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Normalize intensities across samples using smooth Quantile Normalization (qsmooth)

Source: R/normalize.R
normalize_quantile_smooth.Rd

This function performs a smooth Quantile Normalization on each sub-group in the data set (qsmooth). It therefore requires grouping information. See Examples for more information. This approach might perform better than the standard approach, normalize_quantile_all, if sub-groups are very different (e.g., when comparing cancer vs. normal tissue). The result lies somewhere between normalize_quantile_group and normalize_quantile_all. Basically a re-implementation of Hicks et al. (2018).

Usage

normalize_quantile_smooth(
  data,
  group_column = .data$Group,
  rolling_window = 0.05
)

Arguments

data

A tidy tibble created by read_featuretable.

group_column

Which column should be used for grouping? Usually grouping_column = Group. Uses args_data_masking.

rolling_window

normalize_quantile_smooth uses a rolling window median to eliminate isolated outliers. This argument specifies the size of the rolling window as a fraction of the number of unique features in data. For example, if there are 100 features in data and rolling_window = 0.05, the rolling median will be calculated from 5 features. Set rolling_window = 0 to disable.

Value

A tibble with intensities normalized across samples.

References

  • S. C. Hicks, K. Okrah, J. N. Paulson, J. Quackenbush, R. A. Irizarry, H. C. Bravo, Biostatistics 2018, 19, 185–198, DOI 10.1093/biostatistics/kxx028.

  • Y. Zhao, L. Wong, W. W. B. Goh, Sci Rep 2020, 10, 15534, DOI 10.1038/s41598-020-72664-6.

Examples

toy_metaboscape %>%
  # Metadata, including grouping information, must be added before using normalize_quantile_group()
  join_metadata(toy_metaboscape_metadata) %>%
  normalize_quantile_smooth(group_column = Group)
#> # A tibble: 110 × 12
#>      UID Feature      Sample Intensity    RT `m/z` Name  Formula Group Replicate
#>    <int> <chr>        <chr>      <dbl> <dbl> <dbl> <chr> <chr>   <chr>     <int>
#>  1     1 161.10519 D… Sampl…    NaN     0.45  162. NA    C7H15N… cont…         1
#>  2     2 276.13647 D… Sampl…      2.56  0.45  277. Octy… C16H22… cont…         1
#>  3     3 304.24023 D… Sampl…    NaN     0.55  305. Arac… C20H32… cont…         1
#>  4     4 417.23236 D… Sampl…    NaN     1     418. NA    NA      cont…         1
#>  5     5 104.10753 D… Sampl…    NaN     2.84  105. NA    C5H14NO cont…         1
#>  6     6 105.04259 D… Sampl…    NaN     3.33  106. NA    C3H8NO3 cont…         1
#>  7     7 237.09204 D… Sampl…    NaN     5.22  238. Keta… C13H16… cont…         1
#>  8     8 745.09111 D… Sampl…      2.56  6.37  746. NADPH C21H30… cont…         1
#>  9     9 427.02942 D… Sampl…    NaN     7.08  428. ADP   C10H15… cont…         1
#> 10    10 1284.34904 … Sampl…    NaN     8.32 1285. NA    NA      cont…         1
#> # ℹ 100 more rows
#> # ℹ 2 more variables: Batch <int>, Factor <dbl>