4  Action list

List and explanation of the different Shiny actions available in the app, along with their functions and usage.

Table 4.1:

Current available actions in app, see file type
icon action description prerequite_type output_type

correct batch effects 2: fb 2: fb

extract features from FCS concat with clustering 3: sce 5: fe

import OMIQ FE 0: none 5: fe

import FCS concat 0: none 5: fe

control quality of features 5: fe 6: html

analyse abundancy of clusters - univar 5: fe 6: html

analyse intensity of clusters - univar 5: fe 6: html

import .rds object 0: none 5: fe

export to phantasus 5: fe 4: files

filter feature set 5: fe 5: fe

export .rds to spreadsheet 5: fe 2: fb

create feature set variable 5: fe 5: fe

merge clusters 3: sce 3: sce

4.1 Batch Correction

mod_correct_batch

4.2 Extract Clustering Features

mod_extract_sce

Functionality Overview:

This panel allows users to extract features from a specified cell set. Users can define State Markers and choose their preferred clustering method.

Instructions:

  1. Specify State Markers:
    • Use the provided input field to specify State Markers.
  2. Select Clustering Method:
    • Choose a clustering method from the dropdown menu. This selection determines how the cells will be grouped.
  3. Run Feature Extraction:
    • Once you have specified the State Markers and selected a clustering method, click on the Extract Features button to initiate the process.

Additional Information:

  • Accurate specification of State Markers and the appropriate choice of clustering method are essential for effective feature extraction.

4.3 Import Features

mod_import_feature

Functionality Overview:

The "Import from Feature" panel allows users to import various data tables such as abundance, MFI (Mean Fluorescence Intensity), and metadata tables. All these tables should be linked by a common ID. the user will soon be able to reference the type or state status of markers and a clustering annotation.

Instructions:

  1. Browse and Select Files:
    • Click on the Browse button to select the data files you wish to import.
    • You can import files for abundance data, MFI data, and metadata.
  2. Ensure Common ID Column:
    • Make sure that each table has a common ID column. This ID is essential for creating the bound between the different tables.
  3. Upload Files:
    • After selecting the files, complete widgets. The widgets are set by default to OMIQ output values.

Additional Information:

  • This import functionality is designed especially for OMIQ exported features.

4.4 Import Concatenated .fcs

mod_import_concat

Functionality Overview:

This panel allows users to import a concatenated Flow Cytometry Standard (.fcs) file representing a complete experiment. Each .fcs file within the concatenated file should be annotated and assigned to specific biological samples, studied conditions, etc. Additionally, users must provide a metadata file (in .xlsx or .csv format) containing an .fcs ID to identify the origin of cells.

Instructions:

  1. Uploading Files:
    • If using this application on a server, first upload your files.
    • Click on the ‘Browse’ button (max file size: 500MB) to select and upload your concatenated .fcs file and metadata file.
  2. Selecting .fcs Directory:
    • After uploading, select the ‘uploaded_fcs’ directory containing the fcs file by clicking on ‘Select your .fcs directory’.
  3. Metadata File Upload:
    • Upload the metadata file that includes the .fcs IDs.
    • Ensure that the metadata file correctly maps each .fcs ID to its respective sample and condition.

File Requirements:

  • Concatenated .fcs file must include properly annotated data with clustering and origine fcs id of each cells.

Marker Selection:

  • Once the concatenated .fcs is loaded, manually select the marker of interest by clicking in the table.

4.5 Quality Control

mod_qc

Functionality Overview:

The Quality Control action tab provides an interactive interface for users to generate customized quality control reports. Users can set parameters, define thresholds, and choose various options for their report.

Instructions:

  1. General Parameters:
    • In the General Parameters box, you can use the widgets to choose general annotations for your report.
  2. Threshold Settings:
    • Navigate to the Threshold box to set cutoffs for your analysis, like the minimum number of cells for each cluster.
  3. ON/OFF Options:
    • In the ON/OFF box, use boolean widgets to customize your report.
    • Options may include showing the code used for analysis or ordering the graphs in a specific manner.
  4. Run the Report:
    • After setting all parameters and options, click on the RUN THE REPORT button.

