Analiza ekspresije gena - Radna verzija¶
In [125]:
import GEOparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import stringdb
In [4]:
gse = GEOparse.get_GEO(geo="GSE33267", destdir="./")
04-Mar-2021 09:28:43 DEBUG utils - Directory ./ already exists. Skipping. 04-Mar-2021 09:28:43 INFO GEOparse - File already exist: using local version. 04-Mar-2021 09:28:43 INFO GEOparse - Parsing ./GSE33267_family.soft.gz: 04-Mar-2021 09:28:43 DEBUG GEOparse - DATABASE: GeoMiame 04-Mar-2021 09:28:43 DEBUG GEOparse - SERIES: GSE33267 04-Mar-2021 09:28:43 DEBUG GEOparse - PLATFORM: GPL4133 04-Mar-2021 09:28:44 WARNING GEOTypes - Detected duplicated columns in d GPL4133. Correcting. 04-Mar-2021 09:28:44 DEBUG GEOparse - SAMPLE: GSM823177 04-Mar-2021 09:28:44 DEBUG GEOparse - SAMPLE: GSM823178 04-Mar-2021 09:28:44 DEBUG GEOparse - SAMPLE: GSM823179 04-Mar-2021 09:28:44 DEBUG GEOparse - SAMPLE: GSM823180 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823181 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823182 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823183 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823184 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823185 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823186 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823187 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823188 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823189 04-Mar-2021 09:28:45 DEBUG GEOparse - SAMPLE: GSM823190 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823191 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823192 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823193 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823194 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823195 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823196 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823197 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823198 04-Mar-2021 09:28:46 DEBUG GEOparse - SAMPLE: GSM823199 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823200 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823201 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823202 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823203 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823204 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823205 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823206 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823207 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823208 04-Mar-2021 09:28:47 DEBUG GEOparse - SAMPLE: GSM823209 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823210 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823211 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823212 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823213 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823214 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823215 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823216 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823217 04-Mar-2021 09:28:48 DEBUG GEOparse - SAMPLE: GSM823218 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823219 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823220 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823221 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823222 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823223 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823224 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823225 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823226 04-Mar-2021 09:28:49 DEBUG GEOparse - SAMPLE: GSM823227 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823228 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823229 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823230 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823231 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823232 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823233 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823234 04-Mar-2021 09:28:50 DEBUG GEOparse - SAMPLE: GSM823235 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823236 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823237 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823238 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823239 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823240 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823241 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823242 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823243 04-Mar-2021 09:28:51 DEBUG GEOparse - SAMPLE: GSM823244 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823245 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823246 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823247 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823248 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823249 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823250 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823251 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823252 04-Mar-2021 09:28:52 DEBUG GEOparse - SAMPLE: GSM823253 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823254 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823255 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823256 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823257 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823258 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823259 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823260 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823261 04-Mar-2021 09:28:53 DEBUG GEOparse - SAMPLE: GSM823262 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823263 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823264 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823265 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823266 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823267 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823268 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823269 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823270 04-Mar-2021 09:28:54 DEBUG GEOparse - SAMPLE: GSM823271 04-Mar-2021 09:28:55 DEBUG GEOparse - SAMPLE: GSM823272 04-Mar-2021 09:28:55 DEBUG GEOparse - SAMPLE: GSM823273 04-Mar-2021 09:28:55 DEBUG GEOparse - SAMPLE: GSM823274 04-Mar-2021 09:28:55 DEBUG GEOparse - SAMPLE: GSM823275
