greatpy.pl.dotplot_multi_sample

greatpy.pl.dotplot_multi_sample(test_data, n_row=5, list_id=[], fig=None, show_term_name=False, term_name_nchars=30, dot_size_amplifier=7, palette_id='Reds', ylab='GO', xlab='', label_colorbar='-log(p_hypergeometric)', marker='o', plot_title='Dotplot of enrichment GO terms', line_width=0.1, circle_legend='log2(odd ratio)', save=False, **kwargs)

Dotplot of enrichment GO terms for a given list of example genomic regions.

Parameters:

test_data : dict

dict of multiple tests output

n_row : int

Number of rows pick in each dataframe.

Default is 5

list_id : list

List of IDs to be plotted. If list_id == [], any filter will be applied.

Default is []

fig : matplotlib.figure.Figure or None

Figure to plot the dotplot.

Default is None

show_term_name : bool

Whether to show the GO term name.

Default is False

term_name_nchars : int

Number of characters to show for the GO term name.

Default is 30

dot_size_amplifier : int or float

Amplifier for the dot size.

Default is 7

palette_id : str or matplotlib.cm

Color palette for the dotplot.

Default is Reds

ylab : str

Y-axis label.

Default is "GO"

xlab : str

X-axis label.

Default is ""

label_colorbar : str

Label for the colorbar.

Default is "-log(p_hypergeometric)"

marker : str

Marker for the dotplot.

Default is "o"

plot_title : str

Plot title.

Default is "Dotplot of enrichment GO terms"

line_width : int

Dot line width.

Default is 0.1

circle_legend : str

Legend for the circle.

Default is "log2(odd ratio)"

kwargs

Other parameters to be passed to the make make_bubble_heatmap function.

Returns:

• None – Dotplot of enrichment GO terms for the given df

• p_val (pandas.DataFrame) – Dataframe of plotted p-values

• odds_ratio (pandas.DataFrame) – Dataframe of plotted odds ratios

• df (pandas.DataFrame) – Dataframe of all results concatenated

Examples

>>> test = ["SRF:Ishikawa,A-673-clone-Asp114,K-562,MCF-7,Hep-G2","MAX:K-562,WA01,HeLa-S3", "BACH1:A-549,GM12878"]
>>> tmp_df = great.tl.enrichment_multiple(
    tests = test, regdom_file="../data/human/hg38/regulatory_domain.bed",
    chr_size_file="../data/human/hg38/chr_size.bed",
    annotation_file="../data/human/ontologies.csv", binom=True, hypergeom=True,)
>>> p_val,odd_ratio = great.pl.dotplot_multi_sample(tmp_df)

>>> p_val
...    | id         |       0 |       1 |        2 |
...    |:-----------|--------:|--------:|---------:|
...    | GO:0051292 | 6.28405 | 1       |  1       |
...    | GO:0030261 | 5.51863 | 1       |  1       |
...    | GO:0001650 | 5.3019  | 1       |  1       |
...    | GO:0090096 | 5.29709 | 1       |  1       |
...    | GO:0099637 | 5.29709 | 1       |  1       |
...    | GO:0004896 | 1       | 7.98072 |  1       |
...    | GO:0038165 | 1       | 7.93887 |  1       |
...    | GO:0030883 | 1       | 7.03282 |  1       |
...    | GO:0048006 | 1       | 7.03282 |  1       |
...    | GO:0004924 | 1       | 7.03282 |  1       |
...    | GO:0008137 | 1       | 1       | 14.0412  |
...    | GO:0015990 | 1       | 1       | 13.2519  |
...    | GO:0006120 | 1       | 1       | 11.7472  |
...    | GO:0045277 | 1       | 1       | 10.2356  |
...    | GO:0030964 | 1       | 1       |  9.74806 |

>>> odds_ratio
...    | id         |       0 |       1 |       2 |
...    |:-----------|--------:|--------:|--------:|
...    | GO:0051292 | 4.94165 | 0       | 0       |
...    | GO:0030261 | 4.39416 | 0       | 0       |
...    | GO:0001650 | 2.54406 | 0       | 0       |
...    | GO:0090096 | 7.64209 | 0       | 0       |
...    | GO:0099637 | 7.64209 | 0       | 0       |
...    | GO:0004896 | 0       | 3.58059 | 0       |
...    | GO:0038165 | 0       | 6.00685 | 0       |
...    | GO:0030883 | 0       | 5.42189 | 0       |
...    | GO:0048006 | 0       | 5.42189 | 0       |
...    | GO:0004924 | 0       | 5.42189 | 0       |
...    | GO:0008137 | 0       | 0       | 3.76802 |
...    | GO:0015990 | 0       | 0       | 6.61172 |
...    | GO:0006120 | 0       | 0       | 3.65752 |
...    | GO:0045277 | 0       | 0       | 5.44179 |
...    | GO:0030964 | 0       | 0       | 7.02675 |