Correlation heatmap¶
In [1]:
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import gwaslab as gl
import pandas as pd
import gwaslab as gl
import pandas as pd
Load sample data¶
Sample data source:
- 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.
In [2]:
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ldsc = pd.read_csv("toy_data/input_rg.txt",sep="\t")
ldsc
ldsc = pd.read_csv("toy_data/input_rg.txt",sep="\t")
ldsc
Out[2]:
| p1_category | p1 | p2_category | p2 | rg | se | z | p | q | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Anthropometric | Height | Anthropometric | BMI | -0.0587 | 0.0240 | -2.4421 | 0.014602 | 0.136798 |
| 1 | Anthropometric | Height | Metabolic | TC | -0.0778 | 0.0344 | -2.2634 | 0.023611 | 0.188695 |
| 2 | Anthropometric | Height | Metabolic | HDL-C | -0.0045 | 0.0364 | -0.1230 | 0.902080 | 0.971590 |
| 3 | Anthropometric | Height | Metabolic | LDL-C | -0.1245 | 0.0426 | -2.9228 | 0.003469 | 0.050126 |
| 4 | Anthropometric | Height | Metabolic | TG | -0.0426 | 0.0309 | -1.3792 | 0.167820 | 0.523652 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3911 | Other | Pollinosis | Tumor | PrCa | 0.3250 | 0.1433 | 2.2683 | 0.023300 | 0.187944 |
| 3912 | Other | Pollinosis | Tumor | UF | 0.2163 | 0.1756 | 1.2315 | 0.218100 | 0.596616 |
| 3913 | Other | Pollinosis | Other | Urolithiasis | 0.0664 | 0.1794 | 0.3701 | 0.711300 | 0.903018 |
| 3914 | Other | Urolithiasis | Allergic disease | AD | -0.0821 | 0.1592 | -0.5159 | 0.605890 | 0.863730 |
| 3915 | Other | Urolithiasis | Tumor | UF | 0.1980 | 0.1292 | 1.5324 | 0.125410 | 0.456418 |
3916 rows × 9 columns
Full heatmap¶
In [3]:
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df = gl.plot_rg( ldsc,
p="q",
p1="p2",
p2="p1",
sig_levels=[0.05],
corrections =["non"],
full_cell=("non",0.05),
panno_texts=["*"],
panno_args={"size":12,"c":"black"},
fig_args={"figsize":(15,15),"dpi":300},
colorbar_args={"shrink":0.4},
fontsize=8
)
df = gl.plot_rg( ldsc,
p="q",
p1="p2",
p2="p1",
sig_levels=[0.05],
corrections =["non"],
full_cell=("non",0.05),
panno_texts=["*"],
panno_args={"size":12,"c":"black"},
fig_args={"figsize":(15,15),"dpi":300},
colorbar_args={"shrink":0.4},
fontsize=8
)
Sun Feb 4 17:51:33 2024 Start to create ldsc genetic correlation heatmap... Sun Feb 4 17:51:33 2024 Raw dataset records: 3916 Sun Feb 4 17:51:33 2024 -Raw dataset non-NA records: 3916 Sun Feb 4 17:51:33 2024 Filling diagnal line and duplicated pair for plotting... Sun Feb 4 17:51:33 2024 -Diagnal records: 89
/home/yunye/anaconda3/lib/python3.9/site-packages/gwaslab/viz_plot_rg_heatmap.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat([df,df_fill_reverse,df_fill_dia,df_fill_na],ignore_index=True).sort_values(by=p).drop_duplicates(subset=[p1,p2])
Sun Feb 4 17:51:33 2024 Valid unique trait pairs: 3916 Sun Feb 4 17:51:33 2024 -Valid unique trait1: 88 Sun Feb 4 17:51:33 2024 -Valid unique trait2: 88 Sun Feb 4 17:51:33 2024 -Significant correlations with P < 0.05: 270 Sun Feb 4 17:51:33 2024 -Significant correlations after Bonferroni correction: 81 Sun Feb 4 17:51:33 2024 -Significant correlations with FDR <0.05: 127 Sun Feb 4 17:51:33 2024 Plotting heatmap... Sun Feb 4 17:51:34 2024 Full cell : non-corrected P == 0.05 Sun Feb 4 17:51:41 2024 P value annotation text : Sun Feb 4 17:51:41 2024 -* : non-corrected P < 0.05 Sun Feb 4 17:51:41 2024 Start to save figure... Sun Feb 4 17:51:41 2024 -Skip saving figure! Sun Feb 4 17:51:41 2024 Finished saving figure... Sun Feb 4 17:51:41 2024 Finished creating ldsc genetic correlation heatmap!
