cycle_npreg_outsample {peco} | R Documentation

## Predict test-sample ordering using training labels (no update)

### Description

Apply the estimates of cycle_npreg_insample to another gene expression dataset to infer an angle or cell cycle phase for each cell.

### Usage

    cycle_npreg_outsample(Y_test, sigma_est, funs_est,
      method.trend = c("trendfilter", "loess", "bspline"), normed = TRUE,
      polyorder = 2, method.grid = "uniform", ncores = 2, grids = 100,
      get_trend_estimates = FALSE)

### Arguments

`Y_test` | A SingleCellExperiment object.
`sigma_est` | Input from training data. A vector of gene-specific standard error of the cyclic trends.
`funs_est` | Input fron training data. A vector of cyclic functions estimating cyclic trends.
`method.trend` | Varous methods that can be applied to estimate cyclic trend of gene expression levels.
`normed` | Is the data already normalized? TRUE or FALSE.
`polyorder` | We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
`method.grid` | Method for defining bins along the circle.
`ncores` | We use doParallel package for parallel computing.
`grids` | number of bins to be selected along 0 to 2pi.
`get_trend_estimates` | To re-estimate the cylic trend based on the predicted cell cycle phase or not (T or F). Default FALSE. This step calls trendfilter and is computationally intensive.

### Value

A list with the following elements:

`Y` | The input gene expression marix.
`cell_times_est` | Inferred angles or cell cycle phases, NOT ordered.
`loglik_est` | Log-likelihood estimates for each gene.
`cell_times_reordered` | The inferred angles reordered (in ascending order).
`Y_reorded` | The input gene expression matrix reordered by cell_times_reordered.
`sigma_reordered` | Estimated standard error of the cyclic trend for each gene, reordered by cell_times_reordered.
`funs_reordered` | A list of functions for approximating the cyclic trends of gene express levels for each gene, reordered by cell_times_reordered.
`mu_reordered` | Estimated cyclic trend of gene expression values for each gene, reordered by cell_times_reordered.
`prob_per_cell_by_celltimes` | Probabilities of each cell belong to each bin.

### See Also

`cycle_npreg_insample` for obtaining parameteres for cyclic functions from training data, `cycle_npreg_loglik` for log-likehood at angles between 0 to 2pi, `initialize_grids` for selecting angles in `cycle_npreg_loglik`, `cycle_npreg_mstep` for estimating cyclic functions given inferred phases from `cycle_npreg_loglik`

Other peco classifier functions: `cycle_npreg_insample`, `cycle_npreg_loglik`, `cycle_npreg_mstep`, `initialize_grids`

### Examples

    # import data
    library(SingleCellExperiment)
    data(sce_top101genes)

    # select top 5 cyclic genes
    sce_top5 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                      decreasing=TRUE)[1:5],]

    # Select samples from NA18511 for our prediction example
    coldata <- colData(sce_top5)
    which_samples_train <- rownames(coldata)[coldata$chip_id != "NA18511"]
    which_samples_predict <- rownames(coldata)[coldata$chip_id == "NA18511"]

    # learning cyclic functions of the genes using our training data
    sce_top5 <- data_transform_quantile(sce_top5)
    expr_quant <- assay(sce_top5, "cpm_quantNormed")
    Y_train <- expr_quant[, colnames(expr_quant) %in% which_samples_train]
    theta_train <-
        coldata$theta_shifted[rownames(coldata) %in% which_samples_train]
    names(theta_train) <-
        rownames(coldata)[rownames(coldata) %in% which_samples_train]

    # obtain cyclic function estimates
    model_5genes_train <- cycle_npreg_insample(Y = Y_train,
                                               theta = theta_train,
                                               polyorder=2,
                                               ncores=2,
                                               method.trend="trendfilter")

    # predict cell cycle
    model_5genes_predict <- cycle_npreg_outsample(
      Y_test=sce_top5[,colnames(sce_top5) %in% which_samples_predict],
      sigma_est=model_5genes_train$sigma_est,
      funs_est=model_5genes_train$funs_est,
      method.trend="trendfilter",
      ncores=2,
      get_trend_estimates=FALSE)

    # estimate cyclic gene expression levels given cell cycle for each gene
    predict_cyclic <-
        fit_cyclical_many(Y=assay(model_5genes_predict$Y,"cpm_quantNormed"),
                          theta=colData(model_5genes_predict$Y)$cellcycle_peco)
    all.equal(names(predict_cyclic[[2]]), colnames(predict_cyclic[[1]]))

    par(mfrow=c(2,3), mar=c(4,4,3,1))
    for (g in seq_along(rownames(model_5genes_predict$Y))) {
      plot(assay(model_5genes_predict$Y,"cpm_quantNormed")[
          rownames(model_5genes_predict$Y)[g],],
           x=colData(model_5genes_predict$Y)$cellcycle_peco, axes=FALSE,
           xlab="FUCCI phase",
           ylab="Predicted phase")
      points(y=predict_cyclic$cellcycle_function[[
               rownames(model_5genes_predict$Y)[g]]](
        seq(0, 2*pi, length.out = 100)),
        x=seq(0, 2*pi, length.out = 100),
        pch=16, col="royalblue")
      axis(2); axis(1,at=c(0,pi/2, pi, 3*pi/2, 2*pi),
                    labels=c(0,expression(pi/2), expression(pi),
                             expression(3*pi/2), expression(2*pi)))
      abline(h=0, lty=1, col="black", lwd=.7)
      title(rownames(model_5genes_predict$Y_reordered)[g])
    }
    title("Predicting cell cycle phase for NA18511", outer=TRUE)

[Package peco version 0.99.16 Index]