Life Science Computing

Datensatz

"Transcription profiling by high throughput sequencing in different developmental stages of Zea mays subsp. mays tissues"
Set: RNA-Seq mRNA baseline
Organism: Zea mays
Data Source: Expression Atlas EBI Datensatz

Inhaltsverzeichnis

Datensatz
Inhaltsverzeichnis
Preparation
1. Imports
2. Load Data
Exploring and Processing the Experimental Data
Exploring and Processing the Results

Preparation

Imports

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd

Load Data

exp_design = pd.read_csv('E-MTAB-4342-experiment-design.tsv', sep='\t', header=0)
results = pd.read_csv('E-MTAB-4342-query-results.fpkms.tsv', sep='\t', header=4)

Exploring and Processing the Experimental Data

Print all Columns

exp_design.columns

Index(['Run', 'Sample Characteristic[cultivar]',
       'Sample Characteristic Ontology Term[cultivar]',
       'Sample Characteristic[developmental stage]',
       'Sample Characteristic Ontology Term[developmental stage]',
       'Sample Characteristic[organism]',
       'Sample Characteristic Ontology Term[organism]',
       'Sample Characteristic[organism part]',
       'Sample Characteristic Ontology Term[organism part]',
       'Factor Value[developmental stage]',
       'Factor Value Ontology Term[developmental stage]',
       'Factor Value[organism part]',
       'Factor Value Ontology Term[organism part]', 'Analysed'],
      dtype='object')

Check if Columns are equal

exp_design['Sample Characteristic Ontology Term[developmental stage]'].equals(exp_design['Factor Value Ontology Term[developmental stage]'])

True

exp_design['Sample Characteristic Ontology Term[organism part]'].equals(exp_design['Factor Value Ontology Term[organism part]'])

True

exp_design['Sample Characteristic[organism part]'].equals(exp_design['Factor Value[organism part]'])

True

exp_design['Sample Characteristic[developmental stage]'].equals(exp_design['Factor Value[developmental stage]'])

True

Delete Data

Delete all Columns with less than 2 different Values

for col in exp_design.columns:
    if len(exp_design[col].unique()) == 1:
        del exp_design[col]

exp_design.columns

Index(['Run', 'Sample Characteristic[developmental stage]',
       'Sample Characteristic Ontology Term[developmental stage]',
       'Sample Characteristic[organism part]',
       'Sample Characteristic Ontology Term[organism part]',
       'Factor Value[developmental stage]',
       'Factor Value Ontology Term[developmental stage]',
       'Factor Value[organism part]',
       'Factor Value Ontology Term[organism part]', 'Analysed'],
      dtype='object')

Delete double Columns

del exp_design['Factor Value Ontology Term[developmental stage]']
del exp_design['Factor Value Ontology Term[organism part]']
del exp_design['Factor Value[organism part]']
del exp_design['Factor Value[developmental stage]']

Delete Sample Characteristic Ontology Columns

del exp_design['Sample Characteristic Ontology Term[developmental stage]']
del exp_design['Sample Characteristic Ontology Term[organism part]']

Delete useless Cols

del exp_design['Analysed']
del exp_design['Run']

exp_design = exp_design.rename(columns={"Sample Characteristic[developmental stage]": "developmental stage", "Sample Characteristic[organism part]": "organism part"})
exp_design

	developmental stage	organism part
0	6 days after pollination	leaf
1	6 days after pollination	leaf
2	6 days after anthesis	leaf
3	12 days after pollination	leaf
4	12 days after pollination	leaf
...	...	...
265	16 days after pollination	endosperm
266	16 days after pollination	plant embryo
267	16 days after pollination	plant embryo
268	16 days after pollination	plant embryo
269	18 days after pollination	seed

270 rows × 2 columns

Exploring the Leftover-Data

print(exp_design["organism part"].nunique(), exp_design["developmental stage"].nunique())

44 32

exp_design.describe()

	developmental stage	organism part
count	267	270
unique	32	44
top	7 days after sowing	seed
freq	24	33

Zwischenfazit zu den Experiment-Daten 🏁

Kultursorte

Um die Ergebnisse nicht aufgrund verschiedenen Genbestands zu verfälschen, wurde eine Inzuchtline der Sorte B73 gezüchtet. Die Inzuchtline stellt sicher, dass der Genbestand aller Probenobjekte gleich ist.

