gene_md_tables/MEF2B_Hotspots.md
... ...
@@ -0,0 +1,24 @@
1
+# MEF2B_Hotspots
2
+
3
+| Hugo_Symbol | Chromosome | Coordinate | DLBCL | FL | BL | HGVS |
4
+| ------ | ------ | ------ | ------ | ------ | ------ | ------ |
5
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83V |
6
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83A |
7
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83G |
8
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83E |
9
+| MEF2B | 19 | 19260064,19260063 | 9 | 1 | 0 | p.E77K |
10
+| MEF2B | 19 | 19260064,19260063 | 9 | 1 | 0 | p.E77G |
11
+| MEF2B | nan | nan | 4 | 0 | 0 | p.X86_splice |
12
+| MEF2B | 19 | 19260066 | 2 | 2 | 0 | p.H76R |
13
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81K |
14
+| MEF2B | 19 | 19260066 | 2 | 2 | 0 | p.H76P |
15
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81Y |
16
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81_T82del |
17
+| MEF2B | 19 | 19260055 | 3 | 0 | 0 | p.T80A |
18
+| MEF2B | 19 | 19260055 | 3 | 0 | 0 | p.T80P |
19
+| MEF2B | 19 | 19258640 | 1 | 1 | 0 | p.T87M |
20
+| MEF2B | 19 | 19260061 | 2 | 0 | 0 | p.S78G |
21
+| MEF2B | 19 | 19260061 | 2 | 0 | 0 | p.S78R |
22
+| MEF2B | 19 | 19260042 | 1 | 0 | 0 | p.I84T |
23
+| MEF2B | 19 | 19258638 | 1 | 0 | 0 | p.L88M |
24
+| MEF2B | 19 | 19260049 | 1 | 0 | 0 | p.T82A |
scripts/manuela_hotspots/Test_Script
... ...
@@ -1,4 +1,4 @@
1
-import pandas as pd
1
+import pandas as pd
2 2
import os
3 3
4 4
# Config
... ...
@@ -34,7 +34,7 @@ all_md_content += "\n---\n\n"
34 34
for gene, group in df.groupby("Hugo_Symbol"):
35 35
# md table header
36 36
md_table = "| " + " | ".join(group.columns) + " |\n"
37
- md_table += "| " + " | ".join(["---"] * len(group.columns)) + " |\n"
37
+ md_table += "| " + " | ".join(["------"] * len(group.columns)) + " |\n"
38 38
for _, row in group.iterrows():
39 39
md_table += "| " + " | ".join(map(str, row.values)) + " |\n"
40 40
... ...
@@ -52,7 +52,7 @@ all_md_content += "\n---\n\n"
52 52
# add each gene's section to combined file
53 53
for gene, group in df.groupby("Hugo_Symbol"):
54 54
md_table = "| " + " | ".join(group.columns) + " |\n"
55
- md_table += "| " + " | ".join(["---"] * len(group.columns)) + " |\n"
55
+ md_table += "| " + " | ".join(["------"] * len(group.columns)) + " |\n"
56 56
for _, row in group.iterrows():
57 57
md_table += "| " + " | ".join(map(str, row.values)) + " |\n"
58 58
all_md_content += f"## {gene}\n\n{md_table}\n\n"
... ...
@@ -63,5 +63,5 @@ all_path = os.path.join(output_dir, "ALL_GENES.md")
63 63
with open(all_path, "w", encoding="utf-8") as f:
64 64
f.write(all_md_content)
65 65
66
-print(f" Markdown files and ALL_GENES.md created in: {output_dir}/")
66
+
67 67
tables/hotspots_MEF2B.md
... ...
@@ -0,0 +1,24 @@
1
+# MEF2B_Hotspots
2
+
3
+| Hugo_Symbol | Chromosome | Coordinate | DLBCL | FL | BL | HGVS |
4
+| ------ | ------ | ------ | ------ | ------ | ------ | ------ |
5
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83V |
6
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83A |
7
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83G |
8
+| MEF2B | 19 | 19260044,19260045 | 43 | 35 | 0 | p.D83E |
9
+| MEF2B | 19 | 19260064,19260063 | 9 | 1 | 0 | p.E77K |
10
+| MEF2B | 19 | 19260064,19260063 | 9 | 1 | 0 | p.E77G |
11
+| MEF2B | nan | nan | 4 | 0 | 0 | p.X86_splice |
12
+| MEF2B | 19 | 19260066 | 2 | 2 | 0 | p.H76R |
13
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81K |
14
+| MEF2B | 19 | 19260066 | 2 | 2 | 0 | p.H76P |
15
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81Y |
16
+| MEF2B | 19 | 19260050,19260052 | 4 | 0 | 0 | p.N81_T82del |
17
+| MEF2B | 19 | 19260055 | 3 | 0 | 0 | p.T80A |
18
+| MEF2B | 19 | 19260055 | 3 | 0 | 0 | p.T80P |
19
+| MEF2B | 19 | 19258640 | 1 | 1 | 0 | p.T87M |
20
+| MEF2B | 19 | 19260061 | 2 | 0 | 0 | p.S78G |
21
+| MEF2B | 19 | 19260061 | 2 | 0 | 0 | p.S78R |
22
+| MEF2B | 19 | 19260042 | 1 | 0 | 0 | p.I84T |
23
+| MEF2B | 19 | 19258638 | 1 | 0 | 0 | p.L88M |
24
+| MEF2B | 19 | 19260049 | 1 | 0 | 0 | p.T82A |