Sample#

Constructor#

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

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:
itp_len

Combines the counts of inverse-toeprints (ITPs) for each length across all replicates.

This method extracts the counts of inverse-toeprints for each length from the metadata of each replicate and combines them into a single DataFrame, keeping the data for each replicate independent.

pandas.DataFrame

A DataFrame with the following columns:

  • lengthint

    The length of the inverse-toeprints.

  • replicatestr

    The replicate identifier.

  • countint

    The count of inverse-toeprints of the given length for the replicate.

  • samplestr

    The name of the sample this data belongs to.

>>> sample.itp_len
     length replicate     count sample
0        51         1  115732.0    spl
1        20         1  444506.0    spl
2        41         1  130495.0    spl
3        23         1  198257.0    spl
4        17         1   55786.0    spl
..      ...       ...       ...    ...
328     106         3       NaN    spl
329     143         3       NaN    spl
330     102         3       NaN    spl
331     104         3       NaN    spl
332     221         3       NaN    spl
[333 rows x 4 columns]
name_ref

Name of the Sample combined with its reference

name_vs_ref

Name of the Sample combined with its reference

toeprint_df

DataFrame of the counts of each inverse toeprint length per Replicate

Methods

DE([pos, how, join, quiet, filter_size, ...])

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.

codon_violin(pos, *[, motif, query, col, ...])

Plots violin plots for each amino acid motif, with one line per combination of codons.

copy([name, reference])

Creates a copy of the sample.

format_sequences(**kwargs)

Display formatted inverse-toeprints for all replicates

get_counts([pos, how])

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, ...])

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, how])

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, how, transform, cmap, vmax, ...])

Generates a heatmap of enrichment ratios per 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 plot of inverse-toeprint (ITP) counts per length.

load_replicates([how])

Loads all the Replicates in the Sample with the defined method (how)

logo([pos, logo_kwargs, ax, vs_ref, how])

Creates a logo for the selected positions.

rename(name[, rename_replicates])

Changes the name of the sample.

subset_logo(pos, *[, how, query, motif, ...])

Creates a logo from a subset of the Differential Expression data.

volcano([pos, query, motif, ax, x, y, ...])

Draws a volcano plot from the Differential Expression data.

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, how])

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, how])

Computes a DataFrame with the enrichment ratios for each ribosome position.

Sample.DE([pos, how, join, quiet, ...])

Computes the differential expression between the sample and its reference.

Sample.codon_violin(pos, *[, motif, query, ...])

Plots violin plots for each amino acid motif, with one line per combination of codons.

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.hmap_pos([pos, how, transform, cmap, ...])

Generates a heatmap of enrichment ratios per positions across ribosome sites.

Sample.volcano([pos, query, motif, ax, x, ...])

Draws a volcano plot from the Differential Expression data.

Sample.subset_logo(pos, *[, how, query, ...])

Creates a logo from a subset of the Differential Expression data.

Sample.logo([pos, logo_kwargs, ax, vs_ref, how])

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 plot of inverse-toeprint (ITP) counts per length.

Sample.itoeprint([plot, norm, norm_range, ...])

Plots a virtual inverse-toeprint gel.
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