Probenort / Organismus Teil

Die Expressionsrate sollte außerdem in verschiedenen Teilen des Organismus gemessen werden.
Es wurden 44 verschiedene Abschnitte gemessen. Unter Anderem:

Blatt
Internodium
verschiedene Pfahlwurzelzonen
Stele
Hauptwurzel
Perikarp
Seminalwurzel

Entwicklungsstadien 🌱

Das eigentliche Ziel des Versuches war es, die Genexpressionsrate in Abhängigkeit von dem Entwicklungsstadium der Pflanze zu untersuchen. Es wurden 32 verschiedene Entwicklungsstadien untersucht.
Unter Anderem verschiedene Zeitabstände nach:

Bestäubung
Anthese
Aussaat

Außerdem wurde auch die Anzahl der sichbaren Blätter als Stadium eingeteilt und danach unterschieden.
Zusätzlich wurden berücksichtigt:

Blütephase der ganzen Pflanze
Stadium der Fruchtbildung der ganzen Pflanze in verschiedenen prozentualen Abschnitten

Exploring and Processing the Results FPKM (Fragments Per Kilobase Million)

Print all Columns

results

	Gene ID	Gene Name	root, 3 days after sowing	differentiation zone of primary root, 3 days after sowing	meristematic zone and elongation zone, 3 days after sowing	stele, 3 days after sowing	cortical parenchyma of root, 3 days after sowing	coleoptile, 6 days after sowing	primary root, 6 days after sowing	primary root, 7 days after sowing	...	seed, 24 days after pollination	plant embryo, 24 days after pollination	endosperm, 24 days after pollination	leaf, 30 days after pollination	internode, 30 days after pollination	thirteenth leaf, whole plant fruit formation stage 30 to 50%	thirteenth leaf, whole plant flowering stage	pre-pollination cob, whole plant flowering stage	anthers, whole plant flowering stage	silks, whole plant flowering stage
0	Zm00001eb000010	Zm00001eb000010	2.0	2.0	2.0	2.0	3.0	4.0	3.0	3.0	...	2.0	5.0	0.7	10.0	6.0	6.0	10.0	7.0	4.0	4.0
1	Zm00001eb000020	Zm00001eb000020	24.0	15.0	38.0	19.0	11.0	38.0	31.0	20.0	...	23.0	68.0	14.0	0.8	1.0	0.7	1.0	48.0	17.0	16.0
2	Zm00001eb000030	Zm00001eb000030	NaN	NaN	NaN	NaN	NaN	0.1	0.6	NaN	...	0.2	NaN	NaN	0.1	NaN	NaN	NaN	0.2	NaN	NaN
3	Zm00001eb000040	Zm00001eb000040	NaN	NaN	NaN	0.5	NaN	NaN	0.5	NaN	...	NaN	NaN	NaN	0.1	0.1	NaN	NaN	0.3	NaN	0.1
4	Zm00001eb000050	Zm00001eb000050	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	0.2	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	Zm00001eb442870	Zm00001eb442870	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.2	NaN	0.3	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37840	Zm00001eb442890	Zm00001eb442890	NaN	NaN	NaN	NaN	NaN	0.2	NaN	NaN	...	0.1	NaN	NaN	NaN	NaN	NaN	NaN	0.1	NaN	NaN
37841	Zm00001eb442910	Zm00001eb442910	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37842	Zm00001eb442960	Zm00001eb442960	NaN	NaN	0.2	NaN	0.2	1.0	0.8	NaN	...	0.4	1.0	0.1	0.4	0.2	0.1	NaN	0.6	0.5	0.5
37843	Zm00001eb443030	Zm00001eb443030	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

37844 rows × 94 columns

Check if Columns are equal

results['Gene ID'].equals(results['Gene Name'])

True

Delete Data

Delete double Columns

del results['Gene Name']

results

	Gene ID	root, 3 days after sowing	differentiation zone of primary root, 3 days after sowing	meristematic zone and elongation zone, 3 days after sowing	stele, 3 days after sowing	cortical parenchyma of root, 3 days after sowing	coleoptile, 6 days after sowing	primary root, 6 days after sowing	primary root, 7 days after sowing	root, 7 days after sowing	...	seed, 24 days after pollination	plant embryo, 24 days after pollination	endosperm, 24 days after pollination	leaf, 30 days after pollination	internode, 30 days after pollination	thirteenth leaf, whole plant fruit formation stage 30 to 50%	thirteenth leaf, whole plant flowering stage	pre-pollination cob, whole plant flowering stage	anthers, whole plant flowering stage	silks, whole plant flowering stage
0	Zm00001eb000010	2.0	2.0	2.0	2.0	3.0	4.0	3.0	3.0	3.0	...	2.0	5.0	0.7	10.0	6.0	6.0	10.0	7.0	4.0	4.0
1	Zm00001eb000020	24.0	15.0	38.0	19.0	11.0	38.0	31.0	20.0	22.0	...	23.0	68.0	14.0	0.8	1.0	0.7	1.0	48.0	17.0	16.0
2	Zm00001eb000030	NaN	NaN	NaN	NaN	NaN	0.1	0.6	NaN	NaN	...	0.2	NaN	NaN	0.1	NaN	NaN	NaN	0.2	NaN	NaN
3	Zm00001eb000040	NaN	NaN	NaN	0.5	NaN	NaN	0.5	NaN	NaN	...	NaN	NaN	NaN	0.1	0.1	NaN	NaN	0.3	NaN	0.1
4	Zm00001eb000050	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	0.2	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	Zm00001eb442870	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.2	NaN	0.3	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37840	Zm00001eb442890	NaN	NaN	NaN	NaN	NaN	0.2	NaN	NaN	NaN	...	0.1	NaN	NaN	NaN	NaN	NaN	NaN	0.1	NaN	NaN
37841	Zm00001eb442910	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37842	Zm00001eb442960	NaN	NaN	0.2	NaN	0.2	1.0	0.8	NaN	0.3	...	0.4	1.0	0.1	0.4	0.2	0.1	NaN	0.6	0.5	0.5
37843	Zm00001eb443030	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

