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Bioinformatics data analysis and visualization toolkit

Project description

DOI

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
git clone https://github.com/reneshbedre/bioinfokit.git
cd bioinfokit
python setup.py install

Volcano plot

bioinfokit.visuz.gene_exp.volcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle, sign_line, dotsize, markerdot, r, dim, show)

Parameters Description
table Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values columns
lfc Name of a column having log 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 [float][default:1.0]
pv_thr P-values or adjusted P-values cutoff for up and downregulated genes [float][default:0.05]
color Tuple of two colors [tuple][default: ("green", "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.
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]
r Figure resolution in dpi [int][default: 300]. Not compatible with show= True
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]
dim Figure size [tuple of two floats (width, height) in inches][default: (5, 5)]
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"]

Returns:

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

MA plot

bioinfokit.visuz.gene_exp.ma(table, lfc, ct_count, st_count, lfc_thr, color, dim, dotsize, show, r, valpha)

Parameters Description
table Pandas dataframe table having atleast gene IDs, log fold change, and 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 [default:value1]
st_count Name of a column having count values for treatment sample [default:value2]
lfc_thr Log fold change cutoff for up and downregulated genes [default:1]
color Tuple of two colors [tuple][default: ("green", "red")]
dim Figure size [tuple of two floats (width, height) in inches][default: (5, 5)]
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"]
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
valpha Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]

Returns:

MA plot image in same directory (ma.png) Working example

Inverted Volcano plot

bioinfokit.visuz.gene_exp.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle, dotsize, markerdot, r, dim, show)

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 [default:1]
pv_thr P-values or adjusted P-values cutoff for up and downregulated genes [default:0.05]
color Tuple of two colors [tuple][default: ("green", "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]
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
dim Figure size [tuple of two floats (width, height) in inches][default: (5, 5)]
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"]

Returns:

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

Working example

Correlation matrix plot

bioinfokit.visuz.stat.corr_mat(table, corm, cmap, r, dim, show)

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]

Returns:

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

Working example

Merge VCF files

bioinfokit.analys.mergevcf(file)

Parameters Description
file Multiple vcf files and separate them by comma

Returns:

Merged VCF file (merge_vcf.vcf)

Working example

Merge VCF files

bioinfokit.analys.mergevcf(file)

Parameters Description
file Multiple vcf files and separate them by comma

Returns:

Merged VCF file (merge_vcf.vcf)

Working example

PCA

bioinfokit.analys.pca(table)

Parameters Description
table Dataframe object with numerical variables (columns). Dataframe should not have identifier column.

Returns:

PCA summary, scree plot (screepca.png), and 2D/3D pca plots (pcaplot_2d.png and pcaplot_3d.png)

Working example

Reverse complement of DNA sequence

bioinfokit.analys.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

Sequencing coverage

bioinfokit.analys.seqcov(file, gs)

Parameters Description
file FASTQ file
gs Genome size in Mbp

Returns:

Sequencing coverage of the given FASTQ file

Working example

Convert TAB to CSV file

bioinfokit.analys.tcsv(file)

Parameters Description
file TAB delimited text file

Returns:

CSV delimited file (out.csv)

Heatmap

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

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)]
clus Draw hierarchical clustering with heatmap [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]
xlable Plot X-label [boolean (True or False)][default: True]
ylable 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

Returns:

heatmap plot (heatmap.png, heatmap_clus.png)

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

Two sample t-test with equal and unequal variance

bioinfokit.analys.stat.ttsam(table, xfac, res, evar)

Parameters Description
table Pandas dataframe. It should be stacked table with independent (xfac) and dependent (res) variable columns.
xfac Independent group column name with two levels [string][default: None]
res Response variable column name [string][default: None]
evar t-test with equal variance [bool (True or False)][default: True]

Returns:

summary output and group boxplot (ttsam_boxplot.png)

Working example

Chi-square test for independence

bioinfokit.analys.stat.chisq(table)

Parameters Description
table Pandas dataframe. It should be contingency table.

Returns:

summary output and variable mosaic plot (mosaic.png)

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

One-way ANOVA

bioinfokit.stat.oanova(table, res, xfac, ph, phalpha)

Parameters Description
table Pandas dataframe in stacked table format
res Response variable (dependent variable) [string][default: None]
xfac Treatments or groups or factors (independent variable) [string][default: None]
ph perform pairwise comparisons with Tukey HSD test [bool (True or False)] [default: False]
phalpha significance level Tukey HSD test [float (0 to 1)][default: 0.05]

Returns:

ANOVA summary, multiple pairwise comparisons, and assumption tests statistics

Working example

Manhatten plot

bioinfokit.visuz.marker.mhat(df, chr, pv, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol, markernames, gfont, valpha, show)

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]
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]
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]
gwas_sign_line Plot statistical significant threshold line defined by option gwasp [bool (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, dict][default: True]
gfont Font size for SNP names to display on the plot [float][default: 8]
valpha Transparency of points on plot [float (between 0 and 1)][default: 1.0]
show Show the figure on console instead of saving in current folder [True or False][default:False]

Returns:

Manhatten plot image in same directory (manhatten.png)

Working example

Extract the sequences from the FASTA file

bioinfokit.analys.extract_seq(file, id)

Parameters Description
file input FASTA file from which sequneces to be extracted
id sequence ID file

Returns: Extracted sequences in FASTA format file in same directory (out.fasta)

Bar-dot plot

bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar, valphadot, show)

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]
show Show the figure on console instead of saving in current folder [True or False][default:False]

Returns:

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

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

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

bioinfokit.visuz.stat.regplot(df, x, y, yhat, dim, colordot, colorline, r, ar, dotsize, markerdot, linewidth, valphaline, valphadot, show)

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]
show Show the figure on console instead of saving in current folder [True or False][default:False]

Returns:

Regression plot image in same directory (reg_plot.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)

bioinfokit cited by:

  • Greaney AM, Adams TS, Raredon MS, Gubbins E, Schupp JC, Engler AJ, Ghaedi M, Yuan Y, Kaminski N, Niklason LE. Platform Effects on Regeneration by Pulmonary Basal Cells as Evaluated by Single-Cell RNA Sequencing. Cell Reports. 2020 Mar 24;30(12):4250-65.

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