diff --git a/src/visu/figure_maker.py b/src/visu/figure_maker.py
index f499afb064edb84397a1115c4141ecbdec94668b..1dc9183cca72990216062fd190f895fe91fd28e7 100644
--- a/src/visu/figure_maker.py
+++ b/src/visu/figure_maker.py
@@ -18,6 +18,7 @@ from tqdm import tqdm
from loguru import logger
import matplotlib as mpl
import matplotlib.font_manager
+from rpy2.robjects import r, pandas2ri
def load_bed(bed: Path, bed_name: str) -> List[List[Union[int, str]]]:
@@ -259,12 +260,12 @@ def create_df_summary(df_cov: pd.DataFrame, figure_type: str, nb_bin: int,
.mean() \
.reset_index()
if figure_type == "metagene":
+ if environment[0] != 0:
+ df_sum['location'] = df_sum['bin'].apply(
+ lambda x: f"before_{region_name}" if x < 0 else
+ f"after_{region_name}" if x >= nb_bin else region_name)
df_sum, condition_col = merge_condition_region_col(df_sum)
return df_sum, condition_col
- if environment[0] != 0:
- df_sum['location'] = df_sum['bin'].apply(
- lambda x: f"before_{region_name}" if x < 0 else
- f"after_{region_name}" if x >= nb_bin else region_name)
df_sum.drop('bin', axis=1, inplace=True)
if environment[0] != 0:
col_merge = ['condition', 'region', 'replicate', 'location']
@@ -295,7 +296,7 @@ def figure_metagene(df_sum: pd.DataFrame, show_replicate: bool,
border_names: List[str], nb_bin: int,
environment: List[int], bed_name: str,
output: Path, norm: Union[int, Path],
- condition_col: str, ylim: Optional[List[float]]) -> None:
+ condition_col: str, ylim: Optional[List[float]]) -> Path:
"""
Create a metagene figure on the region of interest.
@@ -313,6 +314,7 @@ def figure_metagene(df_sum: pd.DataFrame, show_replicate: bool,
each samples
:param condition_col: The name of the condition columns
:param ylim: The range of the y axis
+ :return: The name of the figure
"""
font_files = matplotlib.font_manager.findSystemFonts(fontpaths=None,
fontext='ttf')
@@ -357,6 +359,7 @@ def figure_metagene(df_sum: pd.DataFrame, show_replicate: bool,
g.ax.tick_params(left=True, bottom=True)
g.savefig(output / outfile_title)
g.fig.clf()
+ return output / outfile_title
def figure_barplot(df_sum: pd.DataFrame, show_replicate: bool,
@@ -445,6 +448,92 @@ def bin_normalisation(df: pd.DataFrame, norm: Union[int, Path],
return df
+def compute_diff(df_sum: pd.DataFrame) -> pd.DataFrame:
+ """
+ Compute the average coverage differences between two conditions.
