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Use dropdown sample selection in some sample charts #8

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Use dropdown sample selection in some sample charts #8

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57 changes: 46 additions & 11 deletions multiqc/modules/bases2fastq/plot_samples.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,18 +96,20 @@ def sequence_content_plot(sample_data, group_lookup_dict, project_lookup_dict, c
"""Create the epic HTML for the FastQC sequence content heatmap"""

# Prep the data
data = dict()
all_data = dict()
plot_content = [all_data]

r1r2_split = 0
for s_name in sorted(sample_data.keys()):
paired_end = True if len(sample_data[s_name]["Reads"]) > 1 else False
for base in "ACTG":
base_s_name = "__".join([s_name, base])
data[base_s_name] = {}
all_data[base_s_name] = {}
R1 = sample_data[s_name]["Reads"][0]["Cycles"]
r1r2_split = max(r1r2_split, len(R1))

for s_name in sorted(sample_data.keys()):
paired_end = True if len(sample_data[s_name]["Reads"]) > 1 else False

R1 = sample_data[s_name]["Reads"][0]["Cycles"]
for cycle in range(len(R1)):
base_no = cycle + 1
Expand All @@ -116,7 +118,7 @@ def sequence_content_plot(sample_data, group_lookup_dict, project_lookup_dict, c

for base in "ACTG":
base_s_name = "__".join([s_name, base])
data[base_s_name].update(
all_data[base_s_name].update(
{base_no: float(R1[cycle]["BaseComposition"][base] / float(tot)) * 100.0 if tot > 0 else None}
)

Expand All @@ -128,15 +130,32 @@ def sequence_content_plot(sample_data, group_lookup_dict, project_lookup_dict, c

for base in "ACTG":
base_s_name = "__".join([s_name, base])
data[base_s_name].update(
all_data[base_s_name].update(
{base_no: float(R2[cycle]["BaseComposition"][base] / float(tot)) * 100.0 if tot > 0 else None}
)

plot_content = data
default_label = {
"name": "All",
"xlab": "Cycle",
"ylab": "Percentage of total reads",
}
data_labels = [
default_label,
]
for s_name in sorted(sample_data.keys()):
sample_plot_data = dict()
for base in "ACTG":
base_s_name = "__".join([s_name, base])
sample_plot_data[base_s_name] = all_data[base_s_name]
plot_content.append(sample_plot_data)
data_labels.append({
"name": s_name,
"xlab": default_label["xlab"],
"ylab": default_label["ylab"],
})

pconfig = {
"xlab": "cycle",
"ylab": "Percentage",
"data_labels": data_labels,
"x_lines": [{"color": "#FF0000", "width": 2, "value": r1r2_split, "dashStyle": "dash"}],
"colors": color_dict,
"ymin": 0,
Expand Down Expand Up @@ -236,6 +255,17 @@ def plot_per_read_gc_hist(sample_data, group_lookup_dict, project_lookup_dict, s
Plot GC Histogram per Sample
"""
gc_hist_dict = dict()
plot_content = [
gc_hist_dict,
]
default_label = {
"name": "All",
"xlab": "Percentage of total reads",
"ylab": "Percentage of reads that are GC",
}
data_labels = [
default_label,
]
for s_name in sample_data.keys():
R1_gc_counts = sample_data[s_name]["Reads"][0]["PerReadGCCountHistogram"]
R2_gc_counts = [0] * len(R1_gc_counts)
Expand All @@ -249,11 +279,16 @@ def plot_per_read_gc_hist(sample_data, group_lookup_dict, project_lookup_dict, s
gc_hist_dict[s_name].update({gc / RLen * 100: R1R2_gc_counts[gc] / totalReads * 100})

# perReadQualityHistogram
plot_content = gc_hist_dict
for s_name in gc_hist_dict.keys():
plot_content.append({s_name: gc_hist_dict[s_name]})
data_labels.append({
"name": s_name,
"xlab": default_label["xlab"],
"ylab": default_label["ylab"],
})

pconfig = {
"xlab": "% GC",
"ylab": "Percentage",
"data_labels": data_labels,
"colors": sample_color,
"id": "gc_hist",
"title": "bases2fastq: Per Sample GC Content Histogram",
Expand Down
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