Package 'InteRD'

Title: The Integrated and Robust Deconvolution
Description: We developed the Integrated and Robust Deconvolution algorithm to infer cell-type proportions from target bulk RNA-seq data. This package is able to effectively integrate deconvolution results from multiple scRNA-seq datasets and calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Moreover, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system.
Authors: Chixiang Chen [cre, aut], Yuk Yee Leung [aut], Matei Lonita [aut], Li-San Wang [aut], Mingyao Li [aut]
Maintainer: Chixiang Chen <[email protected]>
License: Artistic-2.0
Version: 0.1.1
Built: 2024-11-14 05:48:22 UTC
Source: https://github.com/chencxxy28/interd

Help Index

Evaluation for estimated cell type proportions

Description

Several evaluation metrics are provided, such as mean absolute deviance ('MAD'), Kendall-tau correlation coefficient ('Ken'), Pearson correlation coefficient ('Cor'), given true cell type proportions.

Usage

evaluate(est.prop,true.prop)

Arguments

est.prop

The estimated cell type proportions.
true.prop

The True cell type proportions

Value

Cell-type level evaluations based on MAD, Ken, and Pearson ('cell.type.eva'), and overall evaluations based on averaged MAD, Ken, and Pearson ('all.eva').

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
pseudo.seger<-readRDSFromWeb(paste0(urlremote,"pseudo.seger.rds"))
true_p<-readRDSFromWeb(paste0(urlremote,"true_p.rds"))
SCDC_ENSEMBLE_MAD<-readRDSFromWeb(paste0(urlremote,"SCDC_ENSEMBLE_MAD_seger.rds"))
evaluate(SCDC_ENSEMBLE_MAD,true_p)$all.eva

Pseudo bulk data generation function

Description

This function generates a pseudo bulk samples by random sampled number of cells per subject.

Usage

generateBulk(eset,ct.varname,sample,disease = NULL,ct.sub,prop_mat = NULL,
nbulk = 50,samplewithRep = FALSE,low_s = 0.3,upp_s = 0.7)

Arguments

eset

The 'ExpressionSet' object for single cells.
ct.varname

Variable name for 'cell types'.
sample

Variable name for subject/samples.
disease

Indicate the health condition of subjects.
ct.sub

A subset of cell types that are selected to construct pseudo bulk samples. If NULL, then all cell types are used.
prop_mat

Manually input the cell-type proportion for pseudo bulk samples.
nbulk

The number of pseudo bulk samples to be constructed.
samplewithRep

Logical, randomly sample single cells with replacement. Default is F.
low_s

Lower support a for uniform distribution U[a,b].
upp_s

Upper support b for uniform distribution U[a,b].

Value

Pseudo bulk samples in the format of 'ExpressionSet', and the true cell-type proportions.

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
seger<-readRDSFromWeb(paste0(urlremote,"segerstolpe.rds"))

##generate a pseudo bulk data with two samples
set.seed(1234567)
pseudo.seger<-generateBulk(seger[["sc.eset.qc"]], ct.varname = "cluster",
sample = "sample", ct.sub = c("alpha","beta","delta","gamma"),
nbulk = 2, low_s = 0.3, upp_s = 0.7)

Extract the estimated proportions from InteRD

Description

This function extract estimated cell type proportions via InteRD1 and InteRD2.

Usage

InteRD.predict.prop(InteRD.output)

Arguments

InteRD.output

An object from InteRD1 or InteRD2.

Value

Estimated cell type proportions from InteRD.

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
InteRD1.output<-readRDSFromWeb(paste0(urlremote,"InteRD1.output.rds"))
lambda_option<-0
cell_type_unique<-c("alpha","beta","delta","gamma")
InteRD1<-InteRD.predict.prop(InteRD.output=InteRD1.output)

The InteRD1 estimate from reference ensemble

Description

This function provides a reference-based deconvolution by resembling all estimated cell-type proportions based on each reference set.

Usage

InteRD1(bulk.data,list_marker,cell_type_unique,comb_used,
lambda_option,tol=1e-06)

Arguments

bulk.data

The 'ExpressionSet' object for a target bulk data.
list_marker

A list of pre-specified marker genes corresponding to each cell type.
cell_type_unique

A list of cell types. It should match the order in list.marker.
comb_used

A list of pre-estimated cell type proportions based on different references.
lambda_option

A sequence of values for the tuning parameter.
tol

A tolerance value for convergence. The default is 1e-06

Value

A list containing estimated cell type proportions corresponding to each tuning value, named 'est'; and a sequence of goodness-of-fit values corresponding to each tuning value, named 'metrics'. The smaller the better; and a list of weights corresponding to each tuning value, named 'weights_list'.

