reneshbedre/bioinfokit reneshbedre/bioinfokit

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Repository Details

Bioinformatics data analysis and visualization toolkit

DOI PyPI version Downloads Build Status Anaconda-Server Badge Buy me a coffee

The bioinfokit toolkit aimed to provide various easy-to-use functionalities to analyze,
visualize, and interpret the biological data generated from genome-scale omics experiments.

How to install:

bioinfokit requires

  • Python 3
  • NumPy
  • scikit-learn
  • seaborn
  • pandas
  • matplotlib
  • SciPy
  • matplotlib_venn

bioinfokit can be installed using pip, easy_install and git.

latest bioinfokit version: PyPI version

Install using pip for Python 3 (easiest way)

# install
pip install bioinfokit

# upgrade to latest version
pip install bioinfokit --upgrade

# uninstall 
pip uninstall bioinfokit

Install using easy_install for Python 3 (easiest way)

# install latest version
easy_install bioinfokit

# specific version
easy_install bioinfokit==0.3

# uninstall 
pip uninstall bioinfokit

Install using conda

conda install -c bioconda bioinfokit

Install using git

# download and install bioinfokit (Tested on Linux, Mac, Windows) 
git clone https://github.com/reneshbedre/bioinfokit.git
cd bioinfokit
python setup.py install

Check the version of bioinfokit

>>> import bioinfokit
>>> bioinfokit.__version__
'0.4'

How to cite bioinfokit?

  • Renesh Bedre. (2020, March 5). reneshbedre/bioinfokit: Bioinformatics data analysis and visualization toolkit. Zenodo. http://doi.org/10.5281/zenodo.3698145.
  • Additionally check Zenodo to cite specific version of bioinfokit

Support

If you enjoy bioinfokit, consider supporting me,

Buy Me A Coffee

Getting Started

Gene expression analysis

Volcano plot

latest update v2.0.8

bioinfokit.visuz.GeneExpression.volcano(df, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, dim, r, ar, dotsize, markerdot, sign_line, gstyle, show, figtype, axtickfontsize, axtickfontname, axlabelfontsize, axlabelfontname, axxlabel, axylabel, xlm, ylm, plotlegend, legendpos, figname, legendanchor, legendlabels, theme)

Parameters Description
df Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values columns
lfc Name of a column having log or absolute fold change values [string][default:logFC]
pv Name of a column having P-values or adjusted P-values [string][default:p_values]
lfc_thr Log fold change cutoff for up and downregulated genes [Tuple or list][default:(1.0, 1.0)]
pv_thr p value or adjusted p value cutoff for up and downregulated genes [Tuple or list][default:(0.05, 0.05)]
color Tuple of three colors [Tuple or list][default: color=("green", "grey", "red")]
valpha Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
geneid Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
genenames Tuple of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, tuple, dict][default: None]
gfont Font size for genenames [float][default: 10.0]. gfont not compatible with gstyle=2.
dim Figure size [Tuple of two floats (width, height) in inches][default: (5, 5)]
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
ar Rotation of X and Y-axis ticks labels [float][default: 90]
dotsize The size of the dots in the plot [float][default: 8]
markerdot Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
sign_line Show grid lines on plot with defined log fold change (lfc_thr) and P-value (pv_thr) threshold value [True or False][default:False]
gstyle Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
show Show the figure on console instead of saving in current folder [True or False][default:False]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
xlm Range of ticks to plot on X-axis [float (left, right, interval)][default: None]
ylm Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
plotlegend plot legend on volcano plot [True or False][default:False]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
figname name of figure [string ][default:"volcano"]
legendanchor position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
legendlabels legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Volcano plot image in same directory (volcano.png) Working example

Inverted Volcano plot

latest update v2.0.8

bioinfokit.visuz.GeneExpression.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle, dotsize, markerdot, r, dim, show, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize, axtickfontname, plotlegend, legendpos, legendanchor, figname, legendlabels, ar, theme)

Parameters Description
table Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values
lfc Name of a column having log fold change values [default:logFC]
pv Name of a column having P-values or adjusted P-values [default:p_values]
lfc_thr Log fold change cutoff for up and downregulated genes [Tuple or list] [default:(1.0, 1.0)]
pv_thr p value or adjusted p value cutoff for up and downregulated genes [Tuple or list] [default:(0.05, 0.05)]
color Tuple of three colors [Tuple or list][default: color=("green", "grey", "red")]
valpha Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
geneid Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
genenames Tuple of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, Tuple, dict][default: None]
gfont Font size for genenames [float][default: 10.0]
gstyle Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
dotsize The size of the dots in the plot [float][default: 8]
markerdot Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
dim Figure size [Tuple of two floats (width, height) in inches][default: (5, 5)]
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
show Show the figure on console instead of saving in current folder [True or False][default:False]
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
axlabelfontsize Font size for axis labels [float][default: 9]
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
plotlegend plot legend on inverted volcano plot [True or False][default:False]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
legendanchor position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
figname name of figure [string ][default:"involcano"]
legendlabels legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
ar Rotation of X and Y-axis ticks labels [float][default: 90]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Inverted volcano plot image in same directory (involcano.png) Working example

