Package: Sconify 1.27.0

Tyler J Burns

Sconify: A toolkit for performing KNN-based statistics for flow and mass cytometry data

This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

Authors:Tyler J Burns

Sconify_1.27.0.tar.gz
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Sconify.pdf |Sconify.html✨
Sconify/json (API)

# Install 'Sconify' in R:
install.packages('Sconify', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • bz.gmcsf.final - Bodenmiller-Zunder GM-CSF post-SCONE final data
  • bz.gmcsf.final.norm.scale - Bodenmiller-Zunder GM-CSF post-SCONE final data, that's been quantile normalized and z scored.
  • exist - Random musing
  • funct.markers - Functional markers from the Wanderlust dataset.
  • input.markers - Input markers for the Wanderlust dataset
  • markers - Markers for the Wanderlust dataset
  • wand.combined - Wanderlust data combined basal and IL7 cells
  • wand.final - Post-scone output of the "combiend" Wanderlust data.
  • wand.ideal.k - A named vector to help the user determine the ideal k for the Wanderlust dataset.
  • wand.il7 - Wanderlust IL7 data
  • wand.scone - Wanderlust scone output

On BioConductor:Sconify-1.27.0(bioc 3.21)Sconify-1.26.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologysinglecellflowcytometrysoftwaremultiplecomparisonvisualization

4.74 score 11 scripts 197 downloads 1 mentions 17 exports 54 dependencies

Last updated 23 days agofrom:e6f83c28cf. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:FcsToTibbleFnnGetKnnDeGetMarkerNamesImputeTestingMakeHistMakeKnnListMeaningOfLifeParseMarkersPostProcessingProcessMultipleFilesQuantNormalizeElementsSconeValuesSplitFileStringToNumbersSubsampleAndTsneTsneVis

Dependencies:BHBiobaseBiocGenericsbitbit64clicliprcolorspacecpp11crayoncytolibdplyrfansifarverflowCoreFNNgenericsggplot2gluegtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmepillarpkgconfigprettyunitsprogressR6RColorBrewerRcppreadrRhdf5librlangRProtoBufLibRtsneS4Vectorsscalestibbletidyselecttzdbutf8vctrsviridisLitevroomwithr

Assessing quality of CyTOF data with KNN

Tyler J Burns

Rendered fromDataQuality.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-02-21
Started: 2017-10-19

Step 3: Post-Processing

Tyler J Burns

Rendered fromStep3.PostProcessing.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-02-18
Started: 2017-10-04

Finding Ideal K

Tyler J Burns

Rendered fromFindingIdealK.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-02-26
Started: 2017-10-11

Step 2: The Scone Workflow

Tyler J Burns

Rendered fromStep2.TheSconeWorkflow.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-02-18
Started: 2017-10-04

Step 1: Pre-Processing

Tyler J Burns

Rendered fromStep1.PreProcessing.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-02-26
Started: 2017-10-04

Readme and manuals

Help Manual

Help pageTopics
Add tSNE to your results.AddTsne
Bodenmiller-Zunder GM-CSF post-SCONE final databz.gmcsf.final
Bodenmiller-Zunder GM-CSF post-SCONE final data, that's been quantile normalized and z scored.bz.gmcsf.final.norm.scale
Random musingexist
Takes a file as input and returns a data frame of cells by featuresFcsToTibble
Compute knn using the fnn algorithmFnn
Functional markers from the Wanderlust dataset.funct.markers
Get the KNN density estimaionGetKnnDe
Takes in an example file as input and returns all marker namesGetMarkerNames
Imputes values for all markers (used as input) for each cellImpute
Impute testingImputeTesting
Input markers for the Wanderlust datasetinput.markers
Log transform the q valuesLogTransformQ
make.histMakeHist
Make list of cells by features for each KNN memberMakeKnnList
Markers for the Wanderlust datasetmarkers
Meaning of lifeMeaningOfLife
Runs a t test on the medians or means of multiple donors for the same conditionMultipleDonorStatistics
Parse markers contained in a Sconify-directed marker fileParseMarkers
Post-processing for scone analysks.PostProcessing
Converts multiple files into a concatenated data frameProcessMultipleFiles
Corrects all p values for multiple hypotheses, sets threshold for which change values should be reportedQCorrectionThresholding
Performs quantile normalization on the data frame (patient) of interestQuantNormalize
Takes a list of tibbles as input, and performs per-column quantile normalization, then outputs the quantile normalized listQuantNormalizeElements
Performs a series of statistical tests on the batch of cells of interest.RunStatistics
Master function for per-knn statistics functionality, integrating the other non-exported functions within this script.SconeValues
Runs "process.multiple.files" on a single file, splits it randomly, and presends half of it is "unstim" and half of it is "stim"SplitFile
Transform strings to numbers.StringToNumbers
Subsample data and run tSNESubsampleAndTsne
Plot a tSNE map colored by a marker of interestTsneVis
Wanderlust data combined basal and IL7 cellswand.combined
Post-scone output of the "combiend" Wanderlust data.wand.final
A named vector to help the user determine the ideal k for the Wanderlust dataset.wand.ideal.k
Wanderlust IL7 datawand.il7
Wanderlust scone outputwand.scone