In [7]:
dir(gse)
Out[7]:
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__metaclass__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_get_metadata_as_string', '_get_object_as_soft', '_phenotype_data', 'database', 'download_SRA', 'download_supplementary_files', 'geotype', 'get_accession', 'get_metadata_attribute', 'get_type', 'gpls', 'gsms', 'merge_and_average', 'metadata', 'name', 'phenotype_data', 'pivot_and_annotate', 'pivot_samples', 'relations', 'show_metadata', 'to_soft']
In [8]:
gse.show_metadata()
!Series_title = SCL005: icSARS CoV Urbani or icSARS deltaORF6 infections of the 2B4 clonal derivative of Calu-3 cells - Time course !Series_geo_accession = GSE33267 !Series_status = Public on Nov 01 2011 !Series_submission_date = Oct 26 2011 !Series_last_update_date = Feb 22 2018 !Series_pubmed_id = 23365422 !Series_summary = Purpose of experiment was to compare transcriptomics of 2B4 cells (clonal derivative of Calu-3 cells) infected with either icSARS CoV or the icSARS deltaORF6 mutant. !Series_overall_design = Calu-3 cells were infected with either icSARS CoV or the icSARS deltaORF6 mutant at MOI of 5.0. Cells samples were collected at 0, 3, 7, 12, 24, 30, 36, 48, 54, 60 or 72h post infection. Each infected sample was done in triplicate. (Triplicates are defined as 3 different wells, plated at the same time using the same cell stock for all replicates.)There are triplicate time-matched mock for each time point from the same cell stock as rest of samples. Culture medium (the same as what the virus stock is in) was used for the mock infections. !Series_overall_design = The NIAID Systems Virology Center !Series_type = Expression profiling by array !Series_contributor = Armand,,Bankhead !Series_contributor = Jean,,Chang !Series_contributor = Amy,,Sims !Series_contributor = Michael,,Katze !Series_contributor = Ralph,,Baric !Series_contributor = Shannon,,McWeeney !Series_sample_id = GSM823177 !Series_sample_id = GSM823178 !Series_sample_id = GSM823179 !Series_sample_id = GSM823180 !Series_sample_id = GSM823181 !Series_sample_id = GSM823182 !Series_sample_id = GSM823183 !Series_sample_id = GSM823184 !Series_sample_id = GSM823185 !Series_sample_id = GSM823186 !Series_sample_id = GSM823187 !Series_sample_id = GSM823188 !Series_sample_id = GSM823189 !Series_sample_id = GSM823190 !Series_sample_id = GSM823191 !Series_sample_id = GSM823192 !Series_sample_id = GSM823193 !Series_sample_id = GSM823194 !Series_sample_id = GSM823195 !Series_sample_id = GSM823196 !Series_sample_id = GSM823197 !Series_sample_id = GSM823198 !Series_sample_id = GSM823199 !Series_sample_id = GSM823200 !Series_sample_id = GSM823201 !Series_sample_id = GSM823202 !Series_sample_id = GSM823203 !Series_sample_id = GSM823204 !Series_sample_id = GSM823205 !Series_sample_id = GSM823206 !Series_sample_id = GSM823207 !Series_sample_id = GSM823208 !Series_sample_id = GSM823209 !Series_sample_id = GSM823210 !Series_sample_id = GSM823211 !Series_sample_id = GSM823212 !Series_sample_id = GSM823213 !Series_sample_id = GSM823214 !Series_sample_id = GSM823215 !Series_sample_id = GSM823216 !Series_sample_id = GSM823217 !Series_sample_id = GSM823218 !Series_sample_id = GSM823219 !Series_sample_id = GSM823220 !Series_sample_id = GSM823221 !Series_sample_id = GSM823222 !Series_sample_id = GSM823223 !Series_sample_id = GSM823224 !Series_sample_id = GSM823225 !Series_sample_id = GSM823226 !Series_sample_id = GSM823227 !Series_sample_id = GSM823228 !Series_sample_id = GSM823229 !Series_sample_id = GSM823230 !Series_sample_id = GSM823231 !Series_sample_id = GSM823232 !Series_sample_id = GSM823233 !Series_sample_id = GSM823234 !Series_sample_id = GSM823235 !Series_sample_id = GSM823236 !Series_sample_id = GSM823237 !Series_sample_id = GSM823238 !Series_sample_id = GSM823239 !Series_sample_id = GSM823240 !Series_sample_id = GSM823241 !Series_sample_id = GSM823242 !Series_sample_id = GSM823243 !Series_sample_id = GSM823244 !Series_sample_id = GSM823245 !Series_sample_id = GSM823246 !Series_sample_id = GSM823247 !Series_sample_id = GSM823248 !Series_sample_id = GSM823249 !Series_sample_id = GSM823250 !Series_sample_id = GSM823251 !Series_sample_id = GSM823252 !Series_sample_id = GSM823253 !Series_sample_id = GSM823254 !Series_sample_id = GSM823255 !Series_sample_id = GSM823256 !Series_sample_id = GSM823257 !Series_sample_id = GSM823258 !Series_sample_id = GSM823259 !Series_sample_id = GSM823260 !Series_sample_id = GSM823261 !Series_sample_id = GSM823262 !Series_sample_id = GSM823263 !Series_sample_id = GSM823264 !Series_sample_id = GSM823265 !Series_sample_id = GSM823266 !Series_sample_id = GSM823267 !Series_sample_id = GSM823268 !Series_sample_id = GSM823269 !Series_sample_id = GSM823270 !Series_sample_id = GSM823271 !Series_sample_id = GSM823272 !Series_sample_id = GSM823273 !Series_sample_id = GSM823274 !Series_sample_id = GSM823275 !Series_contact_name = Michael,,Katze !Series_contact_email = data@viromics.washington.edu !Series_contact_laboratory = Michael G. Katze, Ph.D !Series_contact_department = Microbiology !Series_contact_institute = University of Washington !Series_contact_address = Rosen Building 960 Republican St. !Series_contact_city = Seattle !Series_contact_state = WA !Series_contact_zip/postal_code = 98109-4325 !Series_contact_country = USA !Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33267/suppl/GSE33267_RAW.tar !Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33267/suppl/GSE33267_marray_matrix_readme.txt !Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33267/suppl/GSE33267_scl005_EXPRS.txt.gz !Series_platform_id = GPL4133 !Series_platform_taxid = 9606 !Series_sample_taxid = 9606 !Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA149059
