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SV_goat.py
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SV_goat.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 21 16:37:47 2023
@author: h
"""
import numpy as np
import pandas as pd
import re
capra = pd.read_csv(r'F:\svdata\capra_sample281_flt_nohead.vcf',sep='\t')
#capra = pd.read_csv(r'F:\svdata\capra-442-have-sv-change.vcf',sep='\t',header=2208)
sheep = pd.read_csv(r'F:\download\jump_max\sheep-532-have-sv.vcf',sep='\t', header=62)
############************
sheep = pd.read_csv(r'F:\svdata\sheep_532_0516.vcf',sep='\t')
capra = pd.read_csv(r'F:\svdata\capra_281_0516.vcf',sep='\t')
location = pd.read_csv(r'F:\svdata\location.txt',sep=' ',header=None)
'''
indices = capra.loc[capra['INFO'].str.contains('=DEL'), :].index
# 然后，使用这些索引来对原始DataFrame进行赋值
capra.loc[indices, 'type'] = 'DEL'
indices = capra.loc[capra['INFO'].str.contains('=DUP'), :].index
# 然后，使用这些索引来对原始DataFrame进行赋值
capra.loc[indices, 'type'] = 'DUP'
indices = capra.loc[capra['INFO'].str.contains('=INV'), :].index
# 然后，使用这些索引来对原始DataFrame进行赋值
capra.loc[indices, 'type'] = 'INV'
indices = capra.loc[capra['INFO'].str.contains('=TRA'), :].index
# 然后，使用这些索引来对原始DataFrame进行赋值
capra.loc[indices, 'type'] = 'TRA'
indices = capra.loc[capra['INFO'].str.contains('=INS'), :].index
# 然后，使用这些索引来对原始DataFrame进行赋值
capra.loc[indices, 'type'] = 'INS'
'''
'''
INFO = capra[capra['INFO'].str.contains('=TRA')][['#CHROM','POS','INFO']]
#提取TRA信息
# Create a DataFrame
df = INFO
# Function to extract data between given start and end markers
def extract_data(s, start, end):
pattern = re.compile(r'{}(.*?){}'.format(re.escape(start), re.escape(end)))
matches = pattern.findall(s)
return matches[0] if matches else None
# Extract 'CHR2' values
df['CHR2'] = df['INFO'].apply(lambda x: extract_data(x, 'CHR2=', ';'))
# Extract 'END' values, excluding the 'END=' and the trailing ';'
df['END2'] = df['INFO'].apply(lambda x: extract_data(x, 'END=', ';'))
# 提取 merged_df 中的 chr2 和 end2 列，并重置索引以便合并
updated_values = merged_df1[['chr2', 'end2']].reset_index()
# 将 updated_values DataFrame 与 capra_anno_havegene 合并，基于 index 列
# 注意这里使用的是 left join，以保留 capra_anno_havegene 中所有的行
capra_anno_havegene_updated = pd.merge(capra_anno_havegene.reset_index(), updated_values, on='index', how='left', suffixes=('', '_updated'))
# 用 updated_values 中的数据更新 capra_anno_havegene 的对应列
capra_anno_havegene_updated['chr2'] = capra_anno_havegene_updated['chr2_updated'].fillna(capra_anno_havegene_updated['chr2'])
capra_anno_havegene_updated['end2'] = capra_anno_havegene_updated['end2_updated'].fillna(capra_anno_havegene_updated['end2'])
# 删除临时列
capra_anno_havegene_updated.drop(columns=['chr2_updated', 'end2_updated'], inplace=True)
# 恢复原始的 index
capra_anno_havegene_updated.set_index('index', inplace=True)
# 查看更新后的 DataFrame
capra_anno_havegene_updated
'''
#提取breakpoint中的END数据
pattern = r'END=(\d+);'
# 使用 Pandas 的 str.extract 方法提取符合条件的字符串
capra['END_values'] = capra['INFO'].str.extract(pattern)
#
breakpoint_goat = capra[['#CHROM','POS','END_values']]
#capra_281_plink = pd.read_csv(r'F:\svdata\all_test.vcf.vcf',header=582, sep='\t')
capra_281_plink = pd.read_csv(r'F:\svdata\test.vcf',sep='\t',header=560)
#capra_281_input = pd.read_csv(r'F:\svdata\capra_sample281.annovar.input',header=None,sep='\t')
capra_281_plink['type'] = capra['type']
capra_281_plink.insert(2,'end',capra['END_values'])
#SV_type_f = capra_281_plink.iloc[:,3:6]
'''
# 创建第四列 'type'，根据条件填充
def match_types(row):
types = re.findall(r'(DEL|INV|DUP|BND)', row)
if not types: # 如果列表为空，说明未匹配到任何类型
return 'notag'
elif len(set(types)) == 1: # 如果列表中只有一个元素，说明匹配到了一种类型
return types[0]
else:
return 'merge' # 如果列表中有多个元素，说明匹配到了多种类型
# 对每一行打上SV的type标记
SV_type_f['type'] = 0
SV_type_f['type'] = SV_type_f.apply(lambda row: match_types(' '.join(map(str, row))), axis=1)
#先找TRA和BND
find_TRA = capra[capra.applymap(lambda x: pd.notna(x) and ('BND:' in str(x) or 'TRA:' in str(x)))]
TRA = find_TRA.dropna(how='all')
x = capra.iloc[TRA.index]
SV_type_f.iloc[TRA.index,-1] = 'BND'
#其他的notag为INV
SV_type_f.iloc[SV_type_f.query('type=="notag"').index,-1]='INV_1'
# 定义一个函数来从字符串中提取目标字符,获取INV的END
def extract_end(row):
pattern = r'\[(.*?)\[|\](.*?)\]'
ref_match = re.search(pattern, ['REF'])
alt_match = re.search(pattern, row['ALT'])
if ref_match and alt_match:
return ''.join(part for part in ref_match.groups() + alt_match.groups() if part is not None)
elif ref_match:
return ''.join(part for part in ref_match.groups() if part is not None)
elif alt_match:
return ''.join(part for part in alt_match.groups() if part is not None)
else:
return None
INV_END = SV_type_f.query('type=="INV_1"').apply(extract_end, axis=1)
capra_281_plink['type'] = SV_type_f['type']
capra_281_plink.iloc[capra_281_plink.query('type=="INV_1"').index,2] =INV_END
BND_CHRB_END = SV_type_f.query('type=="BND"').apply(extract_end, axis=1)
capra_281_plink.iloc[capra_281_plink.query('type=="BND"').index,2] = BND_CHRB_END
#capra_281_plink中的end列的每一项如果出现有：，则将其值替换为：以后的字符
capra_281_plink.end = capra_281_plink.end.astype(str)
def replace_colon(value):
"""
Parameters
----------
value : TYPE
DESCRIPTION.