MA plot

latest update v2.0.7

bioinfokit.visuz.GeneExpression.ma(df, lfc, ct_count, st_count, pv, basemean, lfc_thr, color, dim, dotsize, show, r, valpha, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize, axtickfontname, xlm, ylm, fclines, fclinescolor, legendpos, legendanchor, figname, legendlabels, plotlegend, ar, theme, geneid, genenames, gfont, gstyle, title)

Parameters Description
df Pandas dataframe table having atleast gene IDs, log fold change, and normalized counts (control and treatment) columns
lfc Name of a column having log fold change values [default:"logFC"]
ct_count Name of a column having count values for control sample.Ignored if basemean provided [default:"value1"]
st_count Name of a column having count values for treatment sample. Ignored if basemean provided [default:"value2"]
pv Name of a column having p values or adjusted p values
basemean Basemean (mean of normalized counts) from DESeq2 results
lfc_thr Log fold change cutoff for up and downregulated genes [Tuple or list][default:(1.0, 1.0)]
color Tuple of three colors [Tuple or list][default: ("green", "grey", "red")]
dotsize The size of the dots in the plot [float][default: 8]
markerdot Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
valpha Transparency of points on plot [float (between 0 and 1)][default: 1.0]
dim Figure size [Tuple of two floats (width, height) in inches][default: (5, 5)]
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
show Show the figure on console instead of saving in current folder [True or False][default:False]
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
axlabelfontsize Font size for axis labels [float][default: 9]
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
xlm Range of ticks to plot on X-axis [float (left, right, interval)][default: None]
ylm Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
fclines draw log fold change threshold lines as defines by lfc [True or False][default:False]
fclinescolor color of fclines [string][default: '#2660a4']
plotlegend plot legend on MA plot [True or False][default:False]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
legendanchor position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
figname name of figure [string ][default:"ma"]
legendlabels legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
ar Rotation of X and Y-axis ticks labels [float][default: 90]
theme Change background theme. If theme set to dark_background, the dark background will be produced instead of default white. See more themes here [string][default:'None']
geneid Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
genenames Tuple of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, Tuple, dict][default: None]
gfont Font size for genenames [float][default: 10.0]
gstyle Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
title Add main title to the plot [string][default: None]

Returns:

MA plot image in same directory (ma.png)

Working example

Heatmap

latest update v2.0.1

bioinfokit.visuz.gene_exp.hmap(table, cmap='seismic', scale=True, dim=(6, 8), rowclus=True, colclus=True, zscore=None, xlabel=True, ylabel=True, tickfont=(12, 12), show, r, figtype, figname, theme)

Parameters Description
file CSV delimited data file. It should not have NA or missing values
cmap Color Palette for heatmap [string][default: 'seismic']
scale Draw a color key with heatmap [boolean (True or False)][default: True]
dim heatmap figure size [Tuple of two floats (width, height) in inches][default: (6, 8)]
rowclus Draw hierarchical clustering for rows [boolean (True or False)][default: True]
colclus Draw hierarchical clustering for columns [boolean (True or False)][default: True]
zscore Z-score standardization of row (0) or column (1). It works when clus is True. [None, 0, 1][default: None]
xlabel Plot X-label [boolean (True or False)][default: True]
ylabel Plot Y-label [boolean (True or False)][default: True]
tickfont Fontsize for X and Y-axis tick labels [Tuple of two floats][default: (14, 14)]
show Show the figure on console instead of saving in current folder [True or False][default:False]
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
figname name of figure [string ][default:"heatmap"]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

heatmap plot (heatmap.png, heatmap_clus.png)

Working example

Clustering analysis

Scree plot

latest update v2.0.1

bioinfokit.visuz.cluster.screeplot(obj, axlabelfontsize, axlabelfontname, axxlabel, axylabel, figtype, r, show, dim, theme)

Parameters Description
obj list of component name and component variance
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
r Figure resolution in dpi [int][default: 300]
show Show the figure on console instead of saving in current folder [True or False][default:False]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Scree plot image (screeplot.png will be saved in same directory)

Working Example

Principal component analysis (PCA) loadings plots

latest update v2.0.1

bioinfokit.visuz.cluster.pcaplot(x, y, z, labels, var1, var2, var3, axlabelfontsize, axlabelfontname, figtype, r, show, plotlabels, dim, theme)

