Reference

Plotting Functions

Functions used to plot the results of the data analysis

plot_gates()

Plot Decision trees as gates

plot_flowstyle()

Plots data as faceted scatterplot emulating the ouput of flow cytometry

Functions to handle tree models

Different functions that fit, visualize, output and print tree based models

get_concensus_rules()

Consolidates the rules in a decision tree per cluster

print(<concensus.rules>)

Prints concensus rules

get_cluster_mapping()

Gets the corresponding cluster for each terminl node in a classification tree

fit_ctree()

Fits a classification tree on a Seurat object

as.garnett() print(<garnett.list>)

Converts a classification tree to a garnett output

cross_validate()

Fits a decision tree in one data set and tests the performance in another

Marker Finding functions

Functions with output consistent with Seurat equivalents, but allow using other classifiers (such as RangerDE).

FindAllMarkers()

Gene expression markers for all identity classes

FindConservedMarkers()

Finds markers that are conserved between the groups

FindMarkers()

Gene expression markers of identity classes

Handle Gene names

Utility functions to find gene names and their common aliases (ie. ITGAX <> CD11c)

get_genesymbols()

Gets the official gene symbol for protein or gene aliases

get_aliases()

Gets the aliases for a gene names

is_gene_membrane()

Queries gene symbols and returns if they are annotated as membrane-localized

Antibodies

Query antibody vendors for products that match your gene of interest.

query_cc_antibodies() query_sc_antibodies() query_biocompare_antibodies() query_biolegend_antibodies()

Query Antibodies from multiple vendors

Datasets

Small versions of sample datasets, they are commonly used to test the package functionality and have reproducible examples.

small_5050_mix

A random subset of genes and cells from a 50:50 mixture of 293T:Jurkat cells

small_9901_mix

A random subset of genes and cells from a 99:1 mixture of 293T:Jurkat cells

Utilities

as.data.frame(<Seurat>)

Gets a data frame from a Seurat object

as.frequency.matrix()

Convert confusion matrices and tables to frequency matrices

autoplot(<table>) autoplot(<matrix>)

autoplot.table

ranger_importances() FindAllMarkers_ranger.Seurat()

ranger_importances