could not find function, 'norm.to.one'

Hi! I just installed TED, but I couldn't find function, norm.to.one.

> install_github("Danko-Lab/TED/TED")
Downloading GitHub repo Danko-Lab/TED@HEAD
Error in utils::download.file(url, path, method = method, quiet = quiet,  :
  download from 'https://api.github.com/repos/Danko-Lab/TED/tarball/HEAD' failed
## I download the package from GitHub, and install it locally.
> TED::
TED::learn.embedding.withPhiTum  TED::learn.embedding.Kcls        TED::run.Ted                     TED::cleanup.genes               TED::estimate_sf                 TED::get.signature.genes         TED::convert.cell.fraction
>
> TED::norm.to.one
Error: 'norm.to.one' is not an exported object from 'namespace:TED'

> help(package="TED")

 Information on package ‘TED’

Description:

Package:       TED
Version:       1.1
Date:          2020-01-15
Title:         BayesPrism: A Fully Bayesian Inference of Tumor
               Microenvironment composition and gene expression.
               Formerly called TED (Tumor microEnvironment
               Deconvolution).
Author:        Tinyi Chu<tc532@cornell.edu>, Charles G. Danko
               <dankoc@gmail.com>
Maintainer:    Tinyi Chu<tc532@cornell.edu>
Depends:       R (>= 2.6)
Imports:       DESeq2, parallel, MCMCpack, gplots, scran, BiocParallel
Description:   TED is comprised of the deconvolution modules and the
               embedding learning module. The deconvolution module
               leverages cell type-specific expression profiles from
               scRNA-seq and implements a fully Bayesian inference to
               jointly estimate the posterior distribution of cell type
               composition and cell type-specific gene expression from
               bulk RNA-seq expression of tumor samples. The embedding
               learning module uses Expectation-maximization (EM) to
               approximate the tumor expression using a linear
               combination of tumor pathways while conditional on the
               inferred expression and fraction of non-tumor cells
               estimated by the deconvolution module.
License:       GPL-2 | GPL-3
biocViews:     Sequencing, Analysis
LazyLoad:      yes
RoxygenNote:   6.0.1
RemoteType:    local
RemoteUrl:     /home/user_li/linqin_tmp/SourceCode/ENIGMA/TED.zip
Built:         R 4.0.3; ; 2021-12-26 11:38:32 UTC; unix

Index:

cleanup.genes           Utility function to remove highly expressed
                        outlier genes that are sensitive to batch
                        effects from ref.dat
learn.embedding.Kcls    TED Embedding learning module initialized by
                        hirarchial clustering on tumor expression
                        profiles.
learn.embedding.withPhiTum
                        TED Embedding learning module with provided
                        tumor basis
norm.to.one             Utility function to prepare the input.phi
run.Ted                 Bayesian deconvolution module

Hi Qin,

Thank you for your question. norm.to.one is no longer needed, as it has been internalized in the run.Ted function. Users can directly specify the scRNA-seq input using the ref.dat argument of the run.Ted function without the need to normalize it manually.

Please let me know if there are any questions.

Best,

Tinyi

On Thu, Dec 30, 2021 at 2:25 AM Qin Lin @.***> wrote:

Hi! I just install TED, but I couldn't find function, norm.to.one.

TED::

TED::learn.embedding.withPhiTum TED::learn.embedding.Kcls TED::run.Ted TED::cleanup.genes TED::estimate_sf TED::get.signature.genes TED::convert.cell.fraction

help(package="TED")

Information on package ‘TED’

Description:

Package: TED

Version: 1.1

Date: 2020-01-15

Title: BayesPrism: A Fully Bayesian Inference of Tumor
           Microenvironment composition and gene expression.

           Formerly called TED (Tumor microEnvironment

           Deconvolution).
Author: Tinyi @.***>, Charles G. Danko
           ***@***.***>
Maintainer: Tinyi @.***>

Depends: R (>= 2.6)

Imports: DESeq2, parallel, MCMCpack, gplots, scran, BiocParallel

Description: TED is comprised of the deconvolution modules and the
           embedding learning module. The deconvolution module

           leverages cell type-specific expression profiles from

           scRNA-seq and implements a fully Bayesian inference to

           jointly estimate the posterior distribution of cell type

           composition and cell type-specific gene expression from

           bulk RNA-seq expression of tumor samples. The embedding

           learning module uses Expectation-maximization (EM) to

           approximate the tumor expression using a linear

           combination of tumor pathways while conditional on the

           inferred expression and fraction of non-tumor cells

           estimated by the deconvolution module.
License: GPL-2 | GPL-3

biocViews: Sequencing, Analysis

LazyLoad: yes

RoxygenNote: 6.0.1

RemoteType: local

RemoteUrl: /home/user_li/linqin_tmp/SourceCode/ENIGMA/TED.zip

Built: R 4.0.3; ; 2021-12-26 11:38:32 UTC; unix

Index:

cleanup.genes Utility function to remove highly expressed
                    outlier genes that are sensitive to batch

                    effects from ref.dat
learn.embedding.Kcls TED Embedding learning module initialized by
                    hirarchial clustering on tumor expression

                    profiles.
learn.embedding.withPhiTum
                    TED Embedding learning module with provided

                    tumor basis
norm.to.one Utility function to prepare the input.phi

run.Ted Bayesian deconvolution module

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Danko-Lab / TED

could not find function, 'norm.to.one' #14