# 通过conda安装纯净环境的imputation

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02132-x

## SAVER-X

https://github.com/jingshuw/ SAVERX

- conda create -n imputation -c conda-forge r-base=4.1.3 -y

- conda activate imputation

- conda install -c conda-forge r-seurat=4.1.0 -y

- conda install -c conda-forge r-tidyverse=1.3.1 -y

- conda install -c conda-forge r-biocmanager=1.30.16 -y

- conda install -c conda-forge r-devtools=2.4.3 -y

- conda install -c conda-forge r-reticulate=1.24 -y

- conda install -c conda-forge r-irkernel=1.3 -y

- wget https://github.com/jingshuw/SAVERX/archive/refs/heads/master.zip -O SAVERX-master.zip

- devtools::install_local(“SAVERX-master.zip”)

- IRkernel::installspec(name=’imputation’, displayname=’r-imputation’)

- # IO错误 请修改xui2.json内的DNS服务器ip

- ~/dev/xray/xray -c ~/etc/xui2.json &

- wget -e “https_proxy=http://127.0.0.1:20809“ https://www.dropbox.com/sh/4u22cfuswcfcwvu/AAC4hl-f7P5lD8EZU1CFVt6-a/human\_Immune.hdf5?dl=0 -O human_Immune.hdf5

- wget -e “https_proxy=http://127.0.0.1:20809“ https://www.dropbox.com/sh/4u22cfuswcfcwvu/AABv0YxcNAChwEPS-D9hnDM6a/human\_Tcells.hdf5?dl=0 -O human_Tcells.hdf5

- fg

- 垃圾程序让人高血压！！！

- conda deactivate

- conda create -n imputation_saver_x -c conda-forge python=3.7 -y

- conda activate imputation_saver_x

- pip install scanpy==1.4.4

- pip install tensorflow==2.1.0

- pip install Keras==2.3.1

- https://github.com/tensorflow/tensorflow/issues/38589

- nano -K /opt/conda/envs/imputation_saver_x/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py

- 按说明重定义 is_tensor 函数

- pip install sctransfer

- 修改 /opt/conda/envs/imputation_saver_x/lib/python3.7/site-packages/keras/engine/saving.py

- 删除全部的 .decode(‘utf8’)和.encode(‘utf8’)

- conda deactivate

- conda create -n imputation_saver_x -c conda-forge python=3.7 -y

- conda activate imputation_saver_x

- pip install scanpy==1.4.4

- pip install tensorflow==1.15.0

- pip install Keras==2.3.1

- pip install sctransfer

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    Sys.setenv(RETICULATE_PYTHON = "/opt/conda/envs/imputation_saver_x/bin/python3.7")
    library('reticulate')
    use_condaenv("imputation_saver_x")
    py_config()
    library(SAVERX)

## MAGIC

http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/MAGIC/blob/master/Rmagic/inst/examples/bonemarrow_tutorial.html

- conda deactivate
- conda activate imputation
- conda install -c conda-forge r-viridis=0.6.2 -y
- conda install -c conda-forge r-ggplot2=3.3.5 -y
- conda install -c conda-forge r-readr=2.1.2 -y
- conda install -c conda-forge r-phater=1.0.7 -y
- conda deactivate
- conda create -n imputation_magic -c conda-forge numpy=1.22.3 -y
- conda activate imputation_magic
- pip install phate
- pip install magic-impute

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    Sys.setenv(RETICULATE_PYTHON = "/opt/conda/envs/imputation_magic/bin/python3.8")
    library('reticulate')
    use_condaenv("imputation_magic")
    py_config()
    library(Rmagic)

## harmony

- conda install -c bioconda r-harmony -y

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    require(tidyverse)
    f_ScaleData_RunPCA <- function(scRNA){
        scRNA <- FindVariableFeatures(scRNA, selection.method = "vst", nfeatures = 2000)
        lc_all.genes <- rownames(scRNA)
        scRNA <- ScaleData(scRNA, features = lc_all.genes)

        scRNA <- RunPCA(scRNA, features = VariableFeatures(object = scRNA))
        print(ElbowPlot(scRNA, ndims = 40))
        scRNA
    }

    require(harmony)
    f_RunHarmony <-  function(scRNA, dims=1:30, batchN="batch"){
        scRNA = scRNA %>% RunHarmony(batchN, plot_convergence = TRUE, max.iter.harmony = 30)
        scRNA <- scRNA %>% RunUMAP(reduction = "harmony", dims = dims)
        scRNA
    }

    f_FindNeighbors <- function(scRNA, resolution = 0.5){
        scRNA <- scRNA %>% FindNeighbors(reduction = "harmony") %>%  FindClusters(resolution = resolution)
        scRNA[[paste0('h_resolution_', resolution)]] <- Idents(scRNA)
        scRNA
    }

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通过conda安装纯净环境的imputation
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April 18, 2022
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