Returns
-------
TYPE
DESCRIPTION.
"""
if ':' in value:
return value.split(':', 1)[1] # 将冒号后的部分提取出来
else:
return value
capra_281_plink['end'] = capra_281_plink['end'].apply(replace_colon)
'''
#capra_281_plink.insert(3,'len',capra_281_plink['end']-capra_281_plink['POS'])
#开始进行数目统计
#求长度diff
capra_281_plink_data = capra_281_plink.iloc[:,-282:-1]
#capra_281_plink.iloc[:,10:-3]
'''
#重新将分配给BND的INV找出来
find_INV = capra[capra.applymap(lambda x: pd.notna(x) and ('INV:' in str(x)))]
INV = find_INV.dropna(how='all')
capra_281_plink.iloc[INV.index,-1] = "INV"
'''
#将无法计算长度的TRA长度值赋予NA
capra_281_plink['diff'] = pd.to_numeric(capra_281_plink['end'], errors='coerce', downcast='float') - pd.to_numeric(capra_281_plink['POS'], errors='coerce', downcast='float')
'''
capra_281_plink_data['type'] = capra_281_plink['type']
capra_281_plink_data = capra_281_plink_data.replace('INV_1','INV')
capra_281_plink = capra_281_plink.replace('INV_1','INV')
'''
#sheep_532_plink_0110.to_csv(r'F:\svdata\sheep_532_plink_0110.vcf',sep='\t',index=None)
'''
#删除出现了重复的反向的inversion
capra_281_plink[capra_281_plink['diff'] <-1]
#先计算反向INV中end与其他所有POS相等的值，然后用end在反向INV的POS中验证重复的INV
same = capra_281_plink[capra_281_plink['POS'].isin(capra_281_plink[capra_281_plink['diff'] <-1]['end'].astype(int))]
s_f1 = capra_281_plink[capra_281_plink['diff'] <-1]['POS']
dup_INV = s_f1[s_f1.astype(int).isin(same['end'].astype(int))].index
#删除重复INV
capra_281_plink = capra_281_plink.drop(dup_INV)
#考虑删除merge了多种SV的行87069 --> 87040
capra_281_plink = capra_281_plink.query('type!="merge"')
'''
'''
处理INV反向重复
——————————————————————————————————————————————————————————
'''
capra_281_plink.iloc[:,1] = capra_281_plink.iloc[:,1].astype(int)
capra_281_plink.iloc[:,2] = capra_281_plink.iloc[:,2].astype(int)
checked_pairs = {}
# 循环遍历 DataFrame 数据，检查反向重复且在同一染色体上
for idx, row in capra_281_plink.iterrows():
pos = row['POS']
end = row['end']
chrom = row['#CHROM']
# 构建反向键
reverse_key = (chrom, end, pos)
# 检查是否已存在反向键，并确认是否在同一条染色体上
if reverse_key in checked_pairs:
# 仅添加后出现的行索引到删除列表
checked_pairs[reverse_key]['duplicates'].append(idx)
else:
# 否则，创建新键，记录首次出现的索引和后续重复的索引
checked_pairs[(chrom, pos, end)] = {'first': idx, 'duplicates': []}
# 获取所有重复的索引，仅包括后出现的索引
to_remove = [index for pair in checked_pairs.values() for index in pair['duplicates']]
# 删除这些索引的行
capra_281_plink = capra_281_plink.drop(to_remove)
#capra = capra.drop(to_remove)
'''
处理TRA重复
——————————————————————————————————————————————————————————
'''
# 筛选出 type 为 "TRA" 的数据
tra_data = capra_281_plink[capra_281_plink['type'] == 'TRA']
# 查找具有反向重复的行的索引
# 使用字典存储检查过的组合，避免重复检查
checked_pairs = {}
# 循环遍历 DataFrame 数据，检查反向重复
for idx, row in tra_data.iterrows():
pos = row['POS']
end = row['end']
# 构建反向键
reverse_key = (end, pos)
# 检查是否已存在反向键
if reverse_key in checked_pairs:
# 仅添加后出现的行索引到删除列表
checked_pairs[reverse_key]['duplicates'].append(idx)
else:
# 否则，创建新键，记录首次出现的索引和后续重复的索引
checked_pairs[(pos, end)] = {'first': idx, 'duplicates': []}
# 获取所有重复的索引，仅包括后出现的索引
to_remove = [index for pair in checked_pairs.values() for index in pair['duplicates']]
# 删除这些索引的行
capra_281_plink_clean = capra_281_plink.drop(to_remove)
#capra = capra.drop(to_remove)
#————————————————————————————————————————————————————————————————————————————————————————————
capra_281_plink = capra_281_plink_clean
#diff相减后要+1才是实际值
capra_281_plink['diff'] = capra_281_plink['diff'].abs()
capra_281_plink['diff'] = capra_281_plink['diff'] + 1
capra_281_plink = capra_281_plink.reset_index(drop=True)
capra_281_plink.iloc[capra_281_plink.query('type=="TRA"').index,-1]=None
'''###############################################
capra_281_plink.to_csv(r'F:\svdata\capra_281_plink_clean.vcf', index=None, sep='\t')
capra_281_plink = pd.read_csv(r'F:\svdata\capra_281_plink_clean.vcf',sep='\t')
capra_281_plink.type.value_counts()
Out[1058]:
DEL 65731
TRA 11372
DUP 6596
INV 2583
INS 1
Name: type, dtype: int64
sheep_532_plink_0110.to_csv(r'F:\svdata\sheep_532_plink_0110_clean.vcf', index=None, sep='\t')
sheep_532_plink_0110.type.value_counts()
'''
#capra.iloc[capra_281_plink.index,:-1].to_csv(r'F:\svdata\capra_svout_87404_nohead.vcf',index=None,sep='\t')
capra_281_plink_data = capra_281_plink.filter(regex='RR')
capra_281_plink_data_except_type = capra_281_plink_data.iloc[:, :]
# 找出元素为 "1/1" 或 "0/1" 的行
filtered_rows = capra_281_plink_data_except_type[(capra_281_plink_data_except_type == '1/1') | (capra_281_plink_data_except_type == '0/1')]