+
+ :param df_sum: A dataframe containing the coverage values between \
+ two conditions
+ :return: The dataframe with a coolumn diff
+
+ >>> di = pd.DataFrame({'bin': [-25] * 6,
+ ... 'condition': ['Ctrl', 'Ctrl', 'Ctrl', 'siDDX', 'siDDX', 'siDDX'],
+ ... 'replicate': ['R1', 'R2', 'R3', 'R1', 'R2', 'R3'],
+ ... 'coverage': [0.1,
+ ... 0.2,
+ ... 0.3,
+ ... 0.15,
+ ... 0.25,
+ ... 0.35],
+ ... 'location': ['before_ddx_down'] * 6})
+ >>> compute_diff(di)
+ bin location Ctrl siDDX diff
+ 0 -25 before_ddx_down 0.2 0.25 0.05
+ """
+ cond = df_sum["condition"].unique()
+ df_sum = df_sum.groupby(["bin", "location", "condition"]).mean()
+ if len(cond) != 2:
+ raise IndexError("Only two different conditions must be given to "
+ "perform stats")
+ df = df_sum.pivot_table(index=["bin", "location"],
+ columns="condition",
+ values="coverage").reset_index()
+ df.columns.name = None
+ df.groupby(["bin", "location"])
+ df["diff"] = df[cond[1]] - df[cond[0]]
+ return df
+
+
+def get_shapiro_an_lm_pvalue(df: pd.DataFrame, location: str) -> pd.Series:
+ """
+ Return a series containing the shapiro and the t-test p-value of \
+ the difference in coverage between 2 conditions at a particular location
+
+ :param df: A dataframe containing the difference in coverage \
+ between 2 conditions at a particular location
+ :param location: The region of interest
+ :return: a series containing the shapiro and the t-test p-value of \
+ the difference in coverage between 2 conditions at a particular location
+ """
+ df2 = df[df["location"] == location].copy()
+ df2.to_csv(f"{location}.txt", sep="\t", index=False)
+ pandas2ri.activate()
+ stat_s = r("""
+ function(data){
+ mod <- lm(diff ~ 1, data=data)
+ s <- summary(mod)
+ stat <- s$coefficients[1, "Pr(>|t|)"]
+ shapiro <- shapiro.test(data$diff)$p.value
+ return(c(shapiro, stat))
+ }
+ """)
+ res = stat_s(df2)
+ return pd.Series({"shapiro_p-value": res[0],
+ "t-test-p-value": res[1],
+ "location": location})
+
+
+def compute_stat_dataframe(df_sum: pd.DataFrame,
+ region_name: str) -> pd.DataFrame:
+ """
+ Create a dataframe containing the statistical analysis of \
+ the difference of coverage between two conditions.
+
+ :param df_sum: A dataframe containing the coverage values between \
+ two conditions
+ :param region_name: The name of the region of interest
+ :return: The dataframe containing statistical analysis
+ """
+ df_diff = compute_diff(df_sum)
+ if 'location' not in df_diff.columns:
+ df_diff["location"] = [region_name] * df_diff.shape[0]
+ list_series = [
+ get_shapiro_an_lm_pvalue(df_diff, region)
+ for region in df_diff["location"].unique()
+ ]
+ return pd.DataFrame(list_series)
+
+
def create_figure(design: Path, bw_folder: Path, region_beds: List[Path],
bed_names: List[str], nb_bin: int = 100,
figure_type: str = 'metagene',
@@ -452,7 +541,8 @@ def create_figure(design: Path, bw_folder: Path, region_beds: List[Path],
show_replicate: bool = True, environment: List[int] = (0, 0),
border_names: List[str] = ('', ''),
output: Path = Path('.'),
- ylim: List[float] = (None, None)) -> None:
+ ylim: List[float] = (None, None), stat: bool = False
+ ) -> None:
"""
Create A metagene or a barplot figure from bigwig file on regions defined \
in the bed file provided with 'region_bed' parameter.
@@ -479,6 +569,8 @@ def create_figure(design: Path, bw_folder: Path, region_beds: List[Path],
:param border_names: The name of the borders
:param output: Folder where the results will be created
:param ylim: The range of y-axis
+ :param stat: A boolean indicating whether or not to perform \
+ statistical analysis
"""
if len(region_beds) != len(bed_names):
raise IndexError("Parameter region_beds and bed_names should "
@@ -508,9 +600,14 @@ def create_figure(design: Path, bw_folder: Path, region_beds: List[Path],
environment, region_kind,
ordered_condition, bed_names)
if figure_type == "metagene":
- figure_metagene(df_sum, show_replicate, border_names, nb_bin,
- environment, region_kind, output, norm, cond_col,
- ylim)
+ outfile = figure_metagene(df_sum, show_replicate, border_names, nb_bin,
+ environment, region_kind, output, norm,
+ cond_col, ylim)
+ if stat:
+ df_stat = compute_stat_dataframe(df_sum, region_kind)
+ df_stat.to_csv(outfile.parent / f"{outfile.stem}.txt", sep="\t",
+ index=False)
+
else:
if 'location' in df_sum.columns:
for cur_region in df_sum['location'].unique():