Parameters Description
x loadings (correlation coefficient) for principal component 1 (PC1)
y loadings (correlation coefficient) for principal component 2 (PC2)
z loadings (correlation coefficient) for principal component 3 (PC2)
labels original variables labels from dataframe used for PCA
var1 Proportion of PC1 variance [float (0 to 1)]
var2 Proportion of PC2 variance [float (0 to 1)]
var3 Proportion of PC3 variance [float (0 to 1)]
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
r Figure resolution in dpi [int][default: 300]
show Show the figure on console instead of saving in current folder [True or False][default:False]
plotlabels Plot labels as defined by labels parameter [True or False][default:True]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

PCA loadings plot 2D and 3D image (pcaplot_2d.png and pcaplot_3d.png will be saved in same directory)

Working Example

Principal component analysis (PCA) biplots

latest update v2.0.2

bioinfokit.visuz.cluster.biplot(cscore, loadings, labels, var1, var2, var3, axlabelfontsize, axlabelfontname, figtype, r, show, markerdot, dotsize, valphadot, colordot, arrowcolor, valphaarrow, arrowlinestyle, arrowlinewidth, centerlines, colorlist, legendpos, datapoints, dim, theme)

Parameters Description
cscore principal component scores (obtained from PCA().fit_transfrom() function in sklearn.decomposition)
loadings loadings (correlation coefficient) for principal components
labels original variables labels from dataframe used for PCA
var1 Proportion of PC1 variance [float (0 to 1)]
var2 Proportion of PC2 variance [float (0 to 1)]
var3 Proportion of PC3 variance [float (0 to 1)]
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
r Figure resolution in dpi [int][default: 300]
show Show the figure on console instead of saving in current folder [True or False][default:False]
markerdot Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
dotsize The size of the dots in the plot [float][default: 6]
valphadot Transparency of dots on plot [float (between 0 and 1)][default: 1]
colordot Color of dots on plot [string or list ][default:"#4a4e4d"]
arrowcolor Color of the arrow [string ][default:"#fe8a71"]
valphaarrow Transparency of the arrow [float (between 0 and 1)][default: 1]
arrowlinestyle line style of the arrow. check more styles at https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html [string][default: '-']
arrowlinewidth line width of the arrow [float][default: 1.0]
centerlines draw center lines at x=0 and y=0 for 2D plot [bool (True or False)][default: True]
colorlist list of the categories to assign the color [list][default:None]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
datapoints plot data points on graph [bool (True or False)][default: True]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

PCA biplot 2D and 3D image (biplot_2d.png and biplot_3d.png will be saved in same directory)

Working Example

t-SNE plot

latest update v2.0.1

bioinfokit.visuz.cluster.tsneplot(score, colorlist, axlabelfontsize, axlabelfontname, figtype, r, show, markerdot, dotsize, valphadot, colordot, dim, figname, legendpos, legendanchor, theme)

Parameters Description
score t-SNE component embeddings (obtained from TSNE().fit_transfrom() function in sklearn.manifold)
colorlist list of the categories to assign the color [list][default:None]
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
r Figure resolution in dpi [int][default: 300]
show Show the figure on console instead of saving in current folder [True or False][default:False]
markerdot Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
dotsize The size of the dots in the plot [float][default: 6]
valphadot Transparency of dots on plot [float (between 0 and 1)][default: 1]
colordot Color of dots on plot [string or list ][default:"#4a4e4d"]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
legendanchor position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
figname name of figure [string ][default:"tsne_2d"]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

t-SNE 2D image (tsne_2d.png will be saved in same directory)

Working Example

Normalization

RPM or CPM normalization

latest update v0.8.9

Normalize raw gene expression counts into Reads per million mapped reads (RPM) or Counts per million mapped reads (CPM)

bioinfokit.analys.norm.cpm(df)

Parameters Description
df Pandas dataframe containing raw gene expression values. Genes with missing expression values (NA) will be dropped.

Returns:

RPM or CPM normalized Pandas dataframe as class attributes (cpm_norm)

Working Example

RPKM or FPKM normalization

latest update v0.9

Normalize raw gene expression counts into Reads per kilo base per million mapped reads (RPKM) or Fragments per kilo base per million mapped reads (FPKM)

bioinfokit.analys.norm.rpkm(df, gl)

Parameters Description
df Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.
gl Name of a column having gene length in bp [string][default: None]

Returns:

RPKM or FPKM normalized Pandas dataframe as class attributes (rpkm_norm)

Working Example

TPM normalization

latest update v0.9.1

Normalize raw gene expression counts into Transcript per million (TPM)

bioinfokit.analys.norm.tpm(df, gl)

Parameters Description
df Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.
gl Name of a column having gene length in bp [string][default: None]

Returns:

TPM normalized Pandas dataframe as class attributes (tpm_norm)

Working Example

Variant analysis

Manhattan plot

latest update v2.0.1

bioinfokit.visuz.marker.mhat(df, chr, pv, log_scale, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol, markernames, gfont, valpha, show, figtype, axxlabel, axylabel, axlabelfontsize, ylm, gstyle, figname, theme)

