Removes batch effects from expression datasets using sva::ComBat (for microarray/TPM data) or sva::ComBat_seq (for RNA-seq count data). Generates PCA plots to visualize data before and after correction.
Usage
remove_batcheffect(
eset1,
eset2,
eset3 = NULL,
id_type,
data_type = c("array", "count", "tpm"),
cols = "normal",
palette = "jama",
log2 = TRUE,
check_eset = TRUE,
adjust_eset = TRUE,
repel = FALSE,
path = NULL
)Arguments
- eset1
First expression set (matrix or data frame with genes as rows).
- eset2
Second expression set.
- eset3
Optional third expression set. Use `NULL` if not available.
- id_type
Type of gene ID in expression sets (e.g., `"ensembl"`, `"symbol"`). Required for count data normalization.
- data_type
Type of data: `"array"`, `"count"`, or `"tpm"`. Default is `"array"`.
- cols
Color scale for PCA plot. Default is `"normal"`.
- palette
Color palette for PCA plot. Default is `"jama"`.
- log2
Whether to perform log2 transformation. Default is `TRUE`. Ignored for count data.
- check_eset
Whether to check expression sets for errors. Default is `TRUE`.
- adjust_eset
Whether to adjust expression sets by removing problematic features. Default is `TRUE`.
- repel
Whether to add repelling labels to PCA plot. Default is `FALSE`.
- path
Directory where results should be saved. Default is `NULL` (display only).
References
Zhang Y, et al. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics and Bioinformatics. 2020;2(3):lqaa078. doi:10.1093/nargab/lqaa078
Leek JT, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-883.
Examples
eset_stad <- load_data("eset_stad")
eset_blca <- load_data("eset_blca")
eset_corrected <- remove_batcheffect(
eset_stad[1:1000, 1:5], eset_blca[1:1000, 1:5],
id_type = "ensembl",
data_type = "count"
)
#> ℹ The two expression matrices share 995 features
#> ℹ Processing method: sva::ComBat_seq
#> Found 2 batches
#> Using null model in ComBat-seq.
#> Adjusting for 0 covariate(s) or covariate level(s)
#> Estimating dispersions
#> Fitting the GLM model
#> Shrinkage off - using GLM estimates for parameters
#> Adjusting the data
#> ℹ Using local annotation (anno_grch38) for TPM conversion
#> ℹ No duplicate gene symbols found.
#> ✔ Applied log2 transformation
#> ℹ Count data processed with ComBat_seq
#> ✔ Applied log2 transformation
#>
#> eset1 eset2
#> 5 5
#> 55
#> [1] ">>== colors for group: "
#> >>== #374E55FF>>== #DF8F44FF
#> ✔ Applied log2 transformation
#>
#> eset1 eset2
#> 5 5
#> 55
#> [1] ">>== colors for group: "
#> >>== #374E55FF>>== #DF8F44FF
#> ℹ Log2 transformation not necessary (data appears to already be log-scaled)
#>
#> eset1 eset2
#> 5 5
#> 55
#> [1] ">>== colors for group: "
#> >>== #374E55FF>>== #DF8F44FF
#> Ignoring unknown labels:
#> • linetype : "batch"
#> Ignoring unknown labels:
#> • linetype : "batch"
#> Ignoring unknown labels:
#> • linetype : "batch"