# 合并 type 列
capra_281_plink_data['type'] = capra_281_plink['type']
merged_type = pd.concat([filtered_rows, capra_281_plink_data['type'], capra_281_plink['diff']], axis=1)
# 遍历每一列，统计非缺失值的 type 列的出现次数
all_SVtype_data = []
for col in merged_type.columns[:-2]: # 排除最后一列 type
ind_sv = merged_type[[col, 'type']].dropna()['type']
non_na_type_counts = ind_sv.value_counts()
DEL_len = merged_type.iloc[ind_sv[ind_sv == 'DEL'].index]['diff'].abs().sum()
DUP_len = merged_type.iloc[ind_sv[ind_sv == 'DUP'].index]['diff'].abs().sum()
INV_len = merged_type.iloc[ind_sv[ind_sv == 'INV'].index]['diff'].abs().sum()
INS_len = merged_type.iloc[ind_sv[ind_sv == 'INS'].index]['diff'].abs().sum()
if INS_len !=0:
all_SVtype_data.append([merged_type[[col]].columns[0], non_na_type_counts.DEL, non_na_type_counts.DUP, non_na_type_counts.TRA, non_na_type_counts.INV, non_na_type_counts.INS, DEL_len, DUP_len, INV_len, INS_len])
else:
all_SVtype_data.append([merged_type[[col]].columns[0], non_na_type_counts.DEL, non_na_type_counts.DUP, non_na_type_counts.TRA, non_na_type_counts.INV, 0, DEL_len, DUP_len, INV_len, 0])
#print(f"Counts for {col}:")
#print(non_na_type_counts)
#print()
SVtype_data = pd.DataFrame(all_SVtype_data)
SVtype_data.columns = ['name', 'DEL', 'DUP', 'TRA', 'INV', 'INS', 'DEL_len', 'DUP_len', 'INV_len', 'INS_len']
#将vcf文件的sample中.1，.2这种删掉
#SVtype_data.iloc[SVtype_data[SVtype_data.name.str.contains('\.')].index,0] = [i[:-2] for i in SVtype_data[SVtype_data.name.str.contains('\.')].name]
SVtype_data['all_number'] = SVtype_data.DEL + SVtype_data.TRA + SVtype_data.DUP + SVtype_data.INV + SVtype_data.INS
SVtype_data['all_len'] = SVtype_data.DEL_len + SVtype_data.DUP_len + SVtype_data.INV_len + SVtype_data.INS_len
#根据名称信息将SRR转为对应的物种名
species = pd.read_csv(r'C:\Users\h\Desktop\SVdata\Species.txt',sep='\t')
species = species.iloc[:,[0,5,-1]]
species.columns = ['Species','code','name']
SV_result = pd.merge(species, SVtype_data, on='name')
#SVtype_data.to_excel(r'C:\Users\h\Desktop\GB图片修改\Table6.xlsx')
#SVtype_data.to_excel(r'C:\Users\h\Desktop\GB图片修改\Table6_sheep.xlsx') #20240514修改
SV_result.to_excel(r'C:\Users\h\Desktop\GB图片修改\Table6.xlsx')
#最终结果
capra_281_plink.to_csv(r'F:\svdata\SV_count_result.csv',index=None)
#统计table S7
#50-100bp 100bp-250bp 250bp-500bp 500bp-1kb 1kb-2kb 2kb-5kb 5kb-10kb 10kb-50kb 50kb-100kb 100kb-500kb 500kb-1Mb Total
def count_length(SV):
a = len(capra_281_plink.query('type==@SV and 50<diff<=100'))
b = len(capra_281_plink.query('type==@SV and 100<diff<=250'))
c = len(capra_281_plink.query('type==@SV and 250<diff<=500'))
d = len(capra_281_plink.query('type==@SV and 500<diff<=1000'))
e = len(capra_281_plink.query('type==@SV and 1000<diff<=2000'))
f = len(capra_281_plink.query('type==@SV and 2000<diff<=5000'))
g = len(capra_281_plink.query('type==@SV and 5000<diff<=10000'))
h = len(capra_281_plink.query('type==@SV and 10000<diff<=50000'))
i = len(capra_281_plink.query('type==@SV and 50000<diff<=100000'))
j = len(capra_281_plink.query('type==@SV and 100000<diff<=500000'))
k = len(capra_281_plink.query('type==@SV and 500000<diff<=1000000'))
return [a,b,c,d,e,f,g,h,i,j,k]
capra_281_plink['diff'] = capra_281_plink['diff'].abs()
all_count_length = []
all_count_length.append(count_length('DEL'))
all_count_length.append(count_length('DUP'))
all_count_length.append(count_length('INV'))
all_count_length.append(count_length('INS'))
pd.DataFrame(all_count_length)
#capra_281_plink[['#CHROM','POS','end','type']].query('type=="DEL"').to_csv(r'F:\svdata\capra_281_DEL.bed',index=None,header=None,sep='\t')
'''
Out[411]:
0 1 2 3 4 5 6 7 8 9 10
0 20053 16619 10638 8010 5850 2933 1002 462 77 72 15
1 766 3078 1130 661 533 187 92 112 24 12 1
2 80 227 350 737 674 271 106 86 11 29 11
3 1 0 0 0 0 0 0 0 0 0 0
'''
"____________________________________________________________________________"
"注释结果与基因数目和分布统计_______________________________________________________________________"
capra_anno_input
#Capra
#capra_anno = pd.read_csv(r'F:\svdata\capra_87404.sv.vcf.anno.variant_function',sep='\t',header=None)
#capra_anno = pd.read_csv(r'F:\svdata\capra_87404.type.anno.variant_function',sep='\t',header=None)
capra_anno = pd.read_csv(r'F:\svdata\capra_281_0515.variant_function',sep='\t',header=None)
#sheep_anno = pd.read_csv(r'F:\svdata\sheep_532_type.vcf.variant_function',sep='\t',header=None)
#capra_281_0515.anno
