Sliding window plot using Python
2
1
Entering edit mode
5 weeks ago
A_Lh ▴ 30

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 • 281 views
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0
Entering edit mode

Previous question: How to plot SNPs distribution on each chromosome?

Just need to adjust BEDOPS bedmap commands to count SNPs over sliding windows, and then feed that as input to the provided script.

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1
Entering edit mode
5 weeks ago
A_Lh ▴ 30

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')
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1
Entering edit mode
5 weeks ago
4galaxy77 ★ 1.2k

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()
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