Parameters Description
df Pandas dataframe object with atleast SNP, chromosome, and P-values columns
chr Name of a column having chromosome numbers [string][default:None]
pv Name of a column having P-values. Must be numeric column [string][default:None]
log_scale Change the values provided in pv column to minus log10 scale. If set to False, the original values in pv will be used. This is useful in case of Fst values. [Boolean (True or False)][default:True]
color List the name of the colors to be plotted. It can accept two alternate colors or the number colors equal to chromosome number. If nothing (None) provided, it will randomly assign the color to each chromosome [list][default:None]
gwas_sign_line Plot statistical significant threshold line defined by option gwasp [Boolean (True or False)][default: False]
gwasp Statistical significant threshold to identify significant SNPs [float][default: 5E-08]
dotsize The size of the dots in the plot [float][default: 8]
markeridcol Name of a column having SNPs. This is necessary for plotting SNP names on the plot [string][default: None]
markernames The list of the SNPs to display on the plot. These SNP should be present in SNP column. Additionally, it also accepts the dict of SNPs and its associated gene name. If this option set to True, it will label all SNPs with P-value significant score defined by gwasp [string, list, Tuple, dict][default: True]
gfont Font size for SNP names to display on the plot [float][default: 8]. gfont not compatible with gstyle=2.
valpha Transparency of points on plot [float (between 0 and 1)][default: 1.0]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
r Figure resolution in dpi [int][default: 300]
ar Rotation of X-axis labels [float][default: 90]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
show Show the figure on console instead of saving in current folder [Boolean (True or False)][default:False]
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
axlabelfontsize Font size for axis labels [float][default: 9]
ylm Range of ticks to plot on Y-axis [float Tuple (bottom, top, interval)][default: None]
gstyle Style of the text for markernames. 1 for default text and 2 for box text [int][default: 1]
figname name of figure [string][default:"manhattan"]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Manhattan plot image in same directory (Manhattan.png)

Working example

Variant annotation

latest update v0.9.3

Assign genetic features and function to the variants in VCF file

bioinfokit.analys.marker.vcf_anot(file, id, gff_file, anot_attr)

Parameters Description
file VCF file
id chromosome id column in VCF file [string][default='#CHROM']
gff_file GFF3 genome annotation file
anot_attr Gene function tag in attributes field of GFF3 file

Returns:

Tab-delimited text file with annotation (annotated text file will be saved in same directory)

Working Example

Concatenate VCF files

latest update v0.9.4

Concatenate multiple VCF files into single VCF file (for example, VCF files for each chromosome)

bioinfokit.analys.marker.concatvcf(file)

Parameters Description
file Multiple vcf files separated by comma

Returns:

Concatenated VCF file (concat_vcf.vcf)

Working example

Split VCF file

bioinfokit.analys.marker.splitvcf(file)

Split single VCF file containing variants for all chromosomes into individual file containing variants for each chromosome

Parameters Description
file VCF file to split
id chromosome id column in VCF file [string][default='#CHROM']

Returns:

VCF files for each chromosome

Working example

High-throughput sequence analysis

FASTQ batch downloads from SRA database

latest update v0.9.7

bioinfokit.analys.fastq.sra_bd(file, t, other_opts)

FASTQ files will be downloaded using fasterq-dump. Make sure you have the latest version of the NCBI SRA toolkit (version 2.10.8) is installed and binaries are added to the system path

Parameters Description
file List of SRA accessions for batch download. All accession must be separated by a newline in the file.
t Number of threads for parallel run [int][default=4]
other_opts Provide other relevant options for fasterq-dump [str][default=None]
Provide the options as a space-separated string. You can get a detailed option for fasterq-dump using the -help option.

Returns:

FASTQ files for each SRA accession in the current directory unless specified by other_opts

Description and working example

FASTQ quality format detection

bioinfokit.analys.format.fq_qual_var(file)

Parameters Description
file FASTQ file to detect quality format [deafult: None]

Returns:

Quality format encoding name for FASTQ file (Supports only Sanger, Illumina 1.8+ and Illumina 1.3/1.4)

Working Example

Sequencing coverage

latest update v0.9.7

bioinfokit.analys.fastq.seqcov(file, gs)

Parameters Description
file FASTQ file
gs Genome size in Mbp

Returns:

Sequencing coverage of the given FASTQ file

Description and Working example

Split the sequence into smaller subsequences

latest update v2.0.6

bioinfokit.analys.Fasta.split_seq(seq, seq_size, seq_overlap, any_cond, outfmt)

Parameters Description
seq Input sequence [string]
seq_size subsequence size [int][default: 3]
seq_overlap Split the sequence in overlap mode [bool][default: True]
any_cond Split sequence based on a condition. Note yet defined.
outfmt Output format for the subsequences. If parameter set to 'fasta', the file will be saved in same folder with name output_chunks.fasta ['list' or 'fasta'][default: 'list']

