def save_snps(self, filename=None):
""" Save SNPs to file.
Parameters
----------
filename : str
filename for file to save
Returns
-------
str
path to file in output directory if SNPs were saved, else empty str
"""
comment = ("# Source(s): {}\n"
"# Assembly: {}\n"
"# SNPs: {}\n"
"# Chromosomes: {}\n".format(self.source, self.assembly,
self.snp_count,
self.chromosomes_summary))
if filename is None:
filename = (self.get_var_repr(self._source) + "_lineage_" +
self.assembly + ".csv")
return save_df_as_csv(
self._snps,
self._output_dir,
filename,
comment=comment,
header=["chromosome", "position", "genotype"],
)
def _save_discrepant_snps_file(self, df, name, filename):
if filename is None:
filename = self.get_var_repr(self._name) + "_" + name + ".csv"
return save_df_as_csv(
df,
self._output_dir,
filename,
comment="# Source(s): {}\n".format(self.source),
)
def _find_shared_dna_output_helper(
self,
individual1,
individual2,
one_chrom_shared_dna,
two_chrom_shared_dna,
one_chrom_shared_genes,
two_chrom_shared_genes,
):
cytobands = self._resources.get_cytoBand_hg19()
if create_dir(self._output_dir):
plot_chromosomes(
one_chrom_shared_dna,
two_chrom_shared_dna,
cytobands,
os.path.join(
self._output_dir,
"shared_dna_{}_{}.png".format(individual1.get_var_name(),
individual2.get_var_name()),
),
"{} / {} shared DNA".format(individual1.name,
individual2.name),
37,
)
if len(one_chrom_shared_dna) > 0:
file = "shared_dna_one_chrom_{}_{}_GRCh37.csv".format(
individual1.get_var_name(), individual2.get_var_name())
save_df_as_csv(one_chrom_shared_dna,
self._output_dir,
file,
float_format="%.2f")
if len(two_chrom_shared_dna) > 0:
file = "shared_dna_two_chroms_{}_{}_GRCh37.csv".format(
individual1.get_var_name(), individual2.get_var_name())
save_df_as_csv(two_chrom_shared_dna,
self._output_dir,
file,
float_format="%.2f")
if len(one_chrom_shared_genes) > 0:
file = "shared_genes_one_chrom_{}_{}_GRCh37.csv".format(
individual1.get_var_name(), individual2.get_var_name())
save_df_as_csv(one_chrom_shared_genes, self._output_dir, file)
if len(two_chrom_shared_genes) > 0:
file = "shared_genes_two_chroms_{}_{}_GRCh37.csv".format(
individual1.get_var_name(), individual2.get_var_name())
save_df_as_csv(two_chrom_shared_genes, self._output_dir, file)
def find_discordant_snps(self,
individual1,
individual2,
individual3=None,
save_output=False):
""" Find discordant SNPs between two or three individuals.
Parameters
----------
individual1 : Individual
reference individual (child if `individual2` and `individual3` are parents)
individual2 : Individual
comparison individual
individual3 : Individual
other parent if `individual1` is child and `individual2` is a parent
save_output : bool
specifies whether to save output to a CSV file in the output directory
Returns
-------
pandas.DataFrame
discordant SNPs and associated genetic data
References
----------
..[1] David Pike, "Search for Discordant SNPs in Parent-Child
Raw Data Files," David Pike's Utilities,
http://www.math.mun.ca/~dapike/FF23utils/pair-discord.php
..[2] David Pike, "Search for Discordant SNPs when given data
for child and both parents," David Pike's Utilities,
http://www.math.mun.ca/~dapike/FF23utils/trio-discord.php
"""
self._remap_snps_to_GRCh37([individual1, individual2, individual3])
df = individual1.snps
# remove nulls for reference individual
df = df.loc[df["genotype"].notnull()]
# add SNPs shared with `individual2`
df = df.join(individual2.snps["genotype"], rsuffix="2")
genotype1 = "genotype_" + individual1.get_var_name()
genotype2 = "genotype_" + individual2.get_var_name()
if individual3 is None:
df = df.rename(columns={
"genotype": genotype1,
"genotype2": genotype2
})
# find discordant SNPs between reference and comparison individuals
df = df.loc[df[genotype2].notnull()
& ((df[genotype1].str.len() == 1)
& (df[genotype2].str.len() == 1)
& (df[genotype1] != df[genotype2]))
| ((df[genotype1].str.len() == 2)
& (df[genotype2].str.len() == 2)
& (df[genotype1].str[0] != df[genotype2].str[0])
& (df[genotype1].str[0] != df[genotype2].str[1])
& (df[genotype1].str[1] != df[genotype2].str[0])
& (df[genotype1].str[1] != df[genotype2].str[1]))]
if save_output:
save_df_as_csv(
df,
self._output_dir,
"discordant_snps_{}_{}_GRCh37.csv".format(
individual1.get_var_name(),
individual2.get_var_name()),
)
else:
# add SNPs shared with `individual3`
df = df.join(individual3.snps["genotype"], rsuffix="3")
genotype3 = "genotype_" + individual3.get_var_name()
df = df.rename(
columns={
"genotype": genotype1,
"genotype2": genotype2,
"genotype3": genotype3,
})
# find discordant SNPs between child and two parents
df = df.loc[(df[genotype2].notnull()
& ((df[genotype1].str.len() == 1)
& (df[genotype2].str.len() == 1)
& (df[genotype1] != df[genotype2]))
| ((df[genotype1].str.len() == 2)
& (df[genotype2].str.len() == 2)
& (df[genotype1].str[0] != df[genotype2].str[0])
& (df[genotype1].str[0] != df[genotype2].str[1])
& (df[genotype1].str[1] != df[genotype2].str[0])
& (df[genotype1].str[1] != df[genotype2].str[1])))
| (df[genotype3].notnull()
& ((df[genotype1].str.len() == 1)
& (df[genotype3].str.len() == 1)
& (df[genotype1] != df[genotype3]))
| ((df[genotype1].str.len() == 2)
& (df[genotype3].str.len() == 2)
& (df[genotype1].str[0] != df[genotype3].str[0])
& (df[genotype1].str[0] != df[genotype3].str[1])
& (df[genotype1].str[1] != df[genotype3].str[0])
&
(df[genotype1].str[1] != df[genotype3].str[1])))
| (df[genotype2].notnull()
& df[genotype3].notnull()
& (df[genotype2].str.len() == 2)
& (df[genotype2].str[0] == df[genotype2].str[1])
& (df[genotype2] == df[genotype3])
& (df[genotype1] != df[genotype2]))]
if save_output:
save_df_as_csv(
df,
self._output_dir,
"discordant_snps_{}_{}_{}_GRCh37.csv".format(
individual1.get_var_name(),
individual2.get_var_name(),
individual3.get_var_name(),
),
)
return df
def _add_snps(
self,
snps,
discrepant_snp_positions_threshold,
discrepant_genotypes_threshold,
save_output,
):
""" Add SNPs to this ``SNPsCollection``.