filter and order the data¶
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# Order the data
trait = pd.read_csv("toy_data/trait_list.txt",sep="\t")
trait["order"] = range(len(trait))
trait
# These steps are just used to order the traits
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))),:]
# key for sort
map_dic={order[i]:i+1 for i in range(len(order))}
key=lambda x:x.map(map_dic)
# Order the data
trait = pd.read_csv("toy_data/trait_list.txt",sep="\t")
trait["order"] = range(len(trait))
trait
# These steps are just used to order the traits
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))),:]
# key for sort
map_dic={order[i]:i+1 for i in range(len(order))}
key=lambda x:x.map(map_dic)
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print(map_dic)
print(map_dic)
{'Height': 1, 'BMI': 2, 'TC': 3, 'HDL-C': 4, 'LDL-C': 5, 'TG': 6, 'BS': 7, 'HbA1c': 8, 'TP': 9, 'Alb': 10, 'NAP': 11, 'A/G': 12, 'BUN': 13, 'sCr': 14, 'eGFR': 15, 'UA': 16, 'Na': 17, 'K': 18, 'Cl': 19, 'Ca': 20, 'P': 21, 'TBil': 22, 'ZTT': 23, 'AST': 24, 'ALT': 25, 'ALP': 26, 'GGT': 27, 'APTT': 28, 'PT': 29, 'Fbg': 30, 'CK': 31, 'LDH': 32, 'CRP': 33, 'WBC': 34, 'Neutro': 35, 'Eosino': 36, 'Baso': 37, 'Mono': 38, 'Lym': 39, 'RBC': 40, 'Hb': 41, 'Ht': 42, 'MCV': 43, 'MCH': 44, 'MCHC': 45, 'Plt': 46, 'SBP': 47, 'DBP': 48, 'MAP': 49, 'PP': 50, 'IVS': 51, 'PW': 52, 'LVDd': 53, 'LVDs': 54, 'LVM': 55, 'LVMI': 56, 'RWT': 57, 'FS': 58, 'EF': 59, 'T2D': 60, 'IS': 61, 'CeAn': 62, 'MI': 63, 'PAD': 64, 'AF': 65, 'Asthma': 66, 'AD': 67, 'GD': 68, 'RA': 69, 'CHB': 70, 'CHC': 71, 'Anemia': 72, 'BD': 73, 'SCZ': 74, 'AIS': 75, 'Osteoporosis': 76, 'LuCa': 77, 'GaCa': 78, 'EsCa': 79, 'CoCa': 80, 'PrCa': 81, 'BrCa': 82, 'EnCa': 83, 'UF': 84, 'Glaucoma': 85, 'COPD': 86, 'Epilepsy': 87, 'Pollinosis': 88, 'Urolithiasis': 89}
Replicate heatmap in paper¶
In [6]:
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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"},
fontsize=8,
sort_key=key
)
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"},
fontsize=8,
sort_key=key
)
Sun Feb 4 17:51:43 2024 Start to create ldsc genetic correlation heatmap... Sun Feb 4 17:51:43 2024 Raw dataset records: 1770 Sun Feb 4 17:51:43 2024 -Raw dataset non-NA records: 1770 Sun Feb 4 17:51:43 2024 Filling diagnal line and duplicated pair for plotting... Sun Feb 4 17:51:43 2024 Valid unique trait pairs: 1770 Sun Feb 4 17:51:43 2024 -Valid unique trait1: 59 Sun Feb 4 17:51:43 2024 -Valid unique trait2: 30 Sun Feb 4 17:51:43 2024 -Significant correlations with P < 0.05: 68 Sun Feb 4 17:51:43 2024 -Significant correlations after Bonferroni correction: 13 Sun Feb 4 17:51:43 2024 -Significant correlations with FDR <0.05: 20 Sun Feb 4 17:51:43 2024 Plotting heatmap...
/home/yunye/anaconda3/lib/python3.9/site-packages/gwaslab/viz_plot_rg_heatmap.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat([df,df_fill_reverse,df_fill_dia,df_fill_na],ignore_index=True).sort_values(by=p).drop_duplicates(subset=[p1,p2])
Sun Feb 4 17:51:44 2024 Full cell : non-corrected P == 0.05 Sun Feb 4 17:51:45 2024 P value annotation text : Sun Feb 4 17:51:45 2024 -* : non-corrected P < 0.05 Sun Feb 4 17:51:45 2024 Start to save figure... Sun Feb 4 17:51:45 2024 -Skip saving figure! Sun Feb 4 17:51:45 2024 Finished saving figure... Sun Feb 4 17:51:45 2024 Finished creating ldsc genetic correlation heatmap!
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