Returns:

Subsequences in list or fasta file (output_chunks.fasta) format

Description and Working example

Reverse complement of DNA sequence

latest update v2.1.1

bioinfokit.analys.Fasta.rev_com(sequence)

Parameters Description
seq DNA sequence to perform reverse complement
file DNA sequence in a fasta file

Returns:

Reverse complement of original DNA sequence

Working example

File format conversions

bioinfokit.analys.format

Function Parameters Description
bioinfokit.analys.format.fqtofa(file) FASTQ file Convert FASTQ file into FASTA format
bioinfokit.analys.format.hmmtocsv(file) HMM file Convert HMM text output (from HMMER tool) to CSV format
bioinfokit.analys.format.tabtocsv(file) TAB file Convert TAB file to CSV format
bioinfokit.analys.format.csvtotab(file) CSV file Convert CSV file to TAB format

Returns:

Output will be saved in same directory

Working example

GFF3 to GTF file format conversion

latest update v1.0.1

bioinfokit.analys.gff.gff_to_gtf(file, trn_feature_name)

Parameters Description
file GFF3 genome annotation file
trn_feature_name Name of the feature (column 3 of GFF3 file) of RNA transcripts if other than 'mRNA' or 'transcript'

Returns:

GTF format genome annotation file (file.gtf will be saved in same directory)

Working Example

Bioinformatics file readers and processing (FASTA, FASTQ, and VCF)

latest update v2.0.4

Function Parameters Description
bioinfokit.analys.Fasta.fasta_reader(file) FASTA file FASTA file reader
bioinfokit.analys.fastq.fastq_reader(file) FASTQ file FASTQ file reader
bioinfokit.analys.marker.vcfreader(file) VCF file VCF file reader

Returns:

File generator object (can be iterated only once) that can be parsed for the record

Description and working example

Extract subsequence from FASTA files

latest update v2.0.4

bioinfokit.analys.Fasta.ext_subseq(file, id, st, end, strand)

Extract the subsequence of specified region from FASTA file. If the target subsequence region is on minus strand. the reverse complementary of subsequence will be printed.

Parameters Description
file FASTA file [file]
id The ID of sequence from FASTA file to extract the subsequence [string]
st Start integer coordinate of subsequnece [int]
end End integer coordinate of subsequnece [int]
strand Strand of the subsequence ['plus' or 'minus'][default: 'plus']

Returns:

Subsequence to stdout

Extract sequences from FASTA file

latest update v2.0.4

bioinfokit.analys.Fasta.extract_seq(file, id)

Extract the sequences from FASTA file based on the list of sequence IDs provided from other file

Parameters Description
file FASTA file [file]
id List of sequence IDs separated by new line [file] or Pandas series

Returns:

Sequences extracted from FASTA file based on the given IDs provided in id file. Output FASTA file will be saved as output.fasta in current working directory.

Split FASTA file into multiple FASTA files

latest update v2.0.4

bioinfokit.analys.Fasta.split_fasta(file, n, bases_per_line)

Split one big FASTA file into multiple smaller FASTA files

Parameters Description
file FASTA file [file]
n Number of FASTA files to split the big FASTA file [int][default: 2]
bases_per_line Number of bases per line for ouput FASTA files [int][default: 60]

Returns:

Number of smaller FASTA files with prefix output (output_0.fasta, output_1.fasta and so on)

Convert multi-line FASTA into single-line FASTA

latest update v2.1.2

bioinfokit.analys.Fasta.multi_to_single_line(file)

Convert multi-line FASTA (where sequences are on multi lines) into single-line FASTA (where sequences are in single line)

Parameters Description
file FASTA file [file]

Returns:

Single line FASTA (output.fasta). Output FASTA file will be saved as output.fasta in current working directory.

Merge counts files from featureCounts

latest update v2.0.5

bioinfokit.analys.HtsAna.merge_featureCount(pattern, gene_column_name)

Merge counts files generated from featureCounts when it runs individually on large samples. The count files must be in same folder and should end with .txt file extension.

Parameters Description
pattern file name pattern for each count file [default: '*.txt']
gene_column_name gene id column name for feature and meta-features [default: 'Geneid']

Returns:

Merge count file (gene_matrix_count.csv) in same folder

Split BED file by chromosome

latest update v2.0.9

bioinfokit.analys.HtsAna.split_bed(bed)

Split the BED file by chromosome names

Parameters Description
bed Input BED file [default: None]

Returns:

BED file for each chromosome (files will be saved in same directory)

Working example

Functional enrichment analysis

Gene family enrichment analysis (GenFam)

latest update v1.0.0

bioinfokit.analys.genfam.fam_enrich(id_file, species, id_type, stat_sign_test, multi_test_corr, min_map_ids, alpha)

GenFam is a comprehensive classification and enrichment analysis tool for plant genomes. It provides a unique way to characterize the large-scale gene datasets such as those from transcriptome analysis (read GenFam paper for more details)