In [12]:
dir(gse.gpls['GPL4133'])
Out[12]:
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__metaclass__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_get_columns_as_string', '_get_metadata_as_string', '_get_object_as_soft', '_get_table_as_string', 'columns', 'database', 'geotype', 'get_accession', 'get_metadata_attribute', 'get_type', 'gses', 'gsms', 'head', 'metadata', 'name', 'relations', 'show_columns', 'show_metadata', 'show_table', 'table', 'to_soft']
In [91]:
gse.gpls['GPL4133'].table
gpls = gse.gpls['GPL4133'].table
In [92]:
gpls
Out[92]:
| ID | COL | ROW | NAME | SPOT_ID | CONTROL_TYPE | REFSEQ | GB_ACC | GENE | GENE_SYMBOL | ... | ENSEMBL_ID | TIGR_ID | ACCESSION_STRING | CHROMOSOMAL_LOCATION | CYTOBAND | DESCRIPTION | GO_ID | SEQUENCE | SPOT_ID.1 | ORDER | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 266 | 170 | GE_BrightCorner | GE_BrightCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1 |
| 1 | 2 | 266 | 168 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2 |
| 2 | 3 | 266 | 166 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3 |
| 3 | 4 | 266 | 164 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4 |
| 4 | 5 | 266 | 162 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45215 | 45216 | 1 | 9 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45216 |
| 45216 | 45217 | 1 | 7 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45217 |
| 45217 | 45218 | 1 | 5 | DarkCorner | DarkCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45218 |
| 45218 | 45219 | 1 | 3 | GE_BrightCorner | GE_BrightCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45219 |
| 45219 | 45220 | 1 | 1 | GE_BrightCorner | GE_BrightCorner | pos | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45220 |
45220 rows × 22 columns
In [16]:
gse.gsms['GSM823177'].table
Out[16]:
| ID_REF | VALUE | |
|---|---|---|
| 0 | 1 | 104000.00 |
| 1 | 2 | 3.61 |
| 2 | 3 | 3.62 |
| 3 | 4 | 3.63 |
| 4 | 5 | 3.63 |
| ... | ... | ... |
| 45010 | 45216 | 3.29 |
| 45011 | 45217 | 3.29 |
| 45012 | 45218 | 3.55 |
| 45013 | 45219 | 111000.00 |
| 45014 | 45220 | 108000.00 |
45015 rows × 2 columns
In [32]:
def process_gsms(gsms):
df = None
initialized = False
for k in gsms:
curr_df = gsms[k].table
if not initialized:
df = pd.DataFrame(data=curr_df.values, columns=['ID_REF', k])
initialized = True
else:
new_df = pd.DataFrame(data=curr_df.loc[:,['VALUE']].values, columns=[k])
df = df.join(new_df)
return df
In [48]:
expression_data = process_gsms(gse.gsms)
expression_data.iloc[:, 1:] = np.log2(expression_data.iloc[:, 1:])
In [49]:
expression_data
Out[49]:
| ID_REF | GSM823177 | GSM823178 | GSM823179 | GSM823180 | GSM823181 | GSM823182 | GSM823183 | GSM823184 | GSM823185 | ... | GSM823266 | GSM823267 | GSM823268 | GSM823269 | GSM823270 | GSM823271 | GSM823272 | GSM823273 | GSM823274 | GSM823275 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 16.666224 | 16.919981 | 16.376872 | 16.133704 | 16.459239 | 16.508042 | 16.595141 | 15.476746 | 16.356383 | ... | 16.236313 | 15.649481 | 13.813781 | 16.129685 | 16.269565 | 16.487777 | 16.333854 | 16.285908 | 15.467223 | 16.298492 |
| 1 | 2.0 | 1.851999 | 1.575312 | 1.516015 | 1.163499 | 2.367371 | 1.042644 | 0.903038 | 0.565597 | 0.704872 | ... | 0.941106 | 0.895303 | 0.765535 | 1.438293 | 0.847997 | 0.925999 | 1.169925 | 1.250962 | 1.014355 | 1.063503 |
| 2 | 3.0 | 1.855990 | 1.575312 | 0.526069 | 1.157044 | 1.042644 | 1.042644 | 0.903038 | 0.565597 | 0.704872 | ... | 0.941106 | 0.895303 | 1.327687 | 1.443607 | 0.847997 | 0.925999 | 1.169925 | 1.250962 | 0.722466 | 1.063503 |
| 3 | 4.0 | 1.859970 | 1.575312 | 0.526069 | 1.157044 | 1.042644 | 1.042644 | 0.910733 | 0.565597 | 0.704872 | ... | 0.941106 | 0.903038 | 1.090853 | 1.443607 | 0.847997 | 0.925999 | 1.169925 | 1.250962 | 0.722466 | 1.063503 |
| 4 | 5.0 | 1.859970 | 1.570463 | 0.526069 | 1.150560 | 1.042644 | 1.042644 | 0.910733 | 0.565597 | 0.704872 | ... | 0.941106 | 1.744161 | 0.748461 | 1.443607 | 0.847997 | 0.933573 | 1.169925 | 1.250962 | 0.722466 | 1.063503 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45010 | 45216.0 | 1.718088 | 1.536053 | 3.058316 | 1.124328 | 1.049631 | 1.130931 | 0.839960 | 0.782409 | 0.545968 | ... | 0.910733 | 0.799087 | 0.650765 | 1.367371 | 0.790772 | 0.925999 | 1.189034 | 1.077243 | 0.739848 | 1.049631 |
| 45011 | 45217.0 | 1.718088 | 1.536053 | 3.237258 | 1.124328 | 5.133399 | 1.130931 | 0.831877 | 1.333424 | 0.545968 | ... | 0.918386 | 0.799087 | 0.650765 | 1.367371 | 0.790772 | 0.925999 | 1.182692 | 1.077243 | 1.220330 | 1.049631 |
| 45012 | 45218.0 | 1.827819 | 1.541019 | 2.304511 | 1.130931 | 1.042644 | 1.130931 | 0.823749 | 1.416840 | 1.207893 | ... | 0.918386 | 0.799087 | 0.659925 | 1.367371 | 0.790772 | 0.918386 | 1.182692 | 1.077243 | 0.731183 | 1.049631 |
| 45013 | 45219.0 | 16.760200 | 16.919981 | 16.267738 | 16.376872 | 16.289515 | 16.186888 | 16.500282 | 16.078484 | 16.451211 | ... | 16.153631 | 15.556746 | 16.232571 | 16.161526 | 16.163492 | 16.457637 | 16.219395 | 16.131696 | 15.615400 | 16.192678 |
| 45014 | 45220.0 | 16.720672 | 16.943064 | 16.309192 | 16.253035 | 16.215609 | 16.265908 | 16.544723 | 15.894155 | 16.471993 | ... | 16.184953 | 15.603858 | 16.208006 | 16.204189 | 16.194603 | 16.476746 | 16.159556 | 16.562719 | 15.660664 | 16.251187 |
45015 rows × 100 columns
In [100]:
id_ref = expression_data.iloc[:,0]
id_ref
Out[100]:
0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
...
