Notebook for Phospho-proteoimcs peptide processing¶
Phospho proteomics checking¶
Do the same processing steps as for the protein data (as in merging the names). But then additionally have a look at the normalised vs unnormalised versions.
In [19]:
import math
import pandas as pd
import numpy as np
base_dir = '../data/'
data_dir = f'{base_dir}raw_downloads/CPTAC/'
output_dir = f'{base_dir}sircle/F1_DE_input_TvN/'
fig_dir = '../figures/'
supp_dir = f'{base_dir}raw_downloads/supps/'
gene_name = 'hgnc_symbol'
save_fig = False
# Read in the processed proteomics data
prot_df = pd.read_csv(f'{output_dir}prot_data_sircle_ccRCC.csv')
prot_df
Out[19]:
| external_gene_name | ensembl_gene_id | chromosome_name | start_position | end_position | strand | entrezgene_id | external_synonym | hgnc_symbol | original_gene_id | ... | Protein_Normal_C3N.01646_CPT0088500001 | Protein_Tumor_C3N.01646_CPT0088480003 | Protein_Tumor_C3N.01648_CPT0088550004 | Protein_Normal_C3N.01648_CPT0088570001 | Protein_Tumor_C3N.01649_CPT0088630003 | Protein_Normal_C3N.01649_CPT0088640003 | Protein_Normal_C3N.01651_CPT0088710001 | Protein_Tumor_C3N.01651_CPT0088690003 | Protein_Normal_C3N.01808_CPT0089480003 | Protein_Tumor_C3N.01808_CPT0089460004 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | IFITM2 | ENSG00000185201 | 11 | 303655.0 | 309397.0 | 1.0 | 10581.0 | 1-8D | IFITM2 | IFITM2 | ... | 17.242808 | 17.341401 | 17.276320 | 17.214368 | 17.418534 | 17.475385 | 17.512245 | 17.269921 | 16.677501 | 17.253800 |
| 1 | IFITM3 | ENSG00000142089 | 11 | 319676.0 | 329475.0 | -1.0 | 10410.0 | 1-8U | IFITM3 | IFITM3 | ... | 20.580569 | 21.422762 | 21.138186 | 20.541807 | 21.600754 | 20.612185 | 20.882283 | 21.092480 | 20.601086 | 21.344087 |
| 2 | PRDX6 | ENSG00000117592 | 1 | 173477330.0 | 173488815.0 | 1.0 | 9588.0 | 1-Cys | PRDX6 | PRDX6 | ... | 26.012760 | 25.911260 | 25.779640 | 26.081670 | 25.650650 | 26.092640 | 26.081900 | 25.990450 | 26.254320 | 26.170450 |
| 3 | ALDH1L1 | ENSG00000144908 | 3 | 126103562.0 | 126197994.0 | -1.0 | 10840.0 | 10-fTHF | ALDH1L1 | ALDH1L1 | ... | 25.500336 | 24.123117 | 24.539930 | 25.884126 | 23.804339 | 25.431972 | 26.135252 | 23.710141 | 26.240426 | 24.783373 |
| 4 | KNOP1 | ENSG00000103550 | 16 | 19701937.0 | 19718235.0 | -1.0 | 400506.0 | 101F10.1 | KNOP1 | KNOP1 | ... | 17.308228 | 17.813241 | 17.935471 | 17.406868 | 17.576764 | 17.278729 | 17.122735 | 17.993586 | 17.262391 | 18.181467 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11350 | ZNF827 | ENSG00000151612 | 4 | 145757627.0 | 145938823.0 | -1.0 | 152485.0 | NaN | ZNF827 | ZNF827 | ... | 16.549910 | 16.189710 | 16.238320 | 16.444590 | 16.278300 | 16.460540 | 16.249870 | 16.181790 | 16.276270 | 16.363320 |
| 11351 | ZNF865 | ENSG00000261221 | 19 | 55605647.0 | 55617269.0 | 1.0 | 100507290.0 | NaN | ZNF865 | ZNF865 | ... | 18.515980 | 18.271680 | 18.364140 | 18.489010 | 18.436500 | 18.481750 | 18.980530 | 18.138080 | 18.500360 | 18.502200 |
