Genetic correlation loading and plotting
Heatmap: Genetic correlation matrix
Available since v3.4.15
Simple plot
Options:
ldscrg
:DataFrame
, results from ldsc-rg. 4 columns are required,p1
,p2
,p
,rg
.p1
:string
, column name for trait1, defaul:p1
p2
:string
, column name for trait2 defaul:p2
rg
:string
, column name for rg defaul:rg
p
:string
, column name for p defaul:p
sig_levels
:list
, default:[0.05]
panno
:boolean
, default:True
corrections
:list
,non
no correction,fdr
FDR,bon
bonferroni , default:["non","fdr","bom"]
panno_texts
:list
, text to annotate significant correlations, match the number ofcorrections
times the number ofsig_levels
, default:["*","**","***"]
sort_key
:function
, sort the columns , default:None
equal_aspect
:`, defaul:
True`fontsize
:`, defaul:
10`save
:string
orboolean
, defaul:None
save_args
:dict
, defaul:None
full_cell
:tuple
, threshold for full cell, default:("fdr",0.05)
yticklabel_args
:dict
, default:{"fontsize":10}
xticklabel_args
:dict
, default:{"rotation":45,"horizontalalignment":"left", "verticalalignment":"bottom","fontsize":10}
colorbar_args
:dict
, default:{"shrink":0.82}
cmap
:cmap
, default:matplotlib.cm.get_cmap('RdBu')
Customized plot
# or load from tabular files
ldsc = pd.read_csv("toy_data/input_rg.txt",sep="\t")
ldsc
#prepare the final dataset
trait = pd.read_csv("toy_data/trait_list.txt",sep="\t")
trait["order"] = range(len(trait))
order = trait["TRAIT"].values
trait_set1 = trait.loc[trait["order"]>=59,"TRAIT"].values
trait_set2 = trait.loc[trait["order"]<59,"TRAIT"].values
ldsc = ldsc.loc[((ldsc["p1"].isin(trait_set1))&(ldsc["p2"].isin(trait_set2))) | ((ldsc["p1"].isin(trait_set2))&(ldsc["p2"].isin(trait_set1))),:]
map_dic={order[i]:i+1 for i in range(len(order))}
key=lambda x:x.map(map_dic)
# plot
df = gl.plot_rg( ldsc,
sig_levels=[0.05],
corrections =["non"],
p="q",
p1="p2",
p2="p1",
full_cell=("non",0.05),
panno_texts=["*"],
fig_args={"figsize":(15,15),"dpi":300},
colorbar_args={"shrink":0.4},
panno_args={"size":12,"c":"black"},fdr_method="i",
fontsize=8,
sort_key=key
)
sample data source: https://github.com/mkanai/ldsc-corrplot-rg , Kanai, M., Akiyama, M., Takahashi, A., Matoba, N., Momozawa, Y., Ikeda, M., ... & Kamatani, Y. (2018). Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nature genetics, 50(3), 390-400.