Spaces:
Sleeping
Sleeping
add header and take2 in selecting columns
Browse files
app.py
CHANGED
@@ -4,6 +4,24 @@ import numpy as np
|
|
4 |
import pandas as pd
|
5 |
import altair as alt
|
6 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
7 |
|
8 |
top_coexp_hits = datasets.load_dataset(
|
9 |
path = "maomlab/CryptoCEN",
|
@@ -17,10 +35,10 @@ gene_id = st.text_input(
|
|
17 |
help = "CNAG Gene ID e.g. CNAG_04365")
|
18 |
|
19 |
top_coexp_hits = top_coexp_hits[
|
20 |
-
|
21 |
-
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
|
26 |
st.table(top_coexp_hits)
|
|
|
4 |
import pandas as pd
|
5 |
import altair as alt
|
6 |
|
7 |
+
st.markdown("""
|
8 |
+
# CryptoCEN Top50 co-expressed partners
|
9 |
+
|
10 |
+
**CryptoCEN** is a co-expression network for *Cryptococcus neoformans* built on 1,524 RNA-seq runs across 34 studies.
|
11 |
+
A pair of genes are said to be co-expressed when their expression is correlated across different conditions and
|
12 |
+
is often a marker for genes to be involved in similar processes.
|
13 |
+
|
14 |
+
To Cite:
|
15 |
+
MJ O'Meara, JR Rapala, CB Nichols, C Alexandre, B Billmyre, JL Steenwyk, A Alspaugh,
|
16 |
+
TR O'Meara CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals
|
17 |
+
novel proteins involved in DNA damage repair
|
18 |
+
* Code available at https://github.com/maomlab/CalCEN/tree/master/vignettes/CryptoCEN
|
19 |
+
* Full network and dataset: https://huggingface.co/datasets/maomlab/CryptoCEN
|
20 |
+
|
21 |
+
## Look up top-coexpressed partners:
|
22 |
+
Put in the ``CNAG_#######`` gene_id for a gene and expand the table to get the top 50 co-expressed genes.
|
23 |
+
``coexp_score`` ranges between ``[0-1]``, where ``1`` is the best and greater than ``0.85`` can be considered significant.
|
24 |
+
""")
|
25 |
|
26 |
top_coexp_hits = datasets.load_dataset(
|
27 |
path = "maomlab/CryptoCEN",
|
|
|
35 |
help = "CNAG Gene ID e.g. CNAG_04365")
|
36 |
|
37 |
top_coexp_hits = top_coexp_hits[
|
38 |
+
top_coexp_hits.gene_id_1 == gene_id,]
|
39 |
+
top_coexp_hits = top_coexp_hits[[
|
40 |
+
'gene_id_1', 'gene_symbol_1', 'description_1',
|
41 |
+
'gene_id_2', 'gene_symbol_2', 'description_2',
|
42 |
+
'coexp_score', 'blastp_EValue']]
|
43 |
|
44 |
st.table(top_coexp_hits)
|