| 11352 | ZNF888 | ENSG00000213793 | 19 | 52904415.0 | 52923470.0 | -1.0 | 388559.0 | NaN | ZNF888 | ZNF888 | ... | 14.635330 | 14.775910 | 14.810620 | 14.618430 | 14.785150 | 14.594220 | 14.555330 | 14.751380 | 14.574110 | 14.761570 |
| 11353 | ZNRD1 | ENSG00000066379 | NaN | NaN | NaN | NaN | 30834.0 | NaN | POLR1H | ZNRD1 | ... | 18.251270 | 18.513290 | 18.592980 | 18.300450 | 18.510970 | 18.473130 | 18.274910 | 18.668260 | 18.395860 | 18.408350 |
| 11354 | ZYX | ENSG00000159840 | 7 | 143381295.0 | 143391111.0 | 1.0 | 7791.0 | NaN | ZYX | ZYX | ... | 24.508170 | 24.694090 | 24.994780 | 24.643200 | 25.021640 | 24.309250 | 24.547970 | 24.929770 | 24.418060 | 24.611800 |
11355 rows × 194 columns
Read in the phospho peptide data¶
In [20]:
phospho_prot_df = pd.read_csv(f'{data_dir}6_CPTAC3_CCRCC_Phospho_abundance_phosphopeptide_protNorm=2_CB_imputed_1211.tsv', sep='\t')
phospho_prot_df
Out[20]:
| Index | Gene | Peptide | ReferenceIntensity | CPT0079430001 | CPT0023360001 | CPT0023350003 | CPT0079410003 | CPT0087040003 | CPT0077310003 | ... | CPT0012080003 | CPT0021240003 | CPT0009020003 | CPT0017450001 | CPT0009060003 | CPT0012900004 | CPT0017410003 | CPT0009080003 | CPT0012920003 | CPT0009000003 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NP_000009.1_52_61 | ACADVL | SDSHPSDALTR | 21.417959 | 21.761176 | 21.718694 | 21.309462 | 21.142333 | 21.032659 | 20.897775 | ... | 21.282943 | 20.967924 | 21.571221 | 21.578335 | 21.114262 | 21.403658 | 20.951904 | 21.757505 | 21.361459 | 21.439662 |
| 1 | NP_000010.1_195_200 | ACAT1 | IHMGSCAENTAK | 22.327380 | 22.530936 | 22.405770 | 22.119932 | 21.780781 | 21.876907 | 21.648481 | ... | 22.184800 | 21.137826 | 22.348200 | 22.247994 | 21.916136 | 22.810727 | 21.317200 | 22.968150 | 22.463576 | 22.487031 |
| 2 | NP_000011.2_155_161 | ACVRL1 | GLHSELGESSLILK | 22.592515 | 22.128972 | 22.403675 | 22.589706 | 22.887970 | 23.350894 | 22.875935 | ... | 22.688156 | 22.804701 | 22.287426 | 22.635473 | 22.444509 | 22.950870 | 22.725534 | 22.325998 | 22.372267 | 22.885955 |
| 3 | NP_000017.1_9_13 | ADSL | AAGGDHGSPDSYR | 23.747509 | 23.904101 | 23.726398 | 23.582186 | 24.033232 | 23.863450 | 24.031663 | ... | 23.882674 | 24.081865 | 23.235960 | 23.859549 | 23.945124 | 24.093499 | 23.399682 | 23.605715 | 23.145256 | 24.093286 |
| 4 | NP_000017.1_9_19 | ADSL | AAGGDHGSPDSYRSPLASR | 22.380822 | 22.507791 | 22.295678 | 22.420174 | 22.536810 | 22.368111 | 22.474957 | ... | 22.367719 | 22.753779 | 22.089366 | 22.524299 | 22.442299 | 22.261267 | 22.195088 | 22.370109 | 22.000181 | 22.381184 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20971 | NP_998754.1_845_860 | RAPH1 | QQSFCAKPPPSPLSPVPSVVK | 25.319744 | 25.852331 | 26.276820 | 24.691365 | 24.862791 | 24.688595 | 25.046265 | ... | 25.044381 | 25.306795 | 25.296651 | 25.290435 | 25.286176 | 24.812464 | 25.130971 | 24.879890 | 25.767267 | 25.129330 |
