PAC_pca | R Documentation |
PAC_pca
PAC principle component analysis.
PAC_pca(
PAC,
norm = "counts",
type = "pheno",
graphs = TRUE,
pheno_target = NULL,
anno_target = NULL,
labels = NULL,
...
)
PAC |
PAC-list object. |
norm |
Character indicating what type of data to be used. If
type="counts" the PCA will be conducted on the raw Counts. If type="cpm" the
analysis will be done on cpm values returned from |
type |
Character indicating what type of pca and plot to be drawn. When type="pheno" then results will be drawned from a sample perspective, while if type="anno" then results will be presented from a sequence perspective. If type="both" then a biplot will be drawn, representing both pheno and anno features. Note, the 1st object (target column name) in the pheno_target or anno_taget can be used to specifically highlight sample and sequence groups (see below). |
graphs |
Logical whether or not scatter plots should be plotted. (default=TRUE) |
pheno_target |
List with: 1st object being a character vector of target column(s) in Pheno, 2nd object being a character vector of the target group(s) in the target column (1st object). The 1st object also control group colors when type="pheno" or "both". (default=NULL) |
anno_target |
List with: 1st object being a character vector of target column(s) in Anno, 2nd object being a character vector of the target features(s) in the target column (1st object). The 1st object also control sequence feature colors when type="anno". (default=NULL) |
labels |
If labels="sample", then points will be labeled with the names rownames in pheno(PAC) or anno(PAC) depending on type="pheno" or type="anno", respectively. Point labels can also be manually provided as a character vector in the same length as the intended target. As default, labels=NULL where only point are plotted. |
... |
parsing to the fviz_pca functions of the factoextra package. |
Given a PAC object the function will perform a principle component analysis by calling the PCA function in the FactoMineR package, and then plot scatter plots with the fviz_pca functions of the factoextra package.
A PCA list object generated by the PCA function in the FactoMineR package
https://github.com/Danis102 for updates on the current package.
Other PAC analysis:
PAC_covplot()
,
PAC_deseq()
,
PAC_filter()
,
PAC_filtsep()
,
PAC_gtf()
,
PAC_jitter()
,
PAC_mapper()
,
PAC_nbias()
,
PAC_norm()
,
PAC_pie()
,
PAC_saturation()
,
PAC_sizedist()
,
PAC_stackbar()
,
PAC_summary()
,
PAC_trna()
,
as.PAC()
,
filtsep_bin()
,
map_rangetype()
,
tRNA_class()
# Load a PAC-object
load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata",
package = "seqpac", mustWork = TRUE))
# Simple sample counts pca and scatterplots with no groupings:
pca_cnt <- PAC_pca(pac, norm="counts")
# Sample cpm pca and scatterplots with color groupings from
# pheno(PAC)$type column:
pca_cpm <- PAC_pca(pac, norm="cpm", type="pheno",
pheno_target=list("stage"))
# Same but with or without text labels:
pca_cpm_lab <- PAC_pca(pac, norm="cpm", type="pheno",
pheno_target=list("stage"), labels="samples")
pca_cpm_lab2 <- PAC_pca(pac, norm="cpm", type="pheno",
pheno_target=list("stage"),
labels=pheno(pac)$batch)
# Cpm pca with anno(PAC) sequence features instead of Pheno samples and
# restricted to read size 20-22:
pca_cpm_anno <- PAC_pca(pac, norm="cpm", type="anno",
anno_target=list("Size", 20:22))
# Cpm pca as biplot:
pca_cpm_bi <- PAC_pca(pac, norm="cpm", type="both",
pheno_target=list("stage"))
# Plot individual graphs
pca_cpm_bi$graphs[[1]]
pca_cpm_lab$graphs[[1]]
pca_cpm_anno$graphs[[3]]
# Extract pca output
pca_cpm_anno$pca
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