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
pseudo.seger<-readRDSFromWeb(paste0(urlremote,"pseudo.seger.rds"))
comb<-readRDSFromWeb(paste0(urlremote,"comb_seger.rds"))
list_marker<-readRDSFromWeb(paste0(urlremote,"list_markerbaron20.rds"))
lambda_option<-0
cell_type_unique<-c("alpha","beta","delta","gamma")
InteRD1.output<-InteRD1(bulk.data =pseudo.seger,list_marker,
cell_type_unique,comb_used=comb,lambda_option,tol=1e-02)
InteRD1<-InteRD.predict.prop(InteRD.output=InteRD1.output)

The InteRD2 estimate

Description

This function provides a robust deconvolution framework to integrate information from scRNA-seq references, marker genes, and prior biological knowledge.

Usage

InteRD2(bulk.data,list_marker,cell_type_unique,comb_sampled,ave_est,ave_sd,
lambda_option,tol=0.0005)

Arguments

bulk.data

The 'ExpressionSet' object for a target bulk data.
list_marker

A list of pre-specified marker genes corresponding to each cell type.
cell_type_unique

A list of cell types. It should match the order in list.marker.
comb_sampled

A pre-specified cell type proportions for the target bulk data, which could be obtained from reference-based deconvolution approach.
ave_est

A pre-specified population-level cell type proportions, which could be obtained from single-cell RNA-seq and external expression data from different studies, species, or data types
ave_sd

A pre-specified standard deviation for cell-type proportion estimation. The default is 1 for each cell type.
lambda_option

A sequence of values for the tuning parameter.
tol

A tolerance value for convergence. The default is 0.0005.

Value

A list containing estimated cell type proportions corresponding to each tuning value, named 'est'; and a sequence of goodness-of-fit values corresponding to each tuning value, named 'metrics'. The smaller the better.

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
pseudo.seger<-readRDSFromWeb(paste0(urlremote,"pseudo.seger.rds"))
InteRD1<-readRDSFromWeb(paste0(urlremote,"InteRD1.rds"))
ave_est<-readRDSFromWeb(paste0(urlremote,"ave_est.rds"))
ave_sd<-readRDSFromWeb(paste0(urlremote,"ave_sd.rds"))
list_marker<-readRDSFromWeb(paste0(urlremote,"list_markerbaron20.rds"))
lambda_option<-0
cell_type_unique<-c("alpha","beta","delta","gamma")
lambda_option<-10e+05
InteRD2.output<-InteRD2(bulk.data=pseudo.seger,list_marker,cell_type_unique,
comb_sampled=InteRD1,ave_est,ave_sd,lambda_option=lambda_option,tol=0.01)
InteRD2<-InteRD.predict.prop(InteRD.output=InteRD2.output)

Calculate the population-level cell type proportions from a single-cell data.

Description

Calculate population-level cell type proportions from single-cell data.

Usage

pop.ct.prop.scRNA(scRNA,cluster="cluster",sample="sample",cell_type_unique)

Arguments

scRNA

The 'ExpressionSet' object for single-cell data.
cluster

The character string specifying the variable name for cell types. The default is "cluster".
sample

The character string specifying the variable name for subject/samples. The default is "sample".
cell_type_unique

A vector of cell types. It should match the order in list.marker.

Value

The population-level cell type proportions ('pop.ct.prop') and corresponding standard deviations ('pop.ct.sd').

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
seger<-readRDSFromWeb(paste0(urlremote,"segerstolpe.rds"))
cell_type_unique<-c("alpha","beta","delta","gamma")
ave_est<-pop.ct.prop.scRNA(scRNA=seger[["sc.eset.qc"]],
cell_type_unique=cell_type_unique)$pop.ct.prop
ave_est

A reference-free deconvolution estimate

Description

This function provides a reference-free deconvolution estimate, given a list of marker genes

Usage

Ref_free(bulk.data,list_marker,cell_type_unique,tol=0.001)

Arguments

bulk.data

The 'ExpressionSet' object for a target bulk data.
list_marker

A list of pre-specified marker genes corresponding to each cell type.
cell_type_unique

A list of cell types. It should match the order in 'list.marker'.
tol

A tolerance value for convergence. The default is 0.001.

Value

The estimated cell type proportions, named 'est'; and a goodness-of-fit value, named 'metrics'. The smaller the better.

Examples

##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
pseudo.seger<-readRDSFromWeb(paste0(urlremote,"pseudo.seger.rds"))
list_marker<-readRDSFromWeb(paste0(urlremote,"list_markerbaron20.rds"))
cell_type_unique<-c("alpha","beta","delta","gamma")
ref_free.output<-Ref_free(bulk.data=pseudo.seger,list_marker=list_marker,
cell_type_unique=cell_type_unique,tol=0.01) #make tol=0.001
reffree<-InteRD.predict.prop(InteRD.output=ref_free.output)