| 20972 | NP_998754.1_867_894 | RAPH1 | QIASQFPPPPTPPAMESQPLKPVPANVAPQSPPAVK | 25.265568 | 25.686558 | 25.924294 | 24.652317 | 24.926187 | 24.636498 | 25.176439 | ... | 25.072751 | 25.388786 | 25.264870 | 25.240537 | 25.193915 | 25.098189 | 24.925202 | 25.262182 | 25.481954 | 25.370581 |
| 20973 | NP_998754.1_983_985 | RAPH1 | SSSGAEHPEPK | 20.411702 | 20.881818 | 20.950593 | 20.284714 | 20.362307 | 20.313694 | 20.162719 | ... | 20.283746 | 20.412700 | 20.779037 | 20.849822 | 20.150783 | 20.229844 | 20.404295 | 20.608771 | 20.891060 | 20.102807 |
| 20974 | NP_998771.3_469_478 | SLC16A12 | TQLQFIAKESDPK | 23.242735 | 24.643193 | 24.790644 | 21.862674 | 22.495721 | 21.703528 | 21.893988 | ... | 20.806118 | 20.867537 | 23.765850 | 24.378933 | 22.044204 | 23.302226 | 21.382754 | 23.084380 | 24.153484 | 21.031542 |
| 20975 | NP_998839.1_206_216 | TPM2 | SLMASEEEYSTK | 22.359128 | 22.634533 | 22.470242 | 21.867496 | 22.197343 | 21.940093 | 23.312715 | ... | 22.089107 | 23.602416 | 22.686637 | 22.298291 | 21.983122 | 22.821433 | 22.110968 | 22.348188 | 22.631553 | 20.794591 |
20976 rows × 211 columns
It was unclear whether the file was the correct one so check a value from the paper¶
In [21]:
# Check one of the data from Supp6 from the paper:
# Kinse_substrate_before ranking
#Kinase Sub.Accession Sub.Gene Sub.Seq Sites CPT0001540009-CPT0001550001 CPT0001220008-CPT0001230001 CPT0001340003-CPT0001350001 CPT0001500009-CPT0001510001 CPT0001260009-CPT0001270001 CPT0000870016-CPT0000890001 CPT0001180009-CPT0001190001 CPT0000780007-CPT0000790001 CPT0000640003-CPT0000660001 CPT0066470004-CPT0066430001 CPT0002350011-CPT0002370001 CPT0006440003-CPT0006530001 CPT0010110003-CPT0010120001 CPT0006630003-CPT0006730001 CPT0006900003-CPT0006950001 CPT0010160003-CPT0010170001 CPT0023350003-CPT0023360001 CPT0007320003-CPT0007470001 CPT0019130003-CPT0019160001 CPT0079270003-CPT0079300001 CPT0025050003-CPT0025060001 CPT0026410003-CPT0026420001 CPT0065810003-CPT0065820001 CPT0063320003-CPT0063330001 CPT0064370003-CPT0064400001 CPT0086360003-CPT0086370003 CPT0007860003-CPT0007870001 CPT0023690003-CPT0023710001 CPT0079480003-CPT0079510001 CPT0086950003-CPT0086970003 CPT0079410003-CPT0079430001 CPT0063630003-CPT0063640001 CPT0086820003-CPT0086830003 CPT0087040003-CPT0087050003 CPT0092160003-CPT0092190003 CPT0086870003-CPT0086890003 CPT0092290003-CPT0092310003 CPT0092730003-CPT0092740003 CPT0092790003-CPT0092800003 CPT0081600003-CPT0081620001 CPT0084560003-CPT0084590001 CPT0066480003-CPT0066520001 CPT0019990003-CPT0020020001 CPT0020120003-CPT0020130001 CPT0014160003-CPT0014130001 CPT0014450004-CPT0014470001 CPT0014370004-CPT0014350001 CPT0025580004-CPT0025610001 CPT0009000003-CPT0009020003 CPT0009060003-CPT0009080003 CPT0012280003-CPT0012290003 CPT0012370003-CPT0013790003 CPT0017410003-CPT0017450001 CPT0012550003-CPT0012570003 CPT0012670003-CPT0012640003 CPT0012900004-CPT0012920003 CPT0078510003-CPT0078530001 CPT0069000003-CPT0069010001 CPT0069160003-CPT0069190001 CPT0081990003-CPT0082010001 CPT0025880003-CPT0025920001 CPT0089020003-CPT0089040001 CPT0078830003-CPT0078840001 