Parameters
----------
snps : SNPs
SNPs to add
discrepant_snp_positions_threshold : int
see above
discrepant_genotypes_threshold : int
see above
save_output
see above
Returns
-------
discrepant_positions : pandas.DataFrame
discrepant_genotypes : pandas.DataFrame
"""
discrepant_positions = pd.DataFrame()
discrepant_genotypes = pd.DataFrame()
if snps._snps is None:
return discrepant_positions, discrepant_genotypes
build = snps._build
source = [s.strip() for s in snps._source.split(",")]
if not snps._build_detected:
print("build not detected, assuming build {}".format(snps._build))
if self._build is None:
self._build = build
elif self._build != build:
print(
"build / assembly mismatch between current build of SNPs and SNPs being loaded"
)
# ensure there area always two X alleles
snps = self._double_single_alleles(snps._snps, "X")
if self._snps is None:
self._source.extend(source)
self._snps = snps
else:
common_snps = self._snps.join(snps, how="inner", rsuffix="_added")
discrepant_positions = common_snps.loc[
(common_snps["chrom"] != common_snps["chrom_added"])
| (common_snps["pos"] != common_snps["pos_added"])]
if 0 < len(
discrepant_positions) < discrepant_snp_positions_threshold:
print(
str(len(discrepant_positions)) +
" SNP positions were discrepant; "
"keeping original positions")
if save_output:
self._discrepant_positions_file_count += 1
save_df_as_csv(
discrepant_positions,
self._output_dir,
self.get_var_repr(self._name) +
"_discrepant_positions_" +
str(self._discrepant_positions_file_count) + ".csv",
)
elif len(discrepant_positions
) >= discrepant_snp_positions_threshold:
print(
"too many SNPs differ in position; ensure same genome build is being used"
)
return discrepant_positions, discrepant_genotypes
# remove null genotypes
common_snps = common_snps.loc[
~common_snps["genotype"].isnull()
& ~common_snps["genotype_added"].isnull()]
# discrepant genotypes are where alleles are not equivalent (i.e., alleles are not the
# same and not swapped)
discrepant_genotypes = common_snps.loc[
((common_snps["genotype"].str.len() == 1)
& (common_snps["genotype_added"].str.len() == 1)
& ~(common_snps["genotype"].str[0] ==
common_snps["genotype_added"].str[0]))
| ((common_snps["genotype"].str.len() == 2)
& (common_snps["genotype_added"].str.len() == 2)
& ~((common_snps["genotype"].str[0] ==
common_snps["genotype_added"].str[0])
& (common_snps["genotype"].str[1] ==
common_snps["genotype_added"].str[1]))
& ~((common_snps["genotype"].str[0] ==
common_snps["genotype_added"].str[1])
& (common_snps["genotype"].str[1] ==
common_snps["genotype_added"].str[0])))]
if 0 < len(discrepant_genotypes) < discrepant_genotypes_threshold:
print(
str(len(discrepant_genotypes)) +
" SNP genotypes were discrepant; "
"marking those as null")
if save_output:
self._discrepant_genotypes_file_count += 1
save_df_as_csv(
discrepant_genotypes,
self._output_dir,
self.get_var_repr(self._name) +
"_discrepant_genotypes_" +
str(self._discrepant_genotypes_file_count) + ".csv",
)
elif len(discrepant_genotypes) >= discrepant_genotypes_threshold:
print(
"too many SNPs differ in their genotype; ensure file is for same "
"individual")
return discrepant_positions, discrepant_genotypes
# add new SNPs
self._source.extend(source)
self._snps = self._snps.combine_first(snps)
self._snps.loc[discrepant_genotypes.index, "genotype"] = np.nan
# combine_first converts position to float64, so convert it back to int64
self._snps["pos"] = self._snps["pos"].astype(np.int64)
self.sort_snps()
return discrepant_positions, discrepant_genotypes