#capra_anno = capra_anno.iloc[:,3:]
capra_anno.columns = [0,1,2,3,4,5,6,7,8,9,10,11]
capra_anno_havegene = capra_anno[capra_anno[0] != "intergenic"]
"""
capra_anno[0].value_counts()
Out[1465]:
intergenic 49629
intronic 30384
upstream 1908
downstream 1808
exonic 1642
splicing 1073
ncRNA_intronic 846
UTR3 682
UTR5 375
ncRNA_exonic 291
ncRNA_splicing 31
Name: 0, dtype: int64
"""
#####################################################################################
# Apply the function to each row and expand any rows with multiple genes into separate rows
expanded_rows = []
for idx, row in capra_anno_havegene.iterrows():
genes = extract_genes(row[1])
for gene in genes:
new_row = row.copy()
new_row['gene'] = gene
expanded_rows.append(new_row)
#capra_anno_havegene
# Create a new DataFrame from the expanded rows
expanded_df = pd.DataFrame(expanded_rows).reset_index(drop=True)
expanded_df
count_gene_occurrences = capra_anno_havegene[1].str.contains('gene-').sum()
#用正则表达式提取所有基因
pattern = 'gene-(.*?)(?=,|$|\()'
#提取基因
all_gene_location = capra_anno_havegene[1].str.extractall(pattern).astype(str)
all_gene_loc
INFO = capra[capra['INFO'].str.contains('=TRA')][['#CHROM','POS','INFO']]
df = INFO
df['CHR2'] = df['INFO'].apply(lambda x: extract_data(x, 'CHR2=', ';'))
df['END2'] = df['INFO'].apply(lambda x: extract_data(x, 'END=', ';'))
capra['chr2'] = capra['#CHROM']
capra.loc[capra['type'] == "TRA", 'chr2'] = df['CHR2'].values
"____________________________________________________________________________"
"计算有多少个不重复基因，基因被外显子注释、内含子注释、还是都有。_______________________________________________________________________"
#其中一共有12908个基因被SV注释到，其中1725个基因被外显子注释到，454个基因只被外显子注释到。
#df_exploded[df_exploded[0]=='exonic'][1].drop_duplicates()
#df_exploded[df_exploded[0]=='intronic'][1].drop_duplicates()
import pandas as pd
def clean_gene_name(gene):
# 检查'gene-'前缀是否存在
if 'gene-' in gene:
# 如果存在，移除前缀和括号后的内容
return gene.strip().split('gene-')[1].split('(')[0]
else:
# 如果'gene-'前缀不存在，返回原始字符串（或可以选择返回None或特定的占位符）
return gene.strip()
"""
#capra_anno_havegene[[0, 10]] 计算DEL的不同类型,计算正文表2
#gene_SV_type = capra_anno_havegene[[0, 10]]
#gene_SV_type.columns = ['loc', 'type']
#pd.DataFrame(gene_SV_type.query('type=="DEL"')['loc']).value_counts()
import pandas as pd
# 假设 df 和 common_gene 已经被正确加载
import pandas as pd
import re
# 假设 df 和 common_gene 已经被正确加载
# 初始化一个空的DataFrame用于存放结果
result_df = pd.DataFrame(columns=df.columns)
# 更新正则表达式以匹配包括破折号和数字在内的基因名称
gene_pattern = r'-(\w[\w-]*\w)(?=[(,]|$)'
# 遍历df的每一行
for index, row in df.iterrows():
# 使用正则表达式找出所有满足条件的基因名称
genes = re.findall(gene_pattern, row[1])
# 遍历找到的基因名称
for gene in genes:
# 如果基因名称在common_gene中，则创建一个新的行添加到result_df中
if gene in common_gene[0].values:
new_row = row.copy()
new_row[1] = gene # 将第二列设置为当前的基因名称
result_df = result_df.append(new_row, ignore_index=True)
print(result_df)
result_df.query('type=="DUP"')['loc'].value_counts()
"""
'''
# Assuming capra_anno_havegene is similar to the sample_df defined earlier, with an additional requirement for handling multiple genes
def extract_genes(s):
pattern = re.compile(r'gene-([^\(,]+)')
return pattern.findall(s)
# Apply the function to each row and expand any rows with multiple genes into separate rows
expanded_rows = []
for idx, row in sample_df.iterrows():
genes = extract_genes(row[1])
for gene in genes:
new_row = row.copy()
new_row['gene'] = gene
expanded_rows.append(new_row)
#capra_anno_havegene
# Create a new DataFrame from the expanded rows
expanded_df = pd.DataFrame(expanded_rows).reset_index(drop=True)
expanded_df
import pandas as pd
# 假设 capra_ref 已经加载到 DataFrame 中
# 示例提取基因名、染色体、起始位置和结束位置
capra_ref = capra_ref[capra_ref[2] == 'gene']
capra_ref['gene'] = capra_ref[8].str.extract('Name=([^;]+)')
capra_ref['chromosome'] = capra_ref[0]
capra_ref['start_position'] = capra_ref[3]
capra_ref['end_position'] = capra_ref[4]
# 现在创建一个新的 DataFrame 仅包含所需列
genes_info = capra_ref[['gene', 'chromosome', 'start_position', 'end_position']].dropna()
expanded_df_merge = pd.merge(expanded_df, genes_info, on='gene', how='left')
expanded_df_merge.to_excel(r'F:\svdata\S14.xlsx')
# 示例提取基因名、染色体、起始位置和结束位置
sheep_ref = pd.read_csv(r'F:\svdata\Oar_rambouillet_v1.0x.gff',sep='\t',comment='#',header=None)
sheep_ref = sheep_ref[sheep_ref[2] == 'gene']
sheep_ref['gene'] = sheep_ref[8].str.extract('Name=([^;]+)')
sheep_ref['chromosome'] = sheep_ref[0]
sheep_ref['start_position'] = sheep_ref[3]
