CIPR: Cluster Identity Predictor

In atakanekiz/CIPR-Package: Cluster Identity Predictor

Cluster Identity Predictor

Description

CIPR (Cluster Identity PRedictor) is a pipeline that helps annotating unknown single cell clusters in single cell RNA sequencing (scRNAseq experiments). This function scores unknown cluster gene expression signatures against known reference datasets using user-selected analytical approaches to facilitate scRNAseq analysis.

Usage

CIPR(
  input_dat,
  comp_method = "logfc_dot_product",
  reference = NULL,
  select_ref_subsets = "all",
  custom_reference = NULL,
  custom_ref_annot = NULL,
  keep_top_var = 100,
  plot_ind = F,
  plot_top = T,
  top_num = 5,
  save_png = F,
  global_plot_obj = T,
  global_results_obj = T,
  update_ref = T,
  ...
)

Arguments

input_dat	Data frame containing normalized log2-transformed gene expression values per cluster OR a table of differentially expressed genes per cluster
comp_method	Method to use for identity score calculations. It accepts one of the following: "logfc_dot_product" (default), "logfc_spearman", "logfc_pearson", "all_genes_spearman", "all_genes_pearson"
reference	Reference data frame containing gene expression data from known cell types. It accepts one of the following: "immgen" (default), "mmrnaseq", "blueprint", "hpca", "dice", "hema", "hsrnaseq", "custom"
select_ref_subsets	The names of cell subsets to be included in the analysis. For using the entire reference dataset use "all", or provide a character vector of cell types of interest. Defaults to "all"
custom_reference	A data frame containing custom reference. There must be a column named 'gene' and other columns contain normalized gene expression data from known samples. Defaults to NULL
custom_ref_annot	A data frame containing custom reference metadata. This is optional to get more informative results from CIPR. The data frame must contain columns named 'short_name' (must match column names in custom reference), 'long_name' (human readable names for reference samples), 'description' (details such as positive and negative sorting markers), 'reference_cell_type' (e.g. T cell, B cell, NK)
keep_top_var	Top n percent of highly variant reference genes to include in the analysis. It accepts a numeric value smaller than or equal to 100 (default). The value of 100 results in keeping all the genes in the reference dataset)
plot_ind	Logical value. Set it to TRUE to plot identity scores for each cluster. Defaults to FALSE
plot_top	Logical value. set it to TRUE to plot top scoring reference cell types for each cluster. Defaults ot TRUE.
top_num	A numeric value determining how many top scoring reference cell types will be plotted for each cluster. Defaults to 5.
save_png	Logical value. Set it to TRUE if you would like to export png images of the results. Defaults to FALSE
global_plot_obj	Logical value. Set it to TRUE if you would like to keep the plots as an object in the global environment. This can be useful for accessing and manipulating the graphs. Defaults to TRUE.
global_results_obj	Logical value. Set it to TRUE if you would like to keep the analysis results as a global object. Defaults to TRUE.
...	arguments to pass to theme() (for graph manipulation)

Value

Graphical outputs and/or data frames of identity scores calculated for each cluster in the input data.

Examples

# Example of using CIPR in conjunction with Seurat
library(Seurat)
allmarkers <- FindAllMarkers(seurat_object)
avgexp <- AverageExpression(seurat_object)

# Using built-in immgen as reference and logfc dot product method
CIPR(input_dat = allmarkers,
comp_method = "logfc_dot_product",
reference="immgen",
keep_top_var = 100,
global_results_obj = T,
plot_top = T)

#' # Using built-in immgen as reference and all genes spearman method

CIPR(input_dat = avgexp,
comp_method = "all_genes_spearman",
reference="immgen",
keep_top_var = 100,
global_results_obj = T,
plot_top = T)


# Using built-in dice reference and logFC spearman method
# Variance threshold of top 50%

CIPR(input_dat = allmarkers,
comp_method = "logfc_spearman",
reference="dice",
keep_top_var = 50,
global_results_obj = T,
plot_top = T)


# Using a custom reference

CIPR(input_dat = allmarkers,
comp_method = "logfc_dot_product",
reference="custom",
custom_ref_dat_path = custom_ref_df,
custom_ref_annot_path = custom_annot_df,
keep_top_var = 100,
global_results_obj = T,
plot_top = T)


# Using a blueprint-encode reference and limiting the analysis
# to "Pericytes", "Skeletal muscle", "Smooth muscle"

CIPR(input_dat = allmarkers,
comp_method = "logfc_dot_product",
reference="blueprint-encode",
select_ref_subsets = c("Pericytes", "Skeletal muscle", "Smooth muscle")
keep_top_var = 100,
global_results_obj = T,
plot_top = T)

# Using built in example data (logFC signatures per cluster)
CIPR(input_dat = example_logfc_data,
comp_method = "logfc_dot_product",
reference="blueprint-encode",
select_ref_subsets = c("Pericytes", "Skeletal muscle", "Smooth muscle")
keep_top_var = 100,
global_results_obj = T,
plot_top = T)


# Using built in example data (average expression)
CIPR(input_dat = example_avgexp_data,
comp_method = "all_spearman",
reference="immgen",
select_ref_subsets = "all",
keep_top_var = 100,
global_results_obj = T,
plot_top = T)

atakanekiz/CIPR-Package documentation built on March 28, 2022, 12:08 p.m.