# Example for Classification Data – Breast Invasive Carcinoma

Marta Lopes and André Veríssimo

#### 2020-04-28

# Contents

## 0.1 Instalation

    if (!require("BiocManager"))
      install.packages("BiocManager")
    BiocManager::install("glmSparseNet")

# 1 Required Packages

    library(dplyr)
    library(ggplot2)
    library(survival)
    library(loose.rock)
    library(futile.logger)
    library(curatedTCGAData)
    library(TCGAutils)
    #
    library(glmSparseNet)
    #
    # Some general options for futile.logger the debugging package
    .Last.value <- flog.layout(layout.format('[~l] ~m'))
    .Last.value <- loose.rock::show.message(FALSE)
    # Setting ggplot2 default theme as minimal
    theme_set(ggplot2::theme_minimal())

# 2 Load data

The data is loaded from an online curated dataset downloaded from TCGA using `curatedTCGAData` bioconductor package and processed.

To accelerate the process we use a very reduced dataset down to 107 variables only (genes), which is stored as a data object in this package. However, the procedure to obtain the data manually is described in the following chunk.

    brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm", FALSE)

    brca <- TCGAutils::splitAssays(brca, c('01','11'))
    xdata.raw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))

    # Get matches between survival and assay data
    class.v        <- TCGAbiospec(rownames(xdata.raw))$sample_definition %>% factor
    names(class.v) <- rownames(xdata.raw)

    # keep features with standard deviation > 0
    xdata.raw <- xdata.raw %>%
      { (apply(., 2, sd) != 0) } %>%
      { xdata.raw[, .] } %>%
      scale

    set.seed(params$seed)
    small.subset <- c('CD5', 'CSF2RB', 'HSF1', 'IRGC', 'LRRC37A6P', 'NEUROG2',
                      'NLRC4', 'PDE11A', 'PIK3CB', 'QARS', 'RPGRIP1L', 'SDC1',
                      'TMEM31', 'YME1L1', 'ZBTB11',
                      sample(colnames(xdata.raw), 100))

    xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
    ydata <- class.v

# 3 Fit models

Fit model model penalizing by the hubs using the cross-validation function by `cv.glmHub`.

    fitted <- cv.glmHub(xdata, ydata,
                        family  = 'binomial',
                        network = 'correlation',
                        nlambda = 1000,
                        network.options = networkOptions(cutoff = .6,
                                                         min.degree = .2))

# 4 Results of Cross Validation

Shows the results of `1000` different parameters used to find the optimal value in 10-fold cross-validation. The two vertical dotted lines represent the best model and a model with less variables selected (genes), but within a standard error distance from the best.

    plot(fitted)

## 4.1 Coefficients of selected model from Cross-Validation

Taking the best model described by `lambda.min`

    coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]}
    coefs.v %>% {
      data.frame(ensembl.id  = names(.),
                 gene.name   = geneNames(names(.))$external_gene_name,
                 coefficient = .,
                 stringsAsFactors = FALSE)
      } %>%
      arrange(gene.name) %>%
      knitr::kable()

ensembl.id | gene.name | coefficient
--- | --- | ---
(Intercept) | (Intercept) | -6.8189811
CD5 | AMOTL1 | -1.1200445
NLRC4 | ATR | -1.4434577
PIK3CB | B3GALT2 | -0.3880002
ZBTB11 | BAG2 | -0.3325728
ATR | C16orf82 | 1.2498303
IL2 | CD5 | 0.6327083
GDF11 | CIITA | -0.2676642
DCP1A | DCP1A | 0.2994599
AMOTL1 | FAM86B1 | 0.4430643
BAG2 | FNIP2 | -0.1841676
C16orf82 | GDF11 | 0.0396368
FAM86B1 | GNG11 | 0.2025462
FNIP2 | GREM2 | 0.6101758
MS4A4A | GZMB | 1.1614778
B3GALT2 | HAX1 | -0.0867011
GNG11 | IL2 | 3.0659065
NDRG2 | MMP28 | 1.1142519
HAX1 | MS4A4A | -0.1516836
GREM2 | NDRG2 | -0.2014884
CIITA | NLRC4 | 0.4256103
GZMB | PIK3CB | -2.7663573
MMP28 | ZBTB11 | -0.8438023

## 4.2 Hallmarks of Cancer

    geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }

## 4.3 Accuracy

    ## [INFO] Misclassified (11)

    ## [INFO]   * False primary solid tumour: 7

    ## [INFO]   * False normal              : 4

Histogram of predicted response

ROC curve

    ## Setting levels: control = Primary Solid Tumor, case = Solid Tissue Normal

    ## Setting direction: controls < cases