sheep_ref['end_position'] = sheep_ref[4]
# 现在创建一个新的 DataFrame 仅包含所需列
genes_info = sheep_ref[['gene', 'chromosome', 'start_position', 'end_position']].dropna()
expanded_df_merge = pd.merge(expanded_df, genes_info, on='gene', how='left')
expanded_df_merge.to_excel(r'F:\svdata\S15.xlsx')
result = expanded_df[[0,2,3,'chr2','end2','gene']]
gff = pd.read_csv(r'F:\svdata\extracted_genes.txt',header=None,sep=' ')
#转换GFF
# Adjusted function for replacing chromosome identifiers
def replace_chromosome_v2(chromosome):
prefix = "NC_0308"
if chromosome.startswith(prefix):
try:
# Extract the numerical part after the prefix and convert to integer
num = int(chromosome[len(prefix):].split('.')[0])
# Check if the number is within the desired range
if 8 <= num <= 36:
return str(num - 7) # Adjust the range to start from 1
except ValueError:
# In case the conversion to integer fails, return the original chromosome value
return chromosome
return chromosome
# Apply the adjusted function to the first column of the DataFrame
gff[0] = gff[0].apply(replace_chromosome_v2)
gff.columns = ['a','b','c','gene']
merge = pd.merge(expanded_df, gff, on='gene')
pd.merge(expanded_df, gff, on='gene').to_excel(r'F:\svdata\S14.xlsx')
# Adding new columns based on the conditions described by the user
merged_df['new_col_goat'] = np.where(merged_df['Chromosome_goat'] == '', '', merged_df['gene'])
merged_df['new_col_sheep'] = np.where(merged_df['Chromosome_sheep'] == '', '', merged_df['gene'])
######S15
sheep_result = pd.read_excel(r'C:\Users\h\Desktop\GB图片修改\Additional file 2.xlsx',sheet_name="Table S13 √",header=1)
goat_result = pd.read_excel(r'C:\Users\h\Desktop\GB图片修改\Additional file 2.xlsx',sheet_name="Table S14 √", header=1)
common_goat = pd.merge(common_genes, goat_result, on=['Gene ID'])
common_sheep = pd.merge(common_genes, sheep_result, on=['Gene ID'])
common_goat.columns = ['gene', 'Chromosome', 'Gene start', 'Gene end', 'SV chrA', 'SV start', 'SV chrB', 'SV end', 'SV type', 'Annotation']
common_sheep.columns = ['gene', 'Chromosome', 'Gene start', 'Gene end', 'SV chrA', 'SV start', 'SV chrB', 'SV end', 'SV type', 'Annotation']
# 为每个基因分配一个顺序号
common_sheep['order'] = common_sheep.groupby('gene').cumcount()
common_goat['order'] = common_goat.groupby('gene').cumcount()
# 找到每个基因在两个 DataFrame 中的最大出现次数
max_order_sheep = common_sheep.groupby('gene')['order'].max()
max_order_goat = common_goat.groupby('gene')['order'].max()
max_order = pd.concat([max_order_sheep, max_order_goat], axis=1).max(axis=1)
# 创建一个以基因和顺序号为组合键的 DataFrame
sheep_expanded = common_sheep.set_index(['gene', 'order']).reindex(
pd.MultiIndex.from_tuples(
[(gene, order) for gene in max_order.index for order in range(max_order[gene] + 1)],
names=['gene', 'order']
)
).reset_index()
goat_expanded = common_goat.set_index(['gene', 'order']).reindex(
pd.MultiIndex.from_tuples(
[(gene, order) for gene in max_order.index for order in range(max_order[gene] + 1)],
names=['gene', 'order']
)
).reset_index()
# 外部合并扩展后的 DataFrame
merged_df = pd.merge(sheep_expanded, goat_expanded, on=['gene', 'order'], how='outer', suffixes=('_sheep', '_goat'))
# 填充 NaN 值为空白
merged_df.fillna('', inplace=True)
# 删除辅助列 'order'
merged_df.drop('order', axis=1, inplace=True)
'''
# 应用更健壮的处理方法
df = capra_anno_havegene.iloc[:,:2]
split_genes = df[1].str.split(',').explode()
cleaned_genes = split_genes.apply(clean_gene_name)
# 重新统计不同基因的总数
unique_genes_count = cleaned_genes.nunique()
# 重新构建DataFrame，以便于筛选包含'exonic'的基因
df_exploded = pd.DataFrame({0: df[0].repeat(split_genes.groupby(split_genes.index).size()), 1: cleaned_genes})
# 筛选包含'exonic'的行，并提取基因名称
exonic_genes = df_exploded[1][df_exploded[0].str.contains("exonic")].unique()
# 重新计算包含'exonic'的不同基因数量
exonic_genes_count = len(exonic_genes)
unique_genes_count, exonic_genes_count
# 找到所有出现过的基因
all_genes = cleaned_genes.unique()
#goat_gene = all_genes
#sheep_gene = all_genes
# 初始化一个列表来保存只有'exonic'没有其他位置信息的基因
exonic_only_genes = []
# 对于每个基因，检查它是否只与'exonic'相关联
for gene in all_genes:
# 找到这个基因的所有位置信息
gene_positions = df_exploded[df_exploded[1] == gene][0].unique()
# 如果这个基因的位置信息只有'exonic'，则添加到列表中
if len(gene_positions) == 1 and 'exonic' in gene_positions:
exonic_only_genes.append(gene)
# 计算只有'exonic'没有其他位置信息的基因数量
exonic_only_genes_count = len(exonic_only_genes)
exonic_only_genes_count
"____________________________________________________________________________"