45010 45216.0
45011 45217.0
45012 45218.0
45013 45219.0
45014 45220.0
Name: ID_REF, Length: 45015, dtype: float64
In [50]:
control = expression_data.iloc[:, 1:34]
infected = expression_data.iloc[:, 34:]
In [45]:
plt.hist(control.iloc[:, 1]) # !!!
plt.hist(control.iloc[:, 2], color='#FF0000AA')
Out[45]:
(array([4.4159e+04, 5.1800e+02, 1.7200e+02, 1.0400e+02, 4.6000e+01,
7.0000e+00, 3.0000e+00, 2.0000e+00, 3.0000e+00, 1.0000e+00]),
array([1.08000000e+00, 5.61009720e+04, 1.12200864e+05, 1.68300756e+05,
2.24400648e+05, 2.80500540e+05, 3.36600432e+05, 3.92700324e+05,
4.48800216e+05, 5.04900108e+05, 5.61000000e+05]),
<BarContainer object of 10 artists>)
In [61]:
plt.hist(control.iloc[17, :])
plt.hist(infected.iloc[17, :], color='#FF0000AA')
Out[61]:
(array([ 6., 3., 12., 3., 9., 5., 5., 9., 8., 6.]),
array([5.93781517, 6.1382851 , 6.33875504, 6.53922497, 6.7396949 ,
6.94016484, 7.14063477, 7.3411047 , 7.54157464, 7.74204457,
7.94251451]),
<BarContainer object of 10 artists>)
In [81]:
a = [-1,0,0,1,2,2,3,3,3,3,4,4,5,6,7,7,8]
b = np.array([1,2,2,3,3,3,3,4,4,5]) +1
plt.hist(a)
plt.hist(b, color='#FF0000AA')
Out[81]:
(array([1., 0., 2., 0., 0., 4., 0., 2., 0., 1.]), array([2. , 2.4, 2.8, 3.2, 3.6, 4. , 4.4, 4.8, 5.2, 5.6, 6. ]), <BarContainer object of 10 artists>)
In [82]:
ttest_ind(a, b)
Out[82]:
Ttest_indResult(statistic=-0.7353361753098311, pvalue=0.4689776626892319)
In [86]:
np.array([[1,2],[3,4]]).ravel()
Out[86]:
array([1, 2, 3, 4])
In [102]:
i = 0
id_ref.iloc[i]
Out[102]:
1.0
In [103]:
sig_data = []
for i in range(expression_data.shape[0]):
control_expression_i = control.iloc[i,:].values.ravel()
infected_expression_i = infected.iloc[i,:].values.ravel()
ref = id_ref.iloc[i]
control_mean_i = control_expression_i.mean()
infected_mean_i = infected_expression_i.mean()
log2FC = infected_mean_i - control_mean_i
_, p = ttest_ind(control_expression_i, infected_expression_i, equal_var=False)
sig = False
if p < 0.05 and abs(log2FC) > 1.5:
sig = True
sig_data.append([ref, control_mean_i, infected_mean_i, log2FC, p, sig])
sig_df = pd.DataFrame(data=sig_data, columns=['ID_REF', 'CONTROL_MEAN', 'INFECTED_MEAN', 'LOG2FC', 'P', 'SIG'])
In [104]:
sig_df
Out[104]:
| ID_REF | CONTROL_MEAN | INFECTED_MEAN | LOG2FC | P | SIG | |
|---|---|---|---|---|---|---|
| 0 | 1.0 | 16.473359 | 16.253439 | -0.219920 | 0.009117 | False |
| 1 | 2.0 | 1.293028 | 1.382012 | 0.088984 | 0.581833 | False |
| 2 | 3.0 | 1.003003 | 0.947770 | -0.055233 | 0.448868 | False |
| 3 | 4.0 | 0.981373 | 0.963976 | -0.017397 | 0.816628 | False |
| 4 | 5.0 | 0.983190 | 0.955862 | -0.027329 | 0.718231 | False |
| ... | ... | ... | ... | ... | ... | ... |
| 45010 | 45216.0 | 1.127839 | 0.969169 | -0.158670 | 0.108337 | False |
| 45011 | 45217.0 | 1.370958 | 1.104557 | -0.266401 | 0.214096 | False |
| 45012 | 45218.0 | 1.200348 | 1.021117 | -0.179231 | 0.058063 | False |
| 45013 | 45219.0 | 16.517345 | 16.276750 | -0.240596 | 0.001317 | False |
| 45014 | 45220.0 | 16.498123 | 16.285789 | -0.212335 | 0.012087 | False |
45015 rows × 6 columns
In [105]:
gene_data = gpls.loc[:,['ID', 'GENE_SYMBOL']]
gene_data
Out[105]:
| ID | GENE_SYMBOL | |
|---|---|---|
| 0 | 1 | NaN |
| 1 | 2 | NaN |
| 2 | 3 | NaN |
| 3 | 4 | NaN |
| 4 | 5 | NaN |
| ... | ... | ... |
| 45215 | 45216 | NaN |
| 45216 | 45217 | NaN |
| 45217 | 45218 | NaN |
| 45218 | 45219 | NaN |
| 45219 | 45220 | NaN |
45220 rows × 2 columns
In [107]:
gene_diff = gene_data.set_index('ID').join(sig_df.set_index('ID_REF'))
In [109]:
gene_diff.iloc[11]
Out[109]:
GENE_SYMBOL APOBEC3B CONTROL_MEAN 7.79091 INFECTED_MEAN 8.41112 LOG2FC 0.620215 P 0.00314838 SIG False Name: 12, dtype: object
In [120]:
sig_exp = gene_diff[gene_diff['SIG'] == True].dropna()
In [121]:
sorted_sig_exp = sig_exp.sort_values(by=['LOG2FC'])
sorted_sig_exp
Out[121]:
| GENE_SYMBOL | CONTROL_MEAN | INFECTED_MEAN | LOG2FC | P | SIG | |
|---|---|---|---|---|---|---|
| ID | ||||||
| 4701 | CAPNS2 | 10.361622 | 6.964108 | -3.397514 | 3.567899e-18 | True |
| 6494 | ANXA13 | 8.921412 | 6.294918 | -2.626493 | 5.017886e-15 | True |
| 6748 | CLRN3 | 7.397385 | 4.859611 | -2.537774 | 1.952626e-14 | True |
| 991 | TTR | 8.851363 | 6.335043 | -2.516320 | 4.249409e-17 | True |
| 27545 | ANXA13 | 9.821740 | 7.325120 | -2.496620 | 7.539615e-15 | True |
| ... | ... | ... | ... | ... | ... | ... |
| 30072 | PAEP | 3.327891 | 7.303118 | 3.975227 | 1.057617e-19 | True |
| 1093 | CSF3 | 1.538147 | 6.317711 | 4.779565 | 7.796504e-16 | True |
| 17062 | FADS2 | 5.073183 | 9.929027 | 4.855844 | 1.382949e-22 | True |
| 39118 | PRNT | 1.659269 | 7.161729 | 5.502460 | 1.985034e-23 | True |
| 28337 | EDAR | 0.779498 | 6.825019 | 6.045520 | 1.172127e-24 | True |
433 rows × 6 columns
In [166]:
sorted_sig_exp[sorted_sig_exp['GENE_SYMBOL'] == 'DEFA4']
Out[166]:
| GENE_SYMBOL | CONTROL_MEAN | INFECTED_MEAN | LOG2FC | P | SIG | |
|---|---|---|---|---|---|---|
| ID |
In [122]:
up_regulated = sorted_sig_exp[-10:]
down_regulated = sorted_sig_exp[:10]
In [123]:
up_regulated
Out[123]:
| GENE_SYMBOL | CONTROL_MEAN | INFECTED_MEAN | LOG2FC | P | SIG | |
|---|---|---|---|---|---|---|
| ID | ||||||
| 44003 | HHIP | 3.137443 | 7.000847 | 3.863404 | 7.245861e-20 | True |
| 11787 | RGR | 5.132149 | 9.006631 | 3.874481 | 3.813493e-19 | True |
| 29465 | CCR8 | 0.787747 | 4.728019 | 3.940272 | 6.288462e-20 | True |
| 35433 | CREB5 | 6.609011 | 10.580027 | 3.971016 | 2.748210e-18 | True |
| 28117 | IL28B | 2.135472 | 6.107292 | 3.971820 | 1.915863e-17 | True |
| 30072 | PAEP | 3.327891 | 7.303118 | 3.975227 | 1.057617e-19 | True |
| 1093 | CSF3 | 1.538147 | 6.317711 | 4.779565 | 7.796504e-16 | True |
| 17062 | FADS2 | 5.073183 | 9.929027 | 4.855844 | 1.382949e-22 | True |
| 39118 | PRNT | 1.659269 | 7.161729 | 5.502460 | 1.985034e-23 | True |
| 28337 | EDAR | 0.779498 | 6.825019 | 6.045520 | 1.172127e-24 | True |
In [124]:
down_regulated
Out[124]:
| GENE_SYMBOL | CONTROL_MEAN | INFECTED_MEAN | LOG2FC | P | SIG | |
|---|---|---|---|---|---|---|
| ID | ||||||
| 4701 | CAPNS2 | 10.361622 | 6.964108 | -3.397514 | 3.567899e-18 | True |
| 6494 | ANXA13 | 8.921412 | 6.294918 | -2.626493 | 5.017886e-15 | True |
| 6748 | CLRN3 | 7.397385 | 4.859611 | -2.537774 | 1.952626e-14 | True |
| 991 | TTR | 8.851363 | 6.335043 | -2.516320 | 4.249409e-17 | True |
| 27545 | ANXA13 | 9.821740 | 7.325120 | -2.496620 | 7.539615e-15 | True |
| 5020 | LOC100132167 | 11.964410 | 9.672812 | -2.291598 | 4.835899e-18 | True |
| 26799 | MS4A8B | 6.113347 | 3.858874 | -2.254473 | 6.549172e-11 | True |
| 2169 | F5 | 5.390926 | 3.260689 | -2.130237 | 4.100002e-16 | True |
| 5853 | LEFTY1 | 6.228430 | 4.161496 | -2.066934 | 7.948616e-15 | True |
| 21599 | CA9 | 8.475047 | 6.424245 | -2.050802 | 3.440158e-12 | True |
In [162]:
genes = ['EDAR']
string_ids = stringdb.get_string_ids(genes)
enrichment_df = stringdb.get_enrichment(string_ids.queryItem)
In [163]:
string_ids
Out[163]:
| queryItem | queryIndex | stringId | ncbiTaxonId | taxonName | preferredName | annotation | |
|---|---|---|---|---|---|---|---|
| 0 | EDAR | 0 | 9606.ENSP00000258443 | 9606 | Homo sapiens | EDAR | Tumor necrosis factor receptor superfamily mem... |