CPT0024670003-CPT0024680001 CPT0065430003-CPT0065450001 CPT0078930003-CPT0078940001 CPT0078990003-CPT0079000001 CPT0088900003-CPT0088920001 CPT0075130003-CPT0075170001 CPT0075560003-CPT0075570001 CPT0076330003-CPT0076350001 CPT0077490003-CPT0077500001 CPT0088760003-CPT0088780001 CPT0077110003-CPT0077140001 CPT0077310003-CPT0077320001 CPT0088480003-CPT0088500001 CPT0088550004-CPT0088570001 CPT0088630003-CPT0088640003 CPT0088690003-CPT0088710001 CPT0089460004-CPT0089480003
#EGFR NP_002645.3 PKM ITLDNAYMEK Y148 0.78 0.66 1.12 0.93 1.56 2.23 1.3 1.41 2.34 1.11 0.76 2.69 0.87 1.66 0.81 1.8 0.41 0.71 0.56 1.36 0.7 1.26 1.97 1.44 0.9 0.65 1.33 0.96 1.6 0.43 0.58 0.96 0.43 0.79 0.71 0.54 0.34 0.79 1.08 0.59 0.6 0.94 1.45 0.69 1.11 0.83 0.31 1.08 1.18 -0.44 0.73 1.27 0.7 0.81 -0.12 1.54 0.67 0.48 0.49 2.05 0.79 1.49 1.4 -0.079 0.2 1.32 1.46 1.49 1.62 1.07 0.37 1.15 1.09 1.17 1.45 0.51 0.8 -0.047 1 0.82
pkm = phospho_prot_df[phospho_prot_df['Peptide'] == 'ITLDNAYMEK']
pkm['CPT0001220008'] - pkm['CPT0001230001']
Out[21]:
11272 0.657937 dtype: float64
Add in sample info to the protein dataset¶
While we have the aliqout IDs, we want to add in other info, such as patient demographics we may use for performing DE analysis experiments
In [22]:
# In the protein dataframe they use the Aliquot ID rather than the case ID
# We want to match this to the case and if it was tumour or normal sample
# Get the protein aliqot IDs and we want to match these with patient info
p_Aliquot_ID = [c for c in phospho_prot_df.columns if 'CPT' in c]
# Get the patient info
sample_type = clin_prot_df['Group'].values
cond_ids = []
cond_names = []
case_ids = []
cases = clin_prot_df['ParticipantID'].values
# Iterate though the different aliqots
for cp in p_Aliquot_ID:
# Iterate through the clinical samples to find a match
for i, c in enumerate(clin_prot_df['Aliquot ID'].values):
if c == cp:
cond_names.append(sample_type[i])
case_ids.append(cases[i])
if sample_type[i] == 'Tumor':
cond_ids.append(1)
else:
cond_ids.append(0)
break
# Make a sample Dataframe
prot_sample_data = pd.DataFrame()
prot_sample_data['AliquotID'] = p_Aliquot_ID
prot_sample_data['CondName'] = cond_names
prot_sample_data['CondId'] = cond_ids
prot_sample_data['CaseId'] = case_ids
prot_sample_data['SafeCases'] = [c.replace('-', '.') for c in case_ids]
prot_sample_data['FullLabel'] = [f'{cond_names[i]}_{case_ids[i].replace("-", ".")}_{a}' for i, a in enumerate(p_Aliquot_ID)]
prot_sample_data
Out[22]:
| AliquotID | CondName | CondId | CaseId | SafeCases | FullLabel | |
|---|---|---|---|---|---|---|
| 0 | CPT0079430001 | Normal | 0 | C3L-01287 | C3L.01287 | Normal_C3L.01287_CPT0079430001 |
| 1 | CPT0023360001 | Normal | 0 | C3L-00561 | C3L.00561 | Normal_C3L.00561_CPT0023360001 |
| 2 | CPT0023350003 | Tumor | 1 | C3L-00561 | C3L.00561 | Tumor_C3L.00561_CPT0023350003 |
| 3 | CPT0079410003 | Tumor | 1 | C3L-01287 | C3L.01287 | Tumor_C3L.01287_CPT0079410003 |
| 4 | CPT0087040003 | Tumor | 1 | C3L-01603 | C3L.01603 | Tumor_C3L.01603_CPT0087040003 |