#去重
all_gene = all_gene_location.drop_duplicates()
#all_gene = all_genes
result_str = ','.join(all_gene[0])
gene_df = pd.DataFrame(result_str.split(','))
#删除LOC开头的基因
gene_df = gene_df[~gene_df[0].str.startswith('LOC')]
#gene_df_sheep = pd.DataFrame(sheep_gene)
#gene_df_goat = pd.DataFrame(goat_gene)
#Sheep
sheep_anno = pd.read_csv(r'F:\svdata\sheep_532.type.anno1.variant_function',sep='\t',header=None)
#sheep_anno = pd.read_csv(r'F:\svdata\sheep-532-have-sv.annovar.variant_function',sep='\t',header=None)
sheep_anno_havegene = sheep_anno[sheep_anno[0] != "intergenic"]
all_gene_location = sheep_anno_havegene[1].str.extractall(pattern).astype(str)
"""
extracted_genes = sheep_anno_havegene[1].str.extractall(pattern).astype(str)
# 通过索引匹配将提取后的基因名称与原 DataFrame 合并
result = sheep_anno_havegene.loc[extracted_genes.index.get_level_values(0)].copy()
result['gene'] = extracted_genes[0].values
"""
all_gene = all_gene_location.drop_duplicates()
result_str = ','.join(all_gene[0])
gene_df_sheep = pd.DataFrame(result_str.split(','))
gene_df_sheep = gene_df_sheep[~gene_df[0].str.startswith('LOC')]
#计算SV注释到的merge基因
sheep_goat_merge = pd.merge(gene_df_sheep, gene_df, on='gene')
#计算山羊和绵羊SV注释到基因的交集 5948
pd.merge(gene_df, gene_df_sheep)
#common_gene.sort_values(by=[0])
"____________________________________________________________________________"
"Figure3A计算热点区域中有多少基因绘图 绘图网站：http://mg2c.iask.in/mg2c_v2.1/_______________________________________________________________________"
goat_hotspot_anno = pd.read_csv(r'F:\svdata\capra_hotspot_sv_anno.variant_function',sep='\t',header=None)
capra_anno_havegene = goat_hotspot_anno[goat_hotspot_anno[3] != "intergenic"]
count_gene_occurrences = capra_anno_havegene[4].str.contains('gene-').sum()
#用正则表达式提取所有基因
pattern = 'gene-(.*?)(?=,|$|\()'
#提取基因
all_gene_location = capra_anno_havegene[4].str.extractall(pattern).astype(str)
#去重
capra_all_gene = all_gene_location.drop_duplicates()
############ goat 1516个
sheep_hotspot_anno = pd.read_csv(r'F:\svdata\sheep_hotspot_sv_anno.variant_function',sep='\t',header=None)
sheep_anno_havegene = sheep_hotspot_anno[sheep_hotspot_anno[3] != "intergenic"]
count_gene_occurrences = sheep_anno_havegene[4].str.contains('gene-').sum()
#用正则表达式提取所有基因
pattern = 'gene-(.*?)(?=,|$|\()'
#提取基因
all_gene_location = sheep_anno_havegene[4].str.extractall(pattern).astype(str)
#去重
sheep_all_gene = all_gene_location.drop_duplicates()
#df_combined = df.groupby(['chr', 's', 't'])['gene'].apply(','.join).reset_index()
"____________________________________________________________________________"
"Figure3a数据计算,绘图文件准备_______________________________________________________________________"
#输出基因交集，用于绘制figure 3a
pd.merge(capra_all_gene,sheep_all_gene).to_csv(r'F:\svdata\hotspot_gene_goat-sheep.txt', index=None,header=None,sep='\t')
goat_sheep_hotspotgene = pd.merge(capra_all_gene,sheep_all_gene)
#输出sheep的热点区域与其注释基因的交集，在此处读取
sheep_hotspot_gene_old = pd.read_csv(r'F:\svdata\sheep_hotspot_sv.txt',sep='\t',header=None)
sheep_hotspot_gene = sheep_hotspot_gene_old.iloc[:,:7]
sheep_hotspot_gene = sheep_hotspot_gene[sheep_hotspot_gene[6] != "intergenic"]
pattern = 'gene-(.*?)(?=,|$|\()'
#提取基因
sheep_hotspot_gene = sheep_hotspot_gene.loc[sheep_hotspot_gene[7].drop_duplicates().index]
sheep_hotspot_gene[7] = sheep_hotspot_gene[7].replace(to_replace=r'gene-|\([^)]*\)', value='', regex=True)
sheep_hotspot_gene.columns = ['chr1','s1','t1','chr2','s2','t2','type','gene']
#去重
sheep_hotspot_gene = sheep_hotspot_gene.loc[sheep_hotspot_gene.gene.drop_duplicates().index]
goat_sheep_hotspotgene
goat_sheep_hotspotgene.columns = ['gene']
all_merge = pd.merge(sheep_hotspot_gene, goat_sheep_hotspotgene,on='gene')
all_merge.to_excel(r'F:\svdata\sheep_goat_hotspotgene.xlsx')
#进行处理#GPR146
all_merge = pd.read_excel(r'F:\svdata\sheep_goat_hotspotgene.xlsx')
#合并行,准备作为绘图数据
all_merge_plot = all_merge.groupby(['chr1', 's1', 't1'])['gene'].apply(','.join).reset_index()
"____________________________________________________________________________"
"Figure2a数据计算_______________________________________________________________________"
type1 = pd.read_csv(r'F:\svdata\type.txt','\t', header=None)
#type1 = pd.read_csv(r'F:\svdata\sheep_type1.txt','\t', header=None)
"""
######################对sheep的SV进行分类
find_DEL = sheep.loc[sheep['INFO'].str.contains('=DEL'), :].index
find_INV = sheep.loc[sheep['INFO'].str.contains('=INV'), :].index