Parameters Description
id_file Text file containing the list of gene IDs to analyze using GenFam. IDs must be separated by newline.
species Plant species ID for GenFam analysis. All plant species ID provided here
id_type Plant species ID type
1: Phytozome locus ID
2: Phytozome transcript ID
3: Phytozome PAC ID
stat_sign_test Statistical significance test for enrichment analysis [default=1].
1: Fisher exact test
2: Hypergeometric distribution
3: Binomial distribution
4: Chi-squared distribution
multi_test_corr Multiple testing correction test [default=3].
1: Bonferroni
2: Bonferroni-Holm
3: Benjamini-Hochberg
min_map_ids Minimum number of gene IDs from the user list (id_file) must be mapped to the background database for performing GenFam analysis [default=5]
alpha Significance level [float][default: 0.05]

Returns:

Attribute Description
df_enrich Enriched gene families with p < 0.05
genfam_info GenFam run information
Output files Output figures and files from GenFam analysis
genfam_enrich.png: GenFam figure for enriched gene families
fam_enrich_out.txt: List of enriched gene families with mapped gene IDs, GO annotation, and detailed statistics
fam_all_out.txt: List of all gene families with mapped gene IDs, GO annotation, and detailed statistics

Description and working example

Check allowed ID types for plant species for GenFam

latest update v1.0.0

bioinfokit.analys.genfam.check_allowed_ids(species)

Parameters Description
species Plant species ID to check for allowed ID type. All plant species ID provided here

Returns:

Allowed ID types for GenFam

Description and working example

Biostatistical analysis

Correlation matrix plot

latest update v2.0.1

bioinfokit.visuz.stat.corr_mat(table, corm, cmap, r, dim, show, figtype, axtickfontsize, axtickfontname, theme)

Parameters Description
table Dataframe object with numerical variables (columns) to find correlation. Ideally, you should have three or more variables. Dataframe should not have identifier column.
corm Correlation method [pearson,kendall,spearman] [default:pearson]
cmap Color Palette for heatmap [string][default: 'seismic']. More colormaps are available at https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 5)]
show Show the figure on console instead of saving in current folder [True or False][default:False]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
axtickfontsize Font size for axis ticks [float][default: 7]
axtickfontname Font name for axis ticks [string][default: 'Arial']
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Correlation matrix plot image in same directory (corr_mat.png)

Working example

Bar-dot plot

latest update v0.8.5

bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar, valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, ylm, axtickfontsize, axtickfontname, yerrlw, yerrcw)

Parameters Description
df Pandas dataframe object
colorbar Color of bar graph [string or list][default:"#bbcfff"]
colordot Color of dots on bar [string or list][default:"#ee8972"]
bw Width of bar [float][default: 0.4]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
r Figure resolution in dpi [int][default: 300]
ar Rotation of X-axis labels [float][default: 0]
hbsize Horizontal bar size for standard error bars [float][default: 4]
errorbar Draw standard error bars [bool (True or False)][default: True]
dotsize The size of the dots in the plot [float][default: 6]
markerdot Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
valphabar Transparency of bars on plot [float (between 0 and 1)][default: 1]
valphadot Transparency of dots on plot [float (between 0 and 1)][default: 1]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
show Show the figure on console instead of saving in current folder [True or False][default:False]
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
ylm Range of ticks to plot on Y-axis [float Tuple (bottom, top, interval)][default: None]
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
yerrlw Error bar line width [float][default: None]
yerrcw Error bar cap width [float][default: None]

Returns:

Bar-dot plot image in same directory (bardot.png)

Working Example

One sample and two sample Z-tests

latest update v2.1.0

bioinfokit.analys.stat.ztest(df, x, y, mu, x_std, y_std, alpha, test_type)

Parameters Description
df Pandas dataframe for appropriate Z-test.
One sample: It should have atleast one variable
Two sample independent: It should have atleast two variables
x column name for x group [string][default: None]
y column name for x group [string][default: None]
mu Population or known mean for the one sample Z-test [float][default: None]
x_std Population standard deviation for x group [float][default: None]
y_std Population standard deviation for y group [float][default: None]
alpha Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]
test_type Type of Z-test [int (1,2)][default: None].
1: One sample Z-test
2: Two sample Z-test

Returns:

Summary output as class attribute (summary and result)

Description and Working example

One sample and two sample (independent and paired) t-tests

latest update v2.1.0

bioinfokit.analys.stat.ttest(df, xfac, res, evar, alpha, test_type, mu)

