.. _Vignette_LabelTransferHeart_label:

Label transfer using customized heart cell reference
====================================================

In this vignette, we will explore how to use hECA build customized
reference dataset of the same organ and perform label transfer to
annotate other cells.

**Step 1: load packages**

.. code:: r

    library(Seurat)
    library(SingleR)
    library(scran)

| **Step 2: build reference dataset from hECA**
| In this experiment we will build reference using two study of heart
  cells. Following 5 steps:
| 2.1 - Open `hECA website <http://eca.xglab.tech/>`__, click “Cell
  Sorting” in the menu.
| 2.2 - “Add Filters” - “Organ” - type in “Heart”, click to select and
  include subtypes as default



| 2.3 - Click “Apply”. After seconds, click “Download Data” to download
  the keys of sorted cells, a csv file “keys.csv” will be downloaded.
| 

2.4 - Download cells following the tutorials of
`ECAUGT <http://159.226.47.242:60080/ecaugt/ECAUGT_CD19.html#get-gene-expression-data-and-metadata>`__
and save the results to csv file (in Python):

.. code:: python

   rows_to_get = pd.read_csv('keys.csv')
   rows_to_get = [[('cid',i)] for i in rows_to_get['cid']]
   result = ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get, cols_to_get=None,
                                          col_filter=gene_condition, do_transfer = True,
                                          thread_num = multiprocessing.cpu_count()-1)
   genes = result.columns[:43878]
   metaCols = result.columns[43878:43878+18]
   expr = result.loc[:,genes]
   meta = result.loc[:,metaCols]
   expr.to_csv("hECA_exprs.csv", index=True)
   meta.to_csv("hECA_metadata.csv", index=True)

2.5 - Load downloaded expression matrix and metadata as customized
reference dataset (continue in R).

.. code:: r

    expr <- read.csv("hECA_exprs.csv", header=T, row.names=1)
    meta <- read.csv("hECA_metadata.csv", header=T, row.names=1)

| **Step 3: load query data**
| Please replace the “query_data.csv” to the path of your query dataset.

.. code:: r

    query_path <- "query_data.csv"
    query_data <- read.csv(query_path, header=T, row.names=1)

| **Step 4: perform label transfer with SingleR**
| Now we will use SingleR to transfer labels from reference data to
  query data.

Step 4.1: Train SingleR model

.. code:: r

    # get labels
    ct.ref <- as.character(meta$cell_type)
    
    # train model
    trainedR <- trainSingleR(expr, ct.ref, de.method = "wilcox")
    
    ## (optional) save trained model
    # save(trainedR,file = "trainedModel.Rdata")

Step 4.2: Predict labels of query data

.. code:: r

    predict <- classifySingleR(query_data,trainedR)

Step 4.3: Check prediction results

.. code:: r

    # get true labels
    truth <- obj.query$cell_type
    
    # construct result dataframe
    df.result <- predict[,c("pruned.labels","labels")]
    df.result$truth <- truth
    df.result <- data.frame(df.result)
    ## (optional) save results
    # write.csv(df.result,"result1.csv")
    
    # draw confusion matrix and accuarcy scores
    caret::confusionMatrix(factor(df.result$pruned.labels,levels=unique(df.result$truth)),factor(df.result$truth,levels=unique(df.result$truth)))