find_DUP = sheep.loc[sheep['INFO'].str.contains('=DUP'), :].index
find_TRA = sheep.loc[sheep['INFO'].str.contains('=TRA'), :].index
find_INS = sheep.loc[sheep['INFO'].str.contains('=INS'), :].index
sheep_532_plink_0110.loc[find_DEL, 'type'] = 'DEL'
sheep_532_plink_0110.loc[find_INV, 'type'] = 'INV'
sheep_532_plink_0110.loc[find_DUP, 'type'] = 'DUP'
sheep_532_plink_0110.loc[find_TRA, 'type'] = 'TRA'
sheep_532_plink_0110.loc[find_INS, 'type'] = 'INS'
#sheep_532_plink_0110.to_csv(r'F:\svdata\sheep_532_plink_0110.vcf',sep='\t',index=None)
"""
# 从type1中获取Wild goat和Native goat的ID列表
wild_goat_ids = type1[type1[0] == 'Wild goat'][1].tolist()
native_goat_ids = type1[type1[0] == 'Native goat'][1].tolist()
improved_goat_ids = type1[type1[0] == 'Improved goat'][1].tolist()
# 从capra_281_plink中筛选出Wild goat和Native goat个体对应的列
wild_goat_df = capra_281_plink[wild_goat_ids]
native_goat_df = capra_281_plink[native_goat_ids]
improved_goat_df = capra_281_plink[improved_goat_ids]
wild_goat_df[['type','diff']] = capra_281_plink[['type','diff']]
native_goat_df[['type','diff']] = capra_281_plink[['type','diff']]
improved_goat_df[['type','diff']] = capra_281_plink[['type','diff']]
# 检查DataFrame中每行是否包含"0/1"或"1/1"
wild_goat_df_filtered = wild_goat_df[wild_goat_df.apply(lambda row: ('0/1' in row.values) or ('1/1' in row.values), axis=1)]
# 检查DataFrame中每行是否包含"0/1"或"1/1"
native_goat_df_filtered = native_goat_df[native_goat_df.apply(lambda row: ('0/1' in row.values) or ('1/1' in row.values), axis=1)]
improved_goat_df_filtered = improved_goat_df[improved_goat_df.apply(lambda row: ('0/1' in row.values) or ('1/1' in row.values), axis=1)]
# 获取每个DataFrame的索引集合
native_indices = set(native_goat_df_filtered.index)
wild_indices = set(wild_goat_df_filtered.index)
improved_indices = set(improved_goat_df_filtered.index)
# 找出只在native_goat_df_filtered中存在的索引
len(wild_indices - improved_indices - native_indices)
len(native_indices - wild_indices - improved_indices)
len(improved_indices - native_indices -wild_indices)
# 计算三个集合的交集
len(native_indices.intersection(wild_indices, improved_indices))
# 计算两两交集
len(native_indices.intersection(wild_indices))
len(native_indices.intersection(improved_indices))
len(wild_indices.intersection(improved_indices))
"""
len(wild_indices - improved_indices)
Out[1191]: 35816
len(wild_indices - improved_indices - native_indices)
Out[1192]: 31407
len(native_indices - wild_indices - improved_indices)
Out[1193]: 14323
len(improved_indices - native_indices -wild_indices)
Out[1194]: 1539
"""
capra_281_plink_plt = capra_281_plink.query('type!="BND"')
capra_281_plink_plt['diff'] = capra_281_plink_plt['diff'].abs()
capra_281_plink_plt = capra_281_plink_plt.query('50<diff<=1000').sort_values(by=['diff'])
#如果diff<0,则将起点和终点交换 additional file 2 S16
#capra_281_noTRA_st.loc[capra_281_noTRA_st['diff'] < 0, ['POS', 'end']] = capra_281_noTRA_st.loc[capra_281_noTRA_st['diff'] < 0, ['end', 'POS']].values
"____________________________________________________________________________"
"Figure2c绘图_______________________________________________________________________"
#读取0/1格式的绵羊和山羊数据
sheep_532_plink_0110 = pd.read_csv('F:\svdata\sheep_532_plink_0110.vcf', sep='\t')
sheep_532_plink_0110['animal'] = "sheep"
sheep_532_plink_0110 = sheep_532_plink_0110.drop('END_values',axis=1)
capra_281_plink = pd.read_csv(r'F:\svdata\SV_count_result.csv', sep=',')
#打上标签后合并长度数据
"""
x = sheep_anno.iloc[:,:5]
x.columns = ['loc','gene','chr','s','t']
sheep_532 = sheep_532_plink_0110[['#CHROM','POS','end','type']]
sheep_532.columns = ['chr','s','t','type']
x = x.query('loc!="intergenic"')
"""
capra_281_plink['animal'] = "goat"
sheep_diff = sheep_532_plink_0110[['type','diff','animal']]
goat_diff = capra_281_plink[['type','diff','animal']]
all_diff = pd.concat([sheep_diff, goat_diff])
#去NA、取绝对值、过滤到50-1000
all_diff = all_diff.dropna()
all_diff['diff'] = all_diff['diff'].abs()
#50-1000、1000-10000、>10000一共分3个梯度
diff1 = all_diff.query('50<diff<=1000')
diff2 = all_diff.query('1000<diff<=10000')
diff3 = all_diff.query('10000<diff<100000')
diff4 = all_diff.query('100000<diff')
import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.nonparametric.smoothers_lowess import lowess
bin1 = np.arange(50, 1001, 10)
bin2 = np.arange(1000, 10001, 100)
bin3 = np.arange(10000, 100000, 1000)
bin4 = np.arange(100000, 1000000, 10000)
def diff_binplot(all_diff, bins, number):