Parameters Description
df Pandas dataframe for appropriate t-test.
One sample: It should have atleast dependent (res) variable
Two sample independent: It should have independent (xfac) and dependent (res) variables
Two sample paired: It should have two dependent (res) variables
xfac Independent group column name with two levels [string][default: None]
res Dependent variable column name [string or list or Tuple][default: None]
evar t-test with equal variance [bool (True or False)][default: True]
alpha Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]
test_type Type of t-test [int (1,2,3)][default: None].
1: One sample t-test
2: Two sample independent t-test
3: Two sample paired t-test
mu Population or known mean for the one sample t-test [float][default: None]

Returns:

Summary output as class attribute (summary and result)

Description and Working example

Chi-square test

latest update v0.9.5

bioinfokit.analys.stat.chisq(df, p)

Parameters Description
df Pandas dataframe. It should be one or two-dimensional contingency table.
p Theoretical expected probabilities for each group. It must be non-negative and sum to 1. If p is provide Goodness of Fit test will be performed [list or Tuple][default: None]

Returns:

Summary and expected counts as class attributes (summary and expected_df)

Working example

Linear regression analysis

bioinfokit.visuz.stat.lin_reg(df, x, y)

Parameters Description
df Pandas dataframe object
x Name of column having independent X variables [list][default:None]
y Name of column having dependent Y variables [list][default:None]

Returns:

Regression analysis summary

Working Example

Regression plot

latest update v2.0.1

bioinfokit.visuz.stat.regplot(df, x, y, yhat, dim, colordot, colorline, r, ar, dotsize, markerdot, linewidth, valphaline, valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, xlm, ylm, axtickfontsize, axtickfontname, theme)

Parameters Description
df Pandas dataframe object
x Name of column having independent X variables [string][default:None]
y Name of column having dependent Y variables [string][default:None]
yhat Name of column having predicted response of Y variable (y_hat) from regression [string][default:None]
dim Figure size [Tuple of two floats (width, height) in inches][default: (6, 4)]
r Figure resolution in dpi [int][default: 300]
ar Rotation of X-axis labels [float][default: 0]
dotsize The size of the dots in the plot [float][default: 6]
markerdot Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
valphaline Transparency of regression line on plot [float (between 0 and 1)][default: 1]
valphadot Transparency of dots on plot [float (between 0 and 1)][default: 1]
linewidth Width of regression line [float][default: 1]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
show Show the figure on console instead of saving in current folder [True or False][default:False]
axxlabel Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
axylabel Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
xlm Range of ticks to plot on X-axis [float Tuple (bottom, top, interval)][default: None]
ylm Range of ticks to plot on Y-axis [float Tuple (bottom, top, interval)][default: None]
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

Regression plot image in same directory (reg_plot.png)

Working Example

Tukey HSD test

latest update v1.0.3

bioinfokit.analys.stat.tukey_hsd(df, res_var, xfac_var, anova_model, phalpha, ss_typ)

It performs multiple pairwise comparisons of treatment groups using Tukey's HSD (Honestly Significant Difference) test to check if group means are significantly different from each other. It uses the Tukey-Kramer approach if the sample sizes are unequal among the groups.

Parameters Description
df Pandas dataframe with the variables mentioned in the res_var, xfac_var and anova_model options. It should not have missing data. The missing data will be omitted.
res_var Name of a column having response variable [string][default: None]
xfac_var Name of a column having factor or group for pairwise comparison [string][default: None]
anova_model ANOVA model (calculated using statsmodels ols function) [string][default: None]
phalpha Significance level [float][default: 0.05]
ss_typ Type of sum of square to perform ANOVA [int][default: 2]

Returns:

Attribute Description
tukey_summary Pairwise comparisons for main and interaction effects by Tukey HSD test

Description and Working example

Bartlett's test

latest update v1.0.3

bioinfokit.analys.stat.bartlett(df, xfac_var, res_var)

It performs Bartlett's test to check the homogeneity of variances among the treatment groups. It accepts the input table in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.bartlett.html

Parameters Description
df Pandas dataframe containing response (res_var) and independent variables (xfac_var) in a stacked format. It should not have missing data. The missing data will be omitted.
res_var Name of a column having response variable [string][default: None]
xfac_var Name of a column having treatment groups (independent variables) [string or list][default: None]

Returns:

Attribute Description
bartlett_summary Pandas dataframe containing Bartlett's test statistics, degree of freedom, and p value

Description and Working example

Levene's test

latest update v1.0.3

bioinfokit.analys.stat.levene(df, xfac_var, res_var)

It performs Levene's test to check the homogeneity of variances among the treatment groups. It accepts the input table in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.levene.html

Parameters Description
df Pandas dataframe containing response (res_var) and independent variables (xfac_var) in a stacked format. It should not have missing data. The missing data will be omitted.
res_var Name of a column having response variable [string][default: None]
xfac_var Name of a column having treatment groups (independent variables) [string or list][default: None]
center Choice for the Levene's test [string (median, mean, trimmed)] [default: median]
median: Brown-Forsythe Levene-type test
mean: original Levene's test
trimmed: Brown-Forsythe Levene-type test

Returns:

