View source: R/normalize_shared.R
normalize_se | R Documentation |
Normalize a SummarizedExperiment and think about how I want to reimplement some of this.
normalize_se(
se,
transform = "raw",
norm = "raw",
convert = "raw",
batch = "raw",
filter = FALSE,
annotations = NULL,
fasta = NULL,
entry_type = "gene",
use_original = FALSE,
batch1 = "batch",
batch2 = NULL,
batch_step = 4,
low_to_zero = TRUE,
thresh = 2,
min_samples = 2,
p = 0.01,
A = 1,
k = 1,
cv_min = 0.01,
cv_max = 1000,
na_to_zero = FALSE,
adjust_method = "ruv",
verbose = TRUE,
...
)
se |
Summarized Experiment as input. |
transform |
Transformation desired, usually log2. |
norm |
How to normalize the data? (raw, quant, sf, upperquartile, tmm, rle) |
convert |
Conversion to perform? (raw, cpm, rpkm, cp_seq_m) |
batch |
Batch effect removal tool to use? (limma sva fsva ruv etc) |
filter |
Filter out low/undesired features? (cbcb, pofa, kofa, others?) |
annotations |
Used for rpkm – probably not needed as this is in fData now. |
fasta |
Fasta file for cp_seq_m counting of oligos. |
entry_type |
For getting genelengths by feature type (rpkm or cp_seq_m). |
use_original |
Use the backup data in the expt class? |
batch1 |
Experimental factor to extract first. |
batch2 |
Second factor to remove (only with limma's removebatcheffect()). |
batch_step |
From step 1-5, when should batch correction be applied? |
low_to_zero |
When log transforming, change low numbers (< 0) to 0 to avoid NaN? |
thresh |
Used by cbcb_lowfilter(). |
min_samples |
Also used by cbcb_lowfilter(). |
p |
Used by genefilter's pofa(). |
A |
Also used by genefilter's pofa(). |
k |
Used by genefilter's kofa(). |
cv_min |
Used by genefilter's cv(). |
cv_max |
Also used by genefilter's cv(). |
na_to_zero |
Sometimes rpkm gives some NA values for very low numbers. |
adjust_method |
Given a set of sv estimates, change the counts with this method. |
verbose |
Print what is happening while the normalization is performed? I am not sure why, but I think they should be 0. |
... |
more options |
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