In [164]:
enrichment_df
Out[164]:
| category | term | number_of_genes | number_of_genes_in_background | ncbiTaxonId | inputGenes | preferredNames | p_value | fdr | description | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Process | GO:0042476 | 5 | 113 | 9606 | WNT10A,EDAR,SHH,PAX9,EDA | WNT10A,EDAR,SHH,PAX9,EDA | 3.280000e-09 | 3.530000e-06 | odontogenesis |
| 1 | Process | GO:0060662 | 3 | 5 | 9606 | EDAR,SHH,EDA | EDAR,SHH,EDA | 7.380000e-09 | 3.970000e-06 | salivary gland cavitation |
| 2 | Process | GO:0001942 | 4 | 72 | 9606 | WNT10A,EDAR,SHH,EDA | WNT10A,EDAR,SHH,EDA | 6.780000e-08 | 1.820000e-05 | hair follicle development |
| 3 | Process | GO:0042481 | 3 | 26 | 9606 | WNT10A,SHH,PAX9 | WNT10A,SHH,PAX9 | 4.780000e-07 | 5.150000e-05 | regulation of odontogenesis |
| 4 | Process | GO:0009887 | 6 | 865 | 9606 | WNT10A,EDAR,SHH,WNT10B,PAX9,EDA | WNT10A,EDAR,SHH,WNT10B,PAX9,EDA | 2.910000e-06 | 2.200000e-04 | animal organ morphogenesis |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 198 | RCTM | HSA-373080 | 3 | 93 | 9606 | WNT10A,SHH,WNT10B | WNT10A,SHH,WNT10B | 1.830000e-05 | 4.200000e-04 | Class B/2 (Secretin family receptors) |
| 199 | RCTM | HSA-3238698 | 2 | 26 | 9606 | WNT10A,WNT10B | WNT10A,WNT10B | 1.100000e-04 | 1.200000e-03 | WNT ligand biogenesis and trafficking |
| 200 | NetworkNeighborAL | CL:11051 | 3 | 8 | 9606 | EDAR,EDARADD,EDA | EDAR,EDARADD,EDA | 2.170000e-08 | 8.040000e-07 | mixed, incl. salivary gland cavitation, and Ta... |
| 201 | NetworkNeighborAL | CL:19550 | 2 | 10 | 9606 | SLC45A2,SLC24A5 | SLC45A2,SLC24A5 | 1.890000e-05 | 1.700000e-04 | Melanin biosynthesis, and Sodium/potassium/cal... |
| 202 | NetworkNeighborAL | CL:5745 | 2 | 12 | 9606 | WNT10A,WNT10B | WNT10A,WNT10B | 2.600000e-05 | 1.900000e-04 | WNT ligand biogenesis and trafficking |
203 rows × 10 columns
In [167]:
# up_regulated.loc[:,'GENE_SYMBOL'].values.ravel() + down_regulated.loc[:,'GENE_SYMBOL'].values.ravel()
up_regulated.loc[:,'GENE_SYMBOL'].values
network = stringdb.get_network(['PGLYRP1', 'LEF1', 'CXCR5', 'CXCR1', 'CAMP', 'DEFA4', 'ELANE', 'LTF', 'CR2', 'ARG1', 'SLPI', 'MS4A1'])
network
Out[167]:
| stringId_A | stringId_B | preferredName_A | preferredName_B | ncbiTaxonId | score | nscore | fscore | pscore | ascore | escore | dscore | tscore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9606.ENSP00000008938 | 9606.ENSP00000349446 | PGLYRP1 | ARG1 | 9606 | 0.917 | 0 | 0 | 0 | 0.213 | 0.000 | 0.9 | 0.000 |
| 1 | 9606.ENSP00000008938 | 9606.ENSP00000349446 | PGLYRP1 | ARG1 | 9606 | 0.917 | 0 | 0 | 0 | 0.213 | 0.000 | 0.9 | 0.000 |
| 2 | 9606.ENSP00000008938 | 9606.ENSP00000342082 | PGLYRP1 | SLPI | 9606 | 0.922 | 0 | 0 | 0 | 0.000 | 0.000 | 0.9 | 0.255 |
| 3 | 9606.ENSP00000008938 | 9606.ENSP00000342082 | PGLYRP1 | SLPI | 9606 | 0.922 | 0 | 0 | 0 | 0.000 | 0.000 | 0.9 | 0.255 |
| 4 | 9606.ENSP00000008938 | 9606.ENSP00000466090 | PGLYRP1 | ELANE | 9606 | 0.932 | 0 | 0 | 0 | 0.191 | 0.000 | 0.9 | 0.227 |
| 5 | 9606.ENSP00000008938 | 9606.ENSP00000466090 | PGLYRP1 | ELANE | 9606 | 0.932 | 0 | 0 | 0 | 0.191 | 0.000 | 0.9 | 0.227 |
| 6 | 9606.ENSP00000008938 | 9606.ENSP00000231751 | PGLYRP1 | LTF | 9606 | 0.951 | 0 | 0 | 0 | 0.149 | 0.000 | 0.9 | 0.473 |
| 7 | 9606.ENSP00000008938 | 9606.ENSP00000231751 | PGLYRP1 | LTF | 9606 | 0.951 | 0 | 0 | 0 | 0.149 | 0.000 | 0.9 | 0.473 |
| 8 | 9606.ENSP00000008938 | 9606.ENSP00000297435 | PGLYRP1 | DEFA4 | 9606 | 0.964 | 0 | 0 | 0 | 0.542 | 0.000 | 0.9 | 0.297 |
| 9 | 9606.ENSP00000008938 | 9606.ENSP00000297435 | PGLYRP1 | DEFA4 | 9606 | 0.964 | 0 | 0 | 0 | 0.542 | 0.000 | 0.9 | 0.297 |
