Sample

Constructor

class itpseq.Sample(replicates=None, *, labels=None, reference=None, dataset=None, keys=('sample',), name=None, **kwargs)

Represents a sample in a dataset, its replicates, reference, and associated metadata.

The Sample class is used to encapsulate information and behavior related to samples in a dataset. It manages details like labels, references, replicates, and metadata, and provides methods for analyzing replicates, performing differential enrichment analysis, and creating visualizations.

Examples

Get a Sample from a DataSet

>>> sample = dataset['sample_name']

Compute the differential expression for positions E-P-A.

>>> sample.DE('E:A')

Attributes:

name_ref

name_vs_ref

Methods

DE([pos, join, quiet, filter_size, multi, ...])	Computes the differential expression between the sample and its reference.
all_logos([logo_kwargs])	Creates a logo for all positions for each replicate in the sample.
copy([name, reference])	Creates a copy of the sample.
get_counts([pos])	Counts the number of reads for each motif or combination of amino-acid/position for each replicate in the sample.
get_counts_ratio([pos, factor, exclude_empty])	Outputs the result of get_counts for the sample and its reference and add extra columns: the normalized averages and the sample/reference ratio.
get_counts_ratio_pos([pos])	Computes a DataFrame with the enrichment ratios for each ribosome position.
hmap([r, c, pos, col, transform, cmap, ...])	Generates a heatmap of enrichment for combinations of 2 positions.
hmap_grid([pos, col, transform, cmap, vmax, ...])	Creates a grid of heatmaps for all combinations of ribosome positions passed in pos.
hmap_pos([pos, cmap, vmax, center, ax])	Generates a heatmap of enrichment ratios for amino acid positions across ribosome sites.
infos([html])	Returns a table with information on the NGS reads per replicate.
itoeprint([plot, norm, norm_range, ...])	Plots a virtual inverse-toeprint gel.
itp_len_plot([ax, min_codon, max_codon, ...])	Generates a line plot of inverse-toeprint (ITP) counts per length.
logo([pos, logo_kwargs, ax])	Creates a logo for the selected positions.
rename(name[, rename_replicates])	Changes the name of the sample.
volcano([pos, query, motif, ax, x, y, ...])	Draws a volcano plot from the Differential Expression data.

load_replicates

Methods

Sample.rename(name[, rename_replicates])	Changes the name of the sample.
Sample.copy([name, reference])	Creates a copy of the sample.
Sample.infos([html])	Returns a table with information on the NGS reads per replicate.
Sample.get_counts([pos])	Counts the number of reads for each motif or combination of amino-acid/position for each replicate in the sample.
Sample.get_counts_ratio([pos, factor, ...])	Outputs the result of get_counts for the sample and its reference and add extra columns: the normalized averages and the sample/reference ratio.
Sample.get_counts_ratio_pos([pos])	Computes a DataFrame with the enrichment ratios for each ribosome position.
Sample.DE([pos, join, quiet, filter_size, ...])	Computes the differential expression between the sample and its reference.
Sample.hmap([r, c, pos, col, transform, ...])	Generates a heatmap of enrichment for combinations of 2 positions.
Sample.hmap_grid([pos, col, transform, ...])	Creates a grid of heatmaps for all combinations of ribosome positions passed in pos.
Sample.volcano([pos, query, motif, ax, x, ...])	Draws a volcano plot from the Differential Expression data.
Sample.logo([pos, logo_kwargs, ax])	Creates a logo for the selected positions.
Sample.all_logos([logo_kwargs])	Creates a logo for all positions for each replicate in the sample.
Sample.itp_len_plot([ax, min_codon, ...])	Generates a line plot of inverse-toeprint (ITP) counts per length.
Sample.itoeprint([plot, norm, norm_range, ...])	Plots a virtual inverse-toeprint gel.