# 计算diff值的计数，并对计数取对数
counts = all_diff['diff'].value_counts().sort_index()
log_counts = np.log10(counts)
#bins = np.arange(50, 1001, 10)
all_diff['bin'] = pd.cut(all_diff['diff'], bins=bins)
binned_data = all_diff.groupby(['bin', 'type', 'animal'])['diff'].agg(['count']).reset_index()
# 取对数
binned_data['log_count'] = np.log10(binned_data['count'])
# 创建图表
plt.figure(figsize=(14, 8))
# 分别绘制sheep和goat的曲线
for animal in ['sheep', 'goat']:
animal_data = binned_data[binned_data['animal'].str.lower() == animal]
for sv_type in animal_data['type'].unique():
type_data = animal_data[animal_data['type'] == sv_type]
linestyle = '-' if animal == 'sheep' else '--' # sheep为实线，goat为虚线
plt.plot(type_data['bin'].apply(lambda x: x.mid), type_data['log_count'], linestyle=linestyle, label=f'{animal.capitalize()}-{sv_type}')
# 添加图例
plt.legend()
# 添加标题和坐标轴标签
plt.title('Log10 Counts of SV Types by Animal Type')
plt.xlabel('SV Size (10 BP bins)')
plt.ylabel('Log10(Count)')
# 调整图表布局并展示
plt.tight_layout()
plt.savefig(r"F:\svdata\svplot\Figure2cS{}.pdf".format(number))
plt.show()
"____________________________________________________________________________"
"Figure2d绘图_______________________________________________________________________"
def calculate_maf(row):
# 计算每个等位基因的计数
allele_counts = row.str.cat(sep='').count('0'), row.str.cat(sep='').count('1')
# 计算总的有效等位基因数（排除了'./.'）
total_alleles = sum(allele_counts)
# 计算次等位基因频率
if total_alleles == 0: # 防止除以零
return None
maf = min(allele_counts) / total_alleles
return maf
# 应用函数计算每个位点的MAF
maf_sheep = sheep_532_plink_0110.iloc[:, 10:-3].apply(calculate_maf, axis=1)
maf_goat = capra_281_plink.iloc[:, 10:-3].apply(calculate_maf, axis=1)
sheep_532_plink_0110['MAF'] = maf_sheep
capra_281_plink['MAF'] = maf_goat
all_maf = pd.concat([sheep_532_plink_0110[['type','MAF','animal']], capra_281_plink[['type','MAF','animal']]])
# 计算maf值的计数，并对计数取对数
counts = all_maf['MAF'].value_counts().sort_index()
log_counts = np.log10(counts)
bins = np.arange(-0.000001, 0.51, 0.01)
all_maf['bin'] = pd.cut(all_maf['MAF'], bins=bins)
binned_data = all_maf.groupby(['bin', 'type', 'animal'])['MAF'].agg(['count']).reset_index()
# 取对数
binned_data['log_count'] = np.log10(binned_data['count'])
# 创建图表
plt.figure(figsize=(14, 8))
# 分别绘制sheep和goat的曲线
for animal in ['sheep', 'goat']:
animal_data = binned_data[binned_data['animal'].str.lower() == animal]
for sv_type in animal_data['type'].unique():
type_data = animal_data[animal_data['type'] == sv_type]
linestyle = '-' if animal == 'sheep' else '--' # sheep为实线，goat为虚线
plt.plot(type_data['bin'].apply(lambda x: x.mid), type_data['log_count'], linestyle=linestyle, label=f'{animal.capitalize()}-{sv_type}')
# 添加图例
plt.legend()
# 添加标题和坐标轴标签
plt.title('Log10 Counts of SV Types by Animal Type')
plt.xlabel('SV Size (10 BP bins)')
plt.ylabel('Log10(Count)')
# 调整图表布局并展示
plt.tight_layout()
plt.savefig(r"F:\svdata\svplot\Figure2d.pdf")
plt.show()
"____________________________________________________________________________"
"Figure2F绘图_______________________________________________________________________"
data = {
'type': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'B', 'C'],
'BQ2602': ['0/0', '0/1', '1/1', '0/0', '1/1', '0/0', '1/1', '0/1', '1/1', '0/0'],
'BQ2620': ['0/1', '0/0', '0/1', '1/1', '0/0', '1/1', '0/0', '0/1', '1/1', '0/0'],
# ... 其他列
}
# 选取需要分析的列
genotypes = capra_281_plink.iloc[:, 10:-3]
# 初始化一个空的DataFrame来存储结果
type_counts_per_individual = pd.DataFrame(index=capra_281_plink['type'].unique(),columns=genotypes.columns)
# 对于每个个体（即DataFrame的每一列），统计各种type出现的次数
for individual in genotypes.columns:
# 筛选出符合条件的行索引
valid_indices = genotypes[individual].isin(['0/1', '1/1'])
# 根据这些索引，统计type的出现次数
counts = capra_281_plink.loc[valid_indices, 'type'].value_counts()
# 将统计结果添加到结果DataFrame中
type_counts_per_individual[individual] = counts
type_counts_per_individual = type_counts_per_individual.T
name_data = pd.read_csv(r'F:\svdata\Fig2Edata.txt', sep='\t', header=None)
name_data.columns = ['breed', 'type', 'name', 'srr']
#type_counts_per_individual = type_counts_per_individual.query('type != "Unknown" and type != "Hybrid"')
# 展示结果
type_counts_per_individual
type_counts_per_individual['breed'] = name_data['breed'].to_list()
type_counts_per_individual['type'] = name_data['type'].to_list()
merged_data = type_counts_per_individual.groupby(['breed', 'type']).mean().reset_index()