4.6 Differential Abundance

mod_da

Functionality Overview:

The Differential Analysis Panel is designed for users to conduct comprehensive differential analyses.

It includes advanced settings for thresholds, like p-value and fold change, and a dedicated box for setting univariate parameters to define the differential analysis design.

Instructions:

  1. General Parameters:
    • Define the Condition column on which the differential comparison is to be performed.
  2. Threshold Settings:
    • In the Thresholds box, set critical values for your analysis.
    • This includes specifying a p-value threshold for statistical significance and a fold change threshold to identify meaningful differences.
  3. Univariate Parameters:
    • Navigate to the Univariate Parameters tab to configure the "patient effect" or the sample identifier use as random effect in LMM methods.
  4. Run Analysis:
    • After setting all thresholds and univariate parameters, initiate the differential analysis by clicking the RUN THE REPORT button.

4.7 Differential State

mod_ds

Functionality Overview:

The Differential Analysis Panel is designed for users to conduct comprehensive differential analyses.

It includes advanced settings for thresholds, like p-value and fold change, and a dedicated box for setting univariate parameters to define the differential analysis design.

Instructions:

  1. General Parameters:
    • Define the Condition column on which the differential comparison is to be performed.
  2. Threshold Settings:
    • In the Thresholds box, set critical values for your analysis.
    • This includes specifying a p-value threshold for statistical significance and a fold change threshold to identify meaningful differences.
  3. Univariate Parameters:
    • Navigate to the Univariate Parameters tab to configure the "patient effect" or the sample identifier use as random effect in LMM methods.
  4. Run Analysis:
    • After setting all thresholds and univariate parameters, initiate the differential analysis by clicking the RUN THE REPORT button.

4.8 Import R Object

mod_import_rds

Functionality Overview:

This panel enables users to import an existing .rds file that corresponds to a previously processed experiment.

Instructions:

  1. Browse File: Click on the Browse button to navigate through your local directory.
  2. Select File: Look for the .rds file you wish to import. These files represent pre-processed .fcs data.
  3. Confirmation: Once the file is successfully uploaded, a confirmation message will be displayed.
  4. Next Steps: After importing, you can proceed to new actions in the Select an action box.

Additional Information:

  • This panel is designed to streamline the workflow by allowing easy access to pre-processed data.

4.9 Export to Phantasus

mod_export_ph

Functionality Overview:

This panel facilitates the export of your data to Excel format for advanced analysis in Phantasus.

Downloadable Excel Files:

  • MFI Split by Markers: Export the MFI data, providing detailed insights split by each marker.
  • Median of MFI: Obtain the median MFI values for each .fcs file and marker.
  • Abundance Matrix: Generate an abundance matrix displaying the percentage of cells in each cluster for each .fcs file.

4.10 Filter Feature Set

mod_filter_fe

Functionality Overview:

Planned key features include:

  • Subsetting the Dataset/Metadata Table: This feature would allow users to focus on specific subsets of samples.
  • Subset Markers: Users could choose to analyze specific markers from a larger set, aiding in focused analyses on relevant markers.
  • Subset Clusters

Instructions:

  1. Save Feature:
    • Click on the SAVE FEATURE button to save a .rds feature set in a new <hash folder>.

4.11 Export .rds to Spreadsheet

mod_export_rds

Functionality Overview:

Instructions:

  1. Save Feature:
    • Export to spreadsheet.

4.12 Create Feature Set Variable

mod_create_variable

Functionality Overview:

Planned key features include:

  • Reannotation of Dataset/Metadata Table: Users would be able to modify existing annotations or add new ones to refine the data interpretation.
  • Conversion of Numeric Variables to Factors: This would enhance categorical analysis by converting numeric variables into factor variables, facilitating grouping and comparison.

Instructions:

  1. Save Feature:
    • Click on the SAVE FEATURE button to save a .rds feature set in a new <hash folder>.

4.13 Merge Clusters

mod_merge_clusters

Functionality Overview:

Planned key feature :

  • Redefine Clusters (Merge): It would facilitate the merging of similar clusters to reduce complexity or enhance the statistical power of the analysis.

Instructions:

  1. Save Feature:
    • Click on the SAVE FEATURE button to save a .rds feature set in a new <hash folder>.