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Sliding window plot using Python

I want to plot the number of positions in a sliding window of 1000 and a step of 20 for each sample (A-D).

Interpretation:

  • 1: position exists;
  • NA: position does not exist.

I have tested a dozen tools in bash, R and other but I am looking for a Python solution.
Your advice please.

#This is an example of my data:
window = 1000
step = 20

# Example of dataframe
POSITION        A       B       C       D
1250            1       1       1       1 
1750            NA      1       NA      1
1786            1       NA      1       1
1812            1       1       1       1
1855            1       1       1       1
1896            1       NA      1       NA
2635            NA      1       1       1
1689            1       1       NA      NA
3250            1       1       1       1
3655            1       NA      1       1
3589            NA      1       1       1

I am looking for some thing like this:

Polymorphism density plot using a window size of 1,000,000 with an increment (step) of 100,000

Any help will be appreciated!

python snp plot
• 3,252 views • link
written by A_Lh • A_Lh

2 answers

This is a Python solution:

import pandas as pd
import numpy as np
import seaborn as sns
df = pd.DataFrame({'POSITION': [1250,
  1750,
  1786,
  1812,
  1855,
  1896,
  2635,
  1689,
  3250,
  3655,
  3589],
 'A': [1.0, np.nan, 1.0, 1.0, 1.0, 1.0, np.nan, 1.0, 1.0, 1.0, np.nan],
 'B': [1.0, 1.0, np.nan, 1.0, 1.0, np.nan, 1.0, 1.0, 1.0, np.nan, 1.0],
 'C': [1.0, np.nan, 1.0, 1.0, 1.0, 1.0, 1.0, np.nan, 1.0, 1.0, 1.0],
 'D': [1.0, 1.0, 1.0, 1.0, 1.0, np.nan, 1.0, np.nan, 1.0, 1.0, 1.0]})



window = 5
step = 2

df = df.set_index('POSITION').rolling(window).count().reset_index().iloc[::step, :]    
df = df.melt(id_vars='POSITION', value_vars=['A','B','C','D'], value_name='polym', var_name='chromop')    
sns.lineplot(data=df, x='POSITION',y='polym',hue='chromop')
• 0 views • link
written by A_Lh • A_Lh

In R instead of python, but it will do the job:

library(zoo)
library(tidyverse)
count_positions = function(x) sum(x, na.rm=T)
as.data.frame(rollapply(dat[,-1], FUN=count_positions, width=2, by=2)) %>% 
    mutate(index = 1:n()) %>% 
    pivot_longer(-index) %>% 
    ggplot(aes(x=index, y=value, colour=name)) + 
    geom_line()
• 0 views • link
written by 4galaxy77 4galaxy77

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