| ... | ... | ... | ... | ... | ... | ... |
| 189 | CPT0012900004 | Tumor | 1 | C3N-00494 | C3N.00494 | Tumor_C3N.00494_CPT0012900004 |
| 190 | CPT0017410003 | Tumor | 1 | C3N-00390 | C3N.00390 | Tumor_C3N.00390_CPT0017410003 |
| 191 | CPT0009080003 | Normal | 0 | C3N-00312 | C3N.00312 | Normal_C3N.00312_CPT0009080003 |
| 192 | CPT0012920003 | Normal | 0 | C3N-00494 | C3N.00494 | Normal_C3N.00494_CPT0012920003 |
| 193 | CPT0009000003 | Tumor | 1 | C3N-00310 | C3N.00310 | Tumor_C3N.00310_CPT0009000003 |
194 rows × 6 columns
In [23]:
# Rename the columns to have the aliqiot ID in it
column_map = {}
for i, a in enumerate(p_Aliquot_ID):
column_map[a] = f'{cond_names[i]}_{case_ids[i].replace("-", ".")}_phospho-{a}'
phospho_prot_df = phospho_prot_df.rename(columns=column_map)
# Save the phospho protein data
phospho_prot_df.to_csv(f'{output_dir}phospho_protein_df.csv', index=False)
Combine protein and phosphoprotein data¶
In [24]:
phospho_prot_df.set_index('Gene', inplace=True)
prot_df.set_index('external_gene_name', inplace=True)
joined_df = phospho_prot_df.join(prot_df, how='left')
print("Check one case:")
print([c for c in joined_df if 'C3L.01287' in c])
joined_df
Check one case: ['Normal_C3L.01287_phospho-CPT0079430001', 'Tumor_C3L.01287_phospho-CPT0079410003', 'Protein_Normal_C3L.01287_CPT0079430001', 'Protein_Tumor_C3L.01287_CPT0079410003']
Out[24]:
| Index | Peptide | ReferenceIntensity | Normal_C3L.01287_phospho-CPT0079430001 | Normal_C3L.00561_phospho-CPT0023360001 | Tumor_C3L.00561_phospho-CPT0023350003 | Tumor_C3L.01287_phospho-CPT0079410003 | Tumor_C3L.01603_phospho-CPT0087040003 | Tumor_C3N.01524_phospho-CPT0077310003 | Normal_C3N.01524_phospho-CPT0077320001 | ... | Protein_Normal_C3N.01646_CPT0088500001 | Protein_Tumor_C3N.01646_CPT0088480003 | Protein_Tumor_C3N.01648_CPT0088550004 | Protein_Normal_C3N.01648_CPT0088570001 | Protein_Tumor_C3N.01649_CPT0088630003 | Protein_Normal_C3N.01649_CPT0088640003 | Protein_Normal_C3N.01651_CPT0088710001 | Protein_Tumor_C3N.01651_CPT0088690003 | Protein_Normal_C3N.01808_CPT0089480003 | Protein_Tumor_C3N.01808_CPT0089460004 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A1CF | NP_001185747.1_476_491 | ITIPALASQNPAIHPFTPPK | 20.453126 | 20.449142 | 20.885241 | 20.185938 | 20.525450 | 20.451730 | 20.519747 | 19.648479 | ... | 21.703028 | 20.833115 | 20.833245 | 21.499587 | 20.710021 | 21.460528 | 21.844864 | 21.809387 | 21.994794 | 21.718692 |
| AAAS | NP_001166937.1_452_462 | IAHIPLYFVNAQFPRFSPVLGR | 23.108998 | 22.057504 | 22.340435 | 23.228205 | 23.469294 | 23.094490 | 23.286889 | 22.829507 | ... | 21.234175 | 21.458443 | 21.207059 | 21.241072 | 21.283221 | 21.201960 | 21.213362 | 21.356652 | 21.222759 | 21.407700 |
| AAAS | NP_001166937.1_462_462 | FSPVLGR | 25.806906 | 25.731148 | 25.769810 | 25.948758 | 26.427664 | 25.975726 | 25.913938 | 25.432394 | ... | 21.234175 | 21.458443 | 21.207059 | 21.241072 | 21.283221 | 21.201960 | 21.213362 | 21.356652 | 21.222759 | 21.407700 |