| 10 | 9606.ENSP00000008938 | 9606.ENSP00000296435 | PGLYRP1 | CAMP | 9606 | 0.968 | 0 | 0 | 0 | 0.560 | 0.000 | 0.9 | 0.337 |
| 11 | 9606.ENSP00000008938 | 9606.ENSP00000296435 | PGLYRP1 | CAMP | 9606 | 0.968 | 0 | 0 | 0 | 0.560 | 0.000 | 0.9 | 0.337 |
| 12 | 9606.ENSP00000231751 | 9606.ENSP00000349446 | LTF | ARG1 | 9606 | 0.914 | 0 | 0 | 0 | 0.096 | 0.000 | 0.9 | 0.125 |
| 13 | 9606.ENSP00000231751 | 9606.ENSP00000349446 | LTF | ARG1 | 9606 | 0.914 | 0 | 0 | 0 | 0.096 | 0.000 | 0.9 | 0.125 |
| 14 | 9606.ENSP00000231751 | 9606.ENSP00000342082 | LTF | SLPI | 9606 | 0.941 | 0 | 0 | 0 | 0.101 | 0.000 | 0.9 | 0.398 |
| 15 | 9606.ENSP00000231751 | 9606.ENSP00000342082 | LTF | SLPI | 9606 | 0.941 | 0 | 0 | 0 | 0.101 | 0.000 | 0.9 | 0.398 |
| 16 | 9606.ENSP00000231751 | 9606.ENSP00000296435 | LTF | CAMP | 9606 | 0.946 | 0 | 0 | 0 | 0.231 | 0.000 | 0.9 | 0.355 |
| 17 | 9606.ENSP00000231751 | 9606.ENSP00000296435 | LTF | CAMP | 9606 | 0.946 | 0 | 0 | 0 | 0.231 | 0.000 | 0.9 | 0.355 |
| 18 | 9606.ENSP00000231751 | 9606.ENSP00000466090 | LTF | ELANE | 9606 | 0.952 | 0 | 0 | 0 | 0.155 | 0.067 | 0.9 | 0.473 |
| 19 | 9606.ENSP00000231751 | 9606.ENSP00000466090 | LTF | ELANE | 9606 | 0.952 | 0 | 0 | 0 | 0.155 | 0.067 | 0.9 | 0.473 |
| 20 | 9606.ENSP00000231751 | 9606.ENSP00000297435 | LTF | DEFA4 | 9606 | 0.963 | 0 | 0 | 0 | 0.284 | 0.000 | 0.9 | 0.526 |
| 21 | 9606.ENSP00000231751 | 9606.ENSP00000297435 | LTF | DEFA4 | 9606 | 0.963 | 0 | 0 | 0 | 0.284 | 0.000 | 0.9 | 0.526 |
| 22 | 9606.ENSP00000292174 | 9606.ENSP00000356024 | CXCR5 | CR2 | 9606 | 0.680 | 0 | 0 | 0 | 0.062 | 0.000 | 0.0 | 0.673 |
| 23 | 9606.ENSP00000292174 | 9606.ENSP00000356024 | CXCR5 | CR2 | 9606 | 0.680 | 0 | 0 | 0 | 0.062 | 0.000 | 0.0 | 0.673 |
| 24 | 9606.ENSP00000292174 | 9606.ENSP00000295683 | CXCR5 | CXCR1 | 9606 | 0.910 | 0 | 0 | 0 | 0.055 | 0.000 | 0.9 | 0.630 |
| 25 | 9606.ENSP00000292174 | 9606.ENSP00000295683 | CXCR5 | CXCR1 | 9606 | 0.910 | 0 | 0 | 0 | 0.055 | 0.000 | 0.9 | 0.630 |
| 26 | 9606.ENSP00000296435 | 9606.ENSP00000349446 | CAMP | ARG1 | 9606 | 0.917 | 0 | 0 | 0 | 0.137 | 0.000 | 0.9 | 0.121 |
| 27 | 9606.ENSP00000296435 | 9606.ENSP00000349446 | CAMP | ARG1 | 9606 | 0.917 | 0 | 0 | 0 | 0.137 | 0.000 | 0.9 | 0.121 |
| 28 | 9606.ENSP00000296435 | 9606.ENSP00000342082 | CAMP | SLPI | 9606 | 0.964 | 0 | 0 | 0 | 0.088 | 0.000 | 0.9 | 0.645 |
| 29 | 9606.ENSP00000296435 | 9606.ENSP00000342082 | CAMP | SLPI | 9606 | 0.964 | 0 | 0 | 0 | 0.088 | 0.000 | 0.9 | 0.645 |
| 30 | 9606.ENSP00000296435 | 9606.ENSP00000466090 | CAMP | ELANE | 9606 | 0.980 | 0 | 0 | 0 | 0.320 | 0.379 | 0.9 | 0.586 |
| 31 | 9606.ENSP00000296435 | 9606.ENSP00000466090 | CAMP | ELANE | 9606 | 0.980 | 0 | 0 | 0 | 0.320 | 0.379 | 0.9 | 0.586 |
| 32 | 9606.ENSP00000296435 | 9606.ENSP00000297435 | CAMP | DEFA4 | 9606 | 0.980 | 0 | 0 | 0 | 0.749 | 0.000 | 0.9 | 0.292 |
| 33 | 9606.ENSP00000296435 | 9606.ENSP00000297435 | CAMP | DEFA4 | 9606 | 0.980 | 0 | 0 | 0 | 0.749 | 0.000 | 0.9 | 0.292 |
| 34 | 9606.ENSP00000297435 | 9606.ENSP00000349446 | DEFA4 | ARG1 | 9606 | 0.914 | 0 | 0 | 0 | 0.168 | 0.000 | 0.9 | 0.050 |
| 35 | 9606.ENSP00000297435 | 9606.ENSP00000349446 | DEFA4 | ARG1 | 9606 | 0.914 | 0 | 0 | 0 | 0.168 | 0.000 | 0.9 | 0.050 |
| 36 | 9606.ENSP00000297435 | 9606.ENSP00000342082 | DEFA4 | SLPI | 9606 | 0.916 | 0 | 0 | 0 | 0.000 | 0.000 | 0.9 | 0.195 |
| 37 | 9606.ENSP00000297435 | 9606.ENSP00000342082 | DEFA4 | SLPI | 9606 | 0.916 | 0 | 0 | 0 | 0.000 | 0.000 | 0.9 | 0.195 |
