metamorphr (version 0.3.0)

normalize_quantile_smooth: Normalize intensities across samples using smooth Quantile Normalization (qsmooth)

Description

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
)

Value

A tibble with intensities normalized across samples.

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.

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)