| AAED1 | NP_714542.1_12_26 | QVSGAAALVPAPSGPDSGQPLAAAVAELPVLDAR | 21.927467 | 21.395114 | 20.843158 | 22.025712 | 22.196850 | 21.675030 | 21.987204 | 21.363302 | ... | 16.738174 | 17.076733 | 17.084533 | 16.749497 | 17.002491 | 16.815960 | 16.536287 | 16.838677 | 16.824587 | 17.208924 |
| AAGAB | NP_001258814.1_201_202 | AFWMAIGGDRDEIEGLSSDEEH | 23.474450 | 23.933374 | 23.925157 | 23.473935 | 23.411468 | 23.036560 | 23.047834 | 23.528933 | ... | 21.210292 | 21.175936 | 21.278753 | 21.258959 | 20.928010 | 21.108272 | 21.105292 | 21.011580 | 21.137876 | 21.188299 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| ZZEF1 | NP_055928.3_2036_2064 | DPSCQTQISDSPADASPPTGLPDAEDSEVSSQKPIEEK | 20.409363 | 20.677790 | 20.727328 | 20.411733 | 20.225733 | 20.227110 | 20.326844 | 20.535013 | ... | 22.236100 | 22.483780 | 22.483120 | 22.423540 | 22.396560 | 22.470660 | 22.380040 | 22.171580 | 22.429060 | 22.200750 |
| ZZEF1 | NP_055928.3_2443_2444 | EINALAEHGDLELDERGDREEEVERPVSSPGDPEQK | 24.083359 | 24.407275 | 24.054662 | 24.262107 | 24.016887 | 24.105712 | 24.372652 | 23.739350 | ... | 22.236100 | 22.483780 | 22.483120 | 22.423540 | 22.396560 | 22.470660 | 22.380040 | 22.171580 | 22.429060 | 22.200750 |
| ZZZ3 | NP_056349.1_109_113 | RQTEPVSPVLK | 23.378570 | 23.622857 | 23.770220 | 23.312709 | 23.028803 | 22.709243 | 23.785112 | 23.259046 | ... | 18.346630 | 18.279890 | 18.343030 | 18.432600 | 18.166120 | 18.356000 | 18.418770 | 18.115130 | 18.226970 | 18.723950 |
| ZZZ3 | NP_056349.1_388_393 | AAPTRGSPTK | 22.873652 | 23.330099 | 22.726409 | 22.234700 | 23.072836 | 23.772500 | 23.056753 | 23.253603 | ... | 18.346630 | 18.279890 | 18.343030 | 18.432600 | 18.166120 | 18.356000 | 18.418770 | 18.115130 | 18.226970 | 18.723950 |
| ZZZ3 | NP_056349.1_396_434 | NSSPYRENGQFEENNLSPNETNATVSDNVSQSPTNPGEISQNEK | 21.007027 | 20.990917 | 21.347922 | 20.716818 | 20.875610 | 21.111279 | 21.020385 | 21.416004 | ... | 18.346630 | 18.279890 | 18.343030 | 18.432600 | 18.166120 | 18.356000 | 18.418770 | 18.115130 | 18.226970 | 18.723950 |
20976 rows × 403 columns
Do protein level normalisation as well as not to confirm holds in both cases¶
In [26]:
norm_phospho_prot_df = phospho_prot_df[['Index', 'Peptide']].copy()
norm_phospho_prot_df['Gene'] = phospho_prot_df.index.values
for c in phospho_prot_df.columns:
if 'CPT' in c:
try:
norm_phospho_prot_df[c] = phospho_prot_df[c].values - joined_df['Protein_' + c.replace('phospho-', '')].values
except:
print(c) # These are the patients that were ommitted
Tumor_C3L.00359_phospho-CPT0002270011 Tumor_C3N.01180_phospho-CPT0088970003 Tumor_C3N.01175_phospho-CPT0078660003 Normal_C3N.01175_phospho-CPT0078670001 Tumor_C3N.00832_phospho-CPT0078800003 Tumor_C3N.00313_phospho-CPT0011410003 Normal_C3N.00492_phospho-CPT0012770003 Tumor_C3N.00492_phospho-CPT0079180003 Normal_C3N.00435_phospho-CPT0018250001 Tumor_C3N.00435_phospho-CPT0017850003
Check the genes of interest for us¶
Namely, PGK1, PKM and GAPDH.