| 38 | 9606.ENSP00000297435 | 9606.ENSP00000466090 | DEFA4 | ELANE | 9606 | 0.987 | 0 | 0 | 0 | 0.784 | 0.000 | 0.9 | 0.456 |
| 39 | 9606.ENSP00000297435 | 9606.ENSP00000466090 | DEFA4 | ELANE | 9606 | 0.987 | 0 | 0 | 0 | 0.784 | 0.000 | 0.9 | 0.456 |
| 40 | 9606.ENSP00000342082 | 9606.ENSP00000349446 | SLPI | ARG1 | 9606 | 0.906 | 0 | 0 | 0 | 0.061 | 0.000 | 0.9 | 0.081 |
| 41 | 9606.ENSP00000342082 | 9606.ENSP00000349446 | SLPI | ARG1 | 9606 | 0.906 | 0 | 0 | 0 | 0.061 | 0.000 | 0.9 | 0.081 |
| 42 | 9606.ENSP00000342082 | 9606.ENSP00000466090 | SLPI | ELANE | 9606 | 0.997 | 0 | 0 | 0 | 0.061 | 0.870 | 0.9 | 0.833 |
| 43 | 9606.ENSP00000342082 | 9606.ENSP00000466090 | SLPI | ELANE | 9606 | 0.997 | 0 | 0 | 0 | 0.061 | 0.870 | 0.9 | 0.833 |
| 44 | 9606.ENSP00000349446 | 9606.ENSP00000466090 | ARG1 | ELANE | 9606 | 0.928 | 0 | 0 | 0 | 0.108 | 0.000 | 0.9 | 0.260 |
| 45 | 9606.ENSP00000349446 | 9606.ENSP00000466090 | ARG1 | ELANE | 9606 | 0.928 | 0 | 0 | 0 | 0.108 | 0.000 | 0.9 | 0.260 |
| 46 | 9606.ENSP00000356024 | 9606.ENSP00000433277 | CR2 | MS4A1 | 9606 | 0.541 | 0 | 0 | 0 | 0.232 | 0.000 | 0.0 | 0.427 |
| 47 | 9606.ENSP00000356024 | 9606.ENSP00000433277 | CR2 | MS4A1 | 9606 | 0.541 | 0 | 0 | 0 | 0.232 | 0.000 | 0.0 | 0.427 |
In [168]:
genes = ['PGLYRP1', 'LEF1', 'CXCR5', 'CXCR1', 'CAMP', 'DEFA4', 'ELANE', 'LTF', 'CR2', 'ARG1', 'SLPI', 'MS4A1']
string_ids = stringdb.get_string_ids(genes)
enrichment_df = stringdb.get_enrichment(string_ids.queryItem)
In [169]:
enrichment_df
Out[169]:
| category | term | number_of_genes | number_of_genes_in_background | ncbiTaxonId | inputGenes | preferredNames | p_value | fdr | description | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Process | GO:0045321 | 12 | 894 | 9606 | PGLYRP1,LTF,LEF1,CXCR5,CXCR1,CAMP,DEFA4,SLPI,A... | PGLYRP1,LTF,LEF1,CXCR5,CXCR1,CAMP,DEFA4,SLPI,A... | 9.000000e-17 | 9.490000e-14 | leukocyte activation |
| 1 | Process | GO:0006955 | 12 | 1560 | 9606 | PGLYRP1,LTF,LEF1,CXCR5,CXCR1,CAMP,DEFA4,SLPI,A... | PGLYRP1,LTF,LEF1,CXCR5,CXCR1,CAMP,DEFA4,SLPI,A... | 6.910000e-14 | 2.430000e-11 | immune response |
| 2 | Process | GO:0006959 | 8 | 252 | 9606 | PGLYRP1,LTF,CAMP,DEFA4,SLPI,CR2,MS4A1,ELANE | PGLYRP1,LTF,CAMP,DEFA4,SLPI,CR2,MS4A1,ELANE | 4.110000e-13 | 1.080000e-10 | humoral immune response |
| 3 | Process | GO:0002252 | 10 | 927 | 9606 | PGLYRP1,LTF,LEF1,CXCR1,CAMP,DEFA4,SLPI,ARG1,CR... | PGLYRP1,LTF,LEF1,CXCR1,CAMP,DEFA4,SLPI,ARG1,CR... | 3.640000e-12 | 7.680000e-10 | immune effector process |
| 4 | Process | GO:0035821 | 7 | 182 | 9606 | PGLYRP1,LTF,LEF1,CAMP,DEFA4,SLPI,ELANE | PGLYRP1,LTF,LEF1,CAMP,DEFA4,SLPI,ELANE | 5.310000e-12 | 9.350000e-10 | modification of morphology or physiology of ot... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 231 | RCTM | HSA-380108 | 2 | 48 | 9606 | CXCR5,CXCR1 | CXCR5,CXCR1 | 4.200000e-04 | 3.400000e-03 | Chemokine receptors bind chemokines |
| 232 | RCTM | HSA-977606 | 2 | 47 | 9606 | CR2,ELANE | CR2,ELANE | 4.000000e-04 | 3.400000e-03 | Regulation of Complement cascade |
| 233 | NetworkNeighborAL | CL:10260 | 6 | 11 | 9606 | PGLYRP1,LTF,CAMP,DEFA4,SLPI,ELANE | PGLYRP1,LTF,CAMP,DEFA4,SLPI,ELANE | 1.460000e-16 | 5.690000e-15 | specific granule lumen |
| 234 | NetworkNeighborAL | CL:10261 | 5 | 6 | 9606 | PGLYRP1,CAMP,DEFA4,SLPI,ELANE | PGLYRP1,CAMP,DEFA4,SLPI,ELANE | 1.520000e-14 | 1.490000e-13 | mixed, incl. Cathelicidin, and neutrophil medi... |
| 235 | NetworkNeighborAL | CL:7364 | 2 | 24 | 9606 | CXCR5,CXCR1 | CXCR5,CXCR1 | 1.100000e-04 | 4.800000e-04 | CXC Chemokine domain, and dendritic cell chemo... |
236 rows × 10 columns