bc_summary_barcode {CellBarcode} | R Documentation |
bc_summary_barcode
evaluates sequence diversity metrics using the
barcodes data in the cleanBc
slot of BarcodeObj
object. It
also generates Lorenz curve and barcode frequency distribution graphs.
bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE) ## S4 method for signature 'BarcodeObj' bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE)
barcodeObj |
A BarcodeObj object. |
plot |
A logical value, if TRUE, draw the Lorenz curve and barcode distribution graphs. |
log_x |
A logical value, if TRUE, the |
Followings are the metrics used for evaluating the barcode diversity:
Richness: The unique barcodes number R, it evaluates the richness of the barcodes.
Shannon index: Shannon diversity index is weighted geometric average of the proportion p of barcodes.
H' = - ∑_{i=1}^{R}p_ilnp_i
Equitability index: Shannon equitability E_H characterize the evenness of the barcodes, it is a value between 0 and 1, with 1 being complete evenness.
E_H = H' / H'_{max} = H / ln(R)
Bit: Shannon entropy H, with a units of bit,
H = - ∑_{i=1}^{R}p_ilog_2p_i
A data.frame with following columns:
total_reads
: total read number.
uniq_barcode
: how many barcodes in the dataset.
shannon_index
: Shannon's diversity index or Shannon–Wiener
index.
equitability_index
: Shannon's equitability.
bit_index
: Shannon bit information.
data(bc_obj) # filter barcode by depth bc_obj <- bc_cure_depth(bc_obj) # Output the summary of the barcodes bc_summary_barcode(bc_obj)