In [27]:
from sciviso import Boxplot
import matplotlib.pyplot as plt
for gene in ['PGK1', 'PKM', 'GAPDH']:
df = phospho_prot_df[phospho_prot_df.index == gene]
for i, p in enumerate(df['Peptide'].values):
labels = []
values = []
peptide = []
for c in df.columns:
if 'Normal' in c:
labels.append('Normal')
values.append(df[c].values[i])
peptide.append(p)
elif 'Tumor' in c:
labels.append('Tumor')
values.append(df[c].values[i])
peptide.append(p)
p_df = pd.DataFrame()
p_df['Sample Type'] = labels
p_df['values'] = values
p_df['peptide'] = peptide
fig_size = (4, 4)
vis_opts = {"figsize": fig_size, "title_font_size": 12, "axis_font_size": 10,
'palette': ['navy', 'indianred']}
b = Boxplot(p_df, x='Sample Type', y="values", add_dots=True,
add_stats=True, title=f'{gene} {p}',
xlabel="Sample", ylabel=f"Protein", config=vis_opts)
b.plot()
plt.show()
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.720e-25 U_stat=5.930e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.874e-23 U_stat=7.690e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.696e-08 U_stat=2.465e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.653e-11 U_stat=2.037e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.338e-15 U_stat=7.673e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.055e-12 U_stat=1.970e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=5.287e-32 U_stat=5.700e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.100e-31 U_stat=8.100e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.014e-22 U_stat=8.180e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=5.948e-30 U_stat=2.140e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.550e-32 U_stat=4.400e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.005e-27 U_stat=3.910e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=6.752e-32 U_stat=6.500e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.777e-08 U_stat=2.437e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.821e-29 U_stat=2.520e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.360e-32 U_stat=7.200e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.145e-31 U_stat=1.030e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.995e-02 U_stat=5.299e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.124e-03 U_stat=3.429e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=5.583e-05 U_stat=3.058e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.290e-02 U_stat=3.656e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.028e-20 U_stat=1.031e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.768e-03 U_stat=3.588e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.206e-01 U_stat=4.018e+03
In [28]:
norm_phospho_prot_df
Out[28]:
| Index | Peptide | Gene | Normal_C3L.01287_phospho-CPT0079430001 | Normal_C3L.00561_phospho-CPT0023360001 | Tumor_C3L.00561_phospho-CPT0023350003 | Tumor_C3L.01287_phospho-CPT0079410003 | Tumor_C3L.01603_phospho-CPT0087040003 | Tumor_C3N.01524_phospho-CPT0077310003 | Normal_C3N.01524_phospho-CPT0077320001 | ... | Tumor_C3N.00314_phospho-CPT0012080003 | Tumor_C3N.00380_phospho-CPT0021240003 | Normal_C3N.00310_phospho-CPT0009020003 | Normal_C3N.00390_phospho-CPT0017450001 | Tumor_C3N.00312_phospho-CPT0009060003 | Tumor_C3N.00494_phospho-CPT0012900004 | Tumor_C3N.00390_phospho-CPT0017410003 | Normal_C3N.00312_phospho-CPT0009080003 | Normal_C3N.00494_phospho-CPT0012920003 | Tumor_C3N.00310_phospho-CPT0009000003 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gene | |||||||||||||||||||||
| ACADVL | NP_000009.1_52_61 | SDSHPSDALTR | ACADVL | -0.042266 | -0.131667 | 0.043138 | -0.447795 | -0.344958 | -0.409540 | 0.164360 | ... | -0.131711 | 0.404696 | 0.207896 | 0.004350 | -0.537785 | -0.690874 | -0.194706 | -0.085518 | -0.042698 | 0.223915 |
| ACAT1 | NP_000010.1_195_200 | IHMGSCAENTAK | ACAT1 | 1.233629 | 1.209744 | 0.828509 | 0.373549 | 0.707343 | 0.502501 | 0.238606 | ... | 0.772789 | -0.205708 | 1.316843 | 1.121766 | 0.497685 | 1.410282 | 0.048251 | 1.696554 | 1.258788 | 1.266005 |
| ACVRL1 | NP_000011.2_155_161 | GLHSELGESSLILK | ACVRL1 | 0.831665 | 1.207649 | 1.298282 | 1.480738 | 2.181330 | 1.729955 | 1.077052 | ... | 1.276145 | 1.461167 | 1.256068 | 1.509245 | 1.026057 | 1.550425 | 1.456584 | 1.054402 | 1.167480 | 1.664929 |
| ADSL | NP_000017.1_9_13 | AAGGDHGSPDSYR | ADSL | 6.391308 | 6.146699 | 5.840022 | 6.346376 | 5.875581 | 5.907245 | 5.583326 | ... | 6.891230 | 6.702324 | 6.538873 | 7.181548 | 7.106101 | 6.661961 | 6.512960 | 6.661198 | 6.425755 | 7.387552 |