Attribute Description
levene_summary Pandas dataframe containing Levene's test statistics, degree of freedom, and p value

Description and Working example

ROC plot

latest update v2.0.1

bioinfokit.visuz.stat.roc(fpr, tpr, c_line_style, c_line_color, c_line_width, diag_line, diag_line_style, diag_line_width, diag_line_color, auc, shade_auc, shade_auc_color, axxlabel, axylabel, axtickfontsize, axtickfontname, axlabelfontsize, axlabelfontname, plotlegend, legendpos, legendanchor, legendcols, legendfontsize, legendlabelframe, legend_columnspacing, dim, show, figtype, figname, r, ylm, theme)

Receiver operating characteristic (ROC) curve for visualizing classification performance

Parameters Description
fpr Increasing false positive rates obtained from sklearn.metrics.roc_curve [list][default:None]
tpr Increasing true positive rates obtained from sklearn.metrics.roc_curve [list][default:None]
c_line_style Line style for ROC curve [string][default:'-']
c_line_color Line color for ROC curve [string][default:'#f05f21']
c_line_width Line width for ROC curve [float][default:1]
diag_line Plot reference line [True or False][default: True]
diag_line_style Line style for reference line [string][default:'--']
diag_line_width Line width for reference line [float][default:1]
diag_line_color Line color for reference line [string][default:'b']
auc Area under ROC. It can be obtained from sklearn.metrics.roc_auc_score [float][default: None]
shade_auc Shade are for AUC [True or False][default: False]
shade_auc_color Shade color for AUC [string][default: '#f48d60']
axxlabel Label for X-axis [string][default: 'False Positive Rate (1 - Specificity)']
axylabel Label for Y-axis [string][default: 'True Positive Rate (Sensitivity)']
axtickfontsize Font size for axis ticks [float][default: 9]
axtickfontname Font name for axis ticks [string][default: 'Arial']
axlabelfontsize Font size for axis labels [float][default: 9]
axlabelfontname Font name for axis labels [string][default: 'Arial']
plotlegend plot legend [True or False][default:True]
legendpos position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:'lower right']
legendanchor position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
legendcols Number of columns for legends [int][default: 1]
legendfontsize Font size for the legends [float][default:8]
legendlabelframe Box frame for the legend [True or False][default: False]
legend_columnspacing Spacing between the legends [float][default: None]
dim Figure size [Tuple of two floats (width, height) in inches][default: (5, 4)]
show Show the figure on console instead of saving in current folder [True or False][default:False]
figtype Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
figname name of figure [string ][default:'roc']
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
ylm Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
theme Change background theme. If theme set to dark, the dark background will be produced instead of white [string][default:'None']

Returns:

ROC plot image in same directory (roc.png) Working example

Regression metrics

Calculate Root Mean Square Error (RMSE), Mean squared error (MSE), Mean absolute error (MAE), and Mean absolute percent error (MAPE) from regression fit

latest update v1.0.8

bioinfokit.analys.stat.reg_metric(y, yhat, resid)

Parameters Description
y Original values for dependent variable [numpy array] [default: None]
yhat Predicted values from regression [numpy array] [default: None]
resid Regression residuals [numpy array][default: None]

Returns:

Pandas dataframe with values for RMSE, MSE, MAE, and MAPE

Working example

Venn Diagram

bioinfokit.visuz.venn(vennset, venncolor, vennalpha, vennlabel)

Parameters Description
vennset Venn dataset for 3 and 2-way venn. Data should be in the format of (100,010,110,001,101,011,111) for 3-way venn and 2-way venn (10, 01, 11) [default: (1,1,1,1,1,1,1)]
venncolor Color Palette for Venn [color code][default: ('#00909e', '#f67280', '#ff971d')]
vennalpha Transparency of Venn [float (0 to 1)][default: 0.5]
vennlabel Labels to Venn [string][default: ('A', 'B', 'C')]

Returns:

Venn plot (venn3.png, venn2.png)

Working example

References:

  • Travis E. Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006).
  • John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), DOI:10.1109/MCSE.2007.55 (publisher link)
  • Fernando Pérez and Brian E. Granger. IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, 9, 21-29 (2007), DOI:10.1109/MCSE.2007.53 (publisher link)
  • Michael Waskom, Olga Botvinnik, Joel Ostblom, Saulius Lukauskas, Paul Hobson, MaozGelbart, … Constantine Evans. (2020, January 24). mwaskom/seaborn: v0.10.0 (January 2020) (Version v0.10.0). Zenodo. http://doi.org/10.5281/zenodo.3629446
  • Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay. Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830 (2011)
  • Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010)
  • Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ä°lhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.
  • David C. Howell. Multiple Comparisons With Unequal Sample Sizes. https://www.uvm.edu/~statdhtx/StatPages/MultipleComparisons/unequal_ns_and_mult_comp.html

Last updated: November 20, 2021

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