def stat_cor(input={"correlation_R":"", "cor_pdf": "", "venn": "", },
output={"json": ""}, param=None):
# TODO: merge this into stat_venn
""" ReplicateQC aims to describe the similarity of replicate experiment. Venn diagram and correlation plot will be used."""
correlation_result_r_code = open(input["correlation_R"]).read()
signal_list = re.findall(r"[pc]\d+ <- (.*)$", correlation_result_r_code, re.MULTILINE)
rep_count = len(signal_list)
correlation_value_list = []
# F**K!! WTF!!
# for i in range(rep_count):
# for j in range(i + 1, rep_count):
# tpfile_name = tempfile.mktemp()
# open(tpfile_name,"w").write("cor(%s, %s)" % (signal_list[i], signal_list[j]))
# cmd = 'R --slave --vanilla < %s ' %tpfile_name
# print("Running %s"%cmd)
# cmd_result = subprocess.check_output(cmd, shell=True)
# correlation_value_list.append(float(re.findall("[1] (.*)", cmd_result)[0]))
# min_correlation = min(correlation_value_list)
result_dict = {"stat": {}, "input": input, "output": output, "param": param}
# result_dict["stat"]["judge"] = "Pass" if min_correlation >= 0.6 else "Fail"
result_dict["stat"]["cutoff"] = 0.6
# result_dict["stat"]["min_cor"] = min_correlation
json_dump(result_dict)
def stat_macs2(input={"macs2_peaks_xls": "", "db": "", "template": ""},
output={"R": "", "json": "", "pdf": ""},
param={"id": ""}):
json_dict = {"stat": {}, "input": input, "output": output, "param": param}
json_dict["stat"] = _peaks_parse(input["macs2_peaks_xls"])
json_dict["stat"]["cutoff"] = {"uni_loc": 5000000, "high_conf_peaks": 1000}
json_dict["stat"]["judge"] = {"uni_loc": "Pass" if json_dict["stat"]["treat_unic"] > 5000000 else "Fail",
"high_conf_peaks": "Pass" if json_dict["stat"]["peaksge10"] >= 1000 else "Fail"}
name = [param["id"]]
db = sqlite3.connect(input["db"]).cursor()
db.execute("select peak_fc_10 from peak_calling")
historyData = [math.log(i[0] + 0.001, 10) for i in db.fetchall() if i[0] > 0]
high_confident_peaks_r = JinjaTemplateCommand(name="highpeaksQC",
template=input["template"],
param={'historic_data': historyData,
'current_data': [math.log(json_dict["stat"]["peaksge10"] + 0.01, 10)], #
'ids': name,
'cutoff': 3,
'main': 'High confidence peaks distribution',
'xlab': 'log(Number of Peaks fold greater than 10)',
'ylab': 'fn(log(Number of Peaks fold greater than 10))',
"pdf": output["pdf"],
"render_dump": output["R"]})
template_dump(high_confident_peaks_r)
r_exec(high_confident_peaks_r)
json_dump(json_dict)
def stat_fastqc(input={"db": "", "fastqc_summaries": [], "template": ""},
output={"R": "", "json": "", "pdf": ""},
param={"ids": [], "id": ""}):
json_dict = {"stat": {}, "input": input, "output": output, "param": param}
stat = json_dict["stat"]
quality_medians = []
for a_summary, a_id in zip(input["fastqc_summaries"], param["ids"]):
parsed = _python_fastqc_parse(input=a_summary)
stat[a_id] = {}
stat[a_id]["median"] = parsed["median"]
stat[a_id]["cutoff"] = 25
stat[a_id]['judge'] = "Pass" if parsed["median"] > 25 else "Fail"
stat[a_id]["sequence_length"] = parsed["sequence_length"]
quality_medians.append(parsed["median"])
# The table of fastqc_summary that will be used for rendering
# Col 1: sample ID
# Col 2: sequence length
# Col 3: median of sequence quality
qc_db = sqlite3.connect(input["db"]).cursor()
qc_db.execute("SELECT median_quality FROM fastqc_info")
history_data = [float(i[0]) for i in qc_db.fetchall()]
fastqc_dist_r = JinjaTemplateCommand(
template=input["template"],
param={'historic_data': history_data,
'current_data': quality_medians,
'ids': [underline_to_space(i) for i in param["ids"]],
'cutoff': 25,
'main': 'Sequence Quality Score Cumulative Percentage',
'xlab': 'sequence quality score',
'ylab': 'fn(sequence quality score)',
"need_smooth_curve": True,
"pdf": output["pdf"],
"render_dump": output["R"]})
template_dump(fastqc_dist_r)
r_exec(fastqc_dist_r)
json_dump(json_dict)
def stat_venn(input={"venn": ""}, output={"json",""}, param=None):
result_dict = {"stat": {}, "input": input, "output": output, "param": param}
json_dump(result_dict)