| ADSL | NP_000017.1_9_19 | AAGGDHGSPDSYRSPLASR | ADSL | 1.364222 | 1.088321 | 1.185954 | 1.278038 | 1.331526 | 1.464882 | 0.774132 | ... | 1.431294 | 1.738788 | 0.815063 | 1.331259 | 1.290995 | 1.331310 | 0.967761 | 1.008281 | 0.741806 | 1.440590 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| RAPH1 | NP_998754.1_845_860 | QQSFCAKPPPSPLSPVPSVVK | RAPH1 | 3.389751 | 3.910150 | 2.280005 | 2.531311 | 2.317195 | 2.577875 | 2.842327 | ... | 2.658301 | 2.841295 | 2.896871 | 2.810895 | 2.894826 | 2.555884 | 2.924261 | 2.418320 | 3.478257 | 2.699450 |
| RAPH1 | NP_998754.1_867_894 | QIASQFPPPPTPPAMESQPLKPVPANVAPQSPPAVK | RAPH1 | 3.223978 | 3.557624 | 2.240957 | 2.594707 | 2.265098 | 2.708049 | 3.143363 | ... | 2.686671 | 2.923286 | 2.865090 | 2.760997 | 2.802565 | 2.841609 | 2.718492 | 2.800612 | 3.192944 | 2.940701 |
| RAPH1 | NP_998754.1_983_985 | SSSGAEHPEPK | RAPH1 | 2.561048 | 2.610353 | 1.989954 | 2.037647 | 1.903894 | 1.668209 | 2.494925 | ... | 1.902476 | 2.056080 | 2.526277 | 2.635532 | 1.687583 | 1.544164 | 2.059875 | 2.440891 | 2.632990 | 1.692447 |
| SLC16A12 | NP_998771.3_469_478 | TQLQFIAKESDPK | SLC16A12 | 6.322423 | 6.450404 | 3.567914 | 4.171061 | 3.293728 | 3.399478 | 4.718576 | ... | 2.424848 | 2.510917 | 5.513090 | 6.164643 | 3.581004 | 4.616546 | 3.038334 | 4.916500 | 5.895414 | 2.621182 |
| TPM2 | NP_998839.1_206_216 | SLMASEEEYSTK | TPM2 | 4.313763 | 4.130002 | 3.572736 | 3.872683 | 3.530293 | 4.818205 | 4.071176 | ... | 3.707837 | 5.245796 | 4.433877 | 4.084001 | 3.519922 | 4.135753 | 3.766548 | 4.180308 | 4.373483 | 2.384231 |
20976 rows × 187 columns
Do the same for the protein level normalisation¶
In [29]:
for gene in ['PGK1', 'PKM', 'GAPDH']:
df = norm_phospho_prot_df[norm_phospho_prot_df.index == gene]
for i, p in enumerate(df['Peptide'].values):
labels = []
values = []
peptide = []
for c in df.columns:
if 'Normal' in c:
labels.append('Normal')
values.append(df[c].values[i])
peptide.append(p)
elif 'Tumor' in c:
labels.append('Tumor')
values.append(df[c].values[i])
peptide.append(p)
p_df = pd.DataFrame()
p_df['Sample Type'] = labels
p_df['values'] = values
p_df['peptide'] = peptide
fig_size = (4, 4)
vis_opts = {"figsize": fig_size, "title_font_size": 12, "axis_font_size": 10,
'palette': ['lightblue', 'pink']}
b = Boxplot(p_df, x='Sample Type', y="values", add_dots=True,
add_stats=True, title=f'{gene} {p}',
xlabel="Sample", ylabel=f"Protein", config=vis_opts)
b.plot()
plt.show()
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.119e-29 U_stat=1.720e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.777e-27 U_stat=2.760e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.422e-22 U_stat=7.300e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.309e-27 U_stat=2.660e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.224e-13 U_stat=6.831e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.262e-13 U_stat=1.559e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.178e-29 U_stat=1.150e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.383e-30 U_stat=8.400e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.937e-18 U_stat=1.029e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=9.651e-31 U_stat=3.700e+01
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.571e-30 U_stat=1.050e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.623e-25 U_stat=4.840e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.563e-27 U_stat=3.070e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.501e-06 U_stat=2.526e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.396e-25 U_stat=4.400e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.289e-28 U_stat=1.910e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.045e-29 U_stat=1.540e+02
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.046e-01 U_stat=3.589e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.413e-08 U_stat=2.208e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=9.366e-05 U_stat=2.770e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.031e-02 U_stat=3.251e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.419e-18 U_stat=1.038e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=6.611e-03 U_stat=3.197e+03
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 Normal v.s. Tumor: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.120e-01 U_stat=3.601e+03
