP data | Clean missing data | Filter PSMs | Filter features based on feature annotations | Filter assays based on detected features | Filter features based on SCP metrics | Filter features to control for FDR | Process the PSM data | Relative reporter ion intensity | Aggregate PSM data to peptide data | Join the SCoPE2 sets in one assay | Filter single-cells | Filter samples of interest | Filter based on the median relative intensity | Filter based on the median CV | Process the peptide data | Normalization | Remove peptides with high missing rate | Log-transformation | Aggregate peptide data to protein data | Process the protein data | Imputation | Batch correction | Dimension reduction | PCA | UMAP | Monitoring data processing | Session information | License | Reference

The QFeatures class | Accessing the data | Quantitative data | Feature metadata | Sample metadata | Subsetting the data | Subset assays | Subset samples | Subset features | Common processing steps | Missing data assignment | Feature aggregation | Normalization | Log transformation | Imputation | Data visualization | Session information | License | Reference

The QFeatures class | Accessing the data | Quantitative data | Feature metadata | Sample metadata | Subsetting the data | Subset assays | Subset samples | Subset features | Common processing steps | Missing data assignment | Feature aggregation | Normalization | Log transformation | Imputation | Data visualization | Session information | License | Reference

Installation and loading dependencies | Introduction | Illustrative dataset | Pairwise sample Earth Mover's Distances | Calculating distances between samples | Individual marker contribution in the distance matrix | Metric Multidimensional scaling | Calculating the MDS projection | Plotting the MDS projection | Quality of projection - diagnostic tools | Additional options for the MDS projection | Aid to interpreting projection axes | Bi-plots | Bi-plot wrapping | Handling large datasets | Loading flow frames dynamically during distance matrix computation | Using BiocParallel to parallelize distance matrix computation | Expression matrices as input instead of flowFrames | Session information | References

Introduction | Installation | CT genes | CT gene selection | Testis-specific expression | Activation in cancer cell lines and TCGA tumors | IGV visualisation | Regulation by methylation | Available functions | Expression in normal healthy adult tissues | GTEX_expression() | normal_tissue_expression_multimapping() | testis_expression() | oocytes_expression() | HPA_cell_type_expression() | Expression in fetal cells | embryo_expression() | fetal_germcells_expression() | hESC_expression() | Expression in cancer cells and samples | CCLE_expression() | CT_correlated_genes() | TCGA_expression() | Methylation analysis | DAC_induction() | normal_tissues_methylation() | normal_tissues_mean_methylation() | embryos_mean_methylation() | fetal_germcells_mean_methylation() | hESC_mean_methylation() | TCGA_methylation_expression_correlation() | Interactive heatmaps | Bibliography | Session information

Introduction | Installation | CT genes | CT gene selection | Testis-specific expression | Activation in cancer cell lines and TCGA tumors | IGV visualisation | Regulation by methylation | Available functions | Expression in normal healthy adult tissues | GTEX_expression() | normal_tissue_expression_multimapping() | testis_expression() | oocytes_expression() | HPA_cell_type_expression() | Expression in fetal cells | embryo_expression() | fetal_germcells_expression() | hESC_expression() | Expression in cancer cells and samples | CCLE_expression() | CT_correlated_genes() | TCGA_expression() | Methylation analysis | DAC_induction() | normal_tissues_methylation() | normal_tissues_mean_methylation() | embryos_mean_methylation() | fetal_germcells_mean_methylation() | hESC_mean_methylation() | TCGA_methylation_expression_correlation() | Interactive heatmaps | Bibliography | Session information

Installation | Introduction | Example dataset | Example of pre-processing and QC pipelines | Building the CytoPipeline | preliminaries: paths definition | first method: step by step, using CytoPipeline methods | second method: in one go, using JSON file input | Executing pipelines | Executing PeacoQC pipeline | Executing flowAI pipeline | Inspecting results and visualization | Plotting processing queues as workflow graphs | Obtaining information about pipeline generated objects | Retrieving flow frames at different steps and plotting them | Example of retrieving another type of object | Getting and plotting the nb of retained events are each step | Interactive visualization | Adding function wrappers - note on the CytoPipelineUtils package | Session information | References

Installation | Introduction | Example dataset | Example of pre-processing and QC pipelines | Building the CytoPipeline | preliminaries: paths definition | first method: step by step, using CytoPipeline methods | second method: in one go, using JSON file input | Executing pipelines | Executing PeacoQC pipeline | Executing flowAI pipeline | Inspecting results and visualization | Plotting processing queues as workflow graphs | Obtaining information about pipeline generated objects | Retrieving flow frames at different steps and plotting them | Example of retrieving another type of object | Getting and plotting the nb of retained events are each step | Interactive visualization | Adding function wrappers - note on the CytoPipelineUtils package | Session information | References

Introduction | Installation | Available data | Normal adult tissues | GTEX data | Normal tissue gene expression | Methylation in normal adult tissues | Testis scRNAseq data | Oocytes scRNAseq data | Human Protein Atlas scRNAseq data | Human Protein Atlas cell type specificity data | Fetal cells | Fetal germ cell scRNAseq data | Methylation in fetal germ cells (scWGBS) | Embryonic stem cells RNA-Seq data | Methylation in embryonic stem cells | Early embryo scRNA-seq data | Methylation in early embryo | Demethylated gene expression | Tumor cells | CCLE data | TCGA data | CT genes determination | All genes | CT genes | Session information

Introduction | Installation | Available data | Normal adult tissues | GTEX data | Normal tissue gene expression | Methylation in normal adult tissues | Testis scRNAseq data | Oocytes scRNAseq data | Human Protein Atlas scRNAseq data | Human Protein Atlas cell type specificity data | Fetal cells | Fetal germ cell scRNAseq data | Methylation in fetal germ cells (scWGBS) | Embryonic stem cells RNA-Seq data | Methylation in embryonic stem cells | Early embryo scRNA-seq data | Methylation in early embryo | Demethylated gene expression | Tumor cells | CCLE data | TCGA data | CT genes determination | All genes | CT genes | Session information

About this vignette | Modify the quantitative data | Create a new assay | Overwrite an existing assay | Check for validity | Modify the sample annotations | Modify the feature annotations | Create a new function for scp | What's next? | Session information | License

Introduction | Minimal data processing | Report missing values | Advanced criteria | Jaccard index distribution | Assessing the total sensitivity | License | Reference

About this vignette | Modify the quantitative data | Create a new assay | Overwrite an existing assay | Check for validity | Modify the sample annotations | Modify the feature annotations | Create a new function for scp | What's next? | Session information | License

Introduction | Minimal data processing | Report missing values | Advanced criteria | Jaccard index distribution | Assessing the total sensitivity | License | Reference

Introduction | Background information | Illustrating dataset | Specifying the pipeline | Running the pipeline | Visualizing the results | Comparing pipelines | Example with two different QC methods | Visualizing scale transformations | Defining technical run parameters | Session information

Introduction | Background information | Illustrating dataset | Specifying the pipeline | Running the pipeline | Visualizing the results | Comparing pipelines | Example with two different QC methods | Visualizing scale transformations | Defining technical run parameters | Session information

Introduction | Background information | Illustrating dataset | Specifying the pipeline | Running the pipeline | Visualizing the results | Comparing pipelines | Example with two different QC methods | Visualizing scale transformations | Defining technical run parameters | Session information