37844 rows × 93 columns

Exploring the Leftover-Data

Data: After Sowing

after_sowing = results.filter(
    regex='days after sowing')
after_sowing

	root, 3 days after sowing	differentiation zone of primary root, 3 days after sowing	meristematic zone and elongation zone, 3 days after sowing	stele, 3 days after sowing	cortical parenchyma of root, 3 days after sowing	coleoptile, 6 days after sowing	primary root, 6 days after sowing	primary root, 7 days after sowing	root, 7 days after sowing	seminal root, 7 days after sowing	taproot zone 1, 7 days after sowing	taproot zone 2, 7 days after sowing	taproot zone 3, 7 days after sowing	taproot zone 4, 7 days after sowing
0	2.0	2.0	2.0	2.0	3.0	4.0	3.0	3.0	3.0	2.0	3.0	4.0	4.0	4.0
1	24.0	15.0	38.0	19.0	11.0	38.0	31.0	20.0	22.0	27.0	47.0	29.0	23.0	5.0
2	NaN	NaN	NaN	NaN	NaN	0.1	0.6	NaN	NaN	NaN	0.1	NaN	NaN	NaN
3	NaN	NaN	NaN	0.5	NaN	NaN	0.5	NaN	NaN	NaN	0.4	0.2	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37840	NaN	NaN	NaN	NaN	NaN	0.2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37841	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37842	NaN	NaN	0.2	NaN	0.2	1.0	0.8	NaN	0.3	0.3	NaN	NaN	0.2	NaN
37843	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

37844 rows × 14 columns

Calculate how many Genes where not expressed after sowing compared to Total

len_all = len(results.index)

after_sowing = after_sowing.dropna(axis=0, how='all')
len_sowing = len(after_sowing.index)
print(len_all- len_sowing)

Count how many Genes where expressed after sowing by days after sowing and Organism Part

after_sowing.count()

root, 3 days after sowing                                     24582
differentiation zone of primary root, 3 days after sowing     24787
meristematic zone and elongation zone, 3 days after sowing    22893
stele, 3 days after sowing                                    23762
cortical parenchyma of root, 3 days after sowing              25025
coleoptile, 6 days after sowing                               25759
primary root, 6 days after sowing                             25631
primary root, 7 days after sowing                             26315
root, 7 days after sowing                                     26103
seminal root, 7 days after sowing                             25064
taproot zone 1, 7 days after sowing                           23977
taproot zone 2, 7 days after sowing                           25058
taproot zone 3, 7 days after sowing                           25998
taproot zone 4, 7 days after sowing                           26809
dtype: int64

Data: After Pollination

after_pollination = results.filter(
    regex='days after pollination')
after_pollination

	seed, 2 days after pollination	seed, 4 days after pollination	leaf, 6 days after pollination	internode, 6 days after pollination	seed, 6 days after pollination	seed, 8 days after pollination	seed, 10 days after pollination	leaf, 12 days after pollination	internode, 12 days after pollination	seed, 12 days after pollination	...	seed, 22 days after pollination	plant embryo, 22 days after pollination	endosperm, 22 days after pollination	leaf, 24 days after pollination	internode, 24 days after pollination	seed, 24 days after pollination	plant embryo, 24 days after pollination	endosperm, 24 days after pollination	leaf, 30 days after pollination	internode, 30 days after pollination
0	5.0	6.0	14.0	11.0	5.0	4.0	4.0	12.0	6.0	3.0	...	2.0	5.0	0.9	9.0	7.0	2.0	5.0	0.7	10.0	6.0
1	42.0	40.0	0.9	2.0	30.0	29.0	28.0	0.6	4.0	41.0	...	25.0	84.0	15.0	0.7	1.0	23.0	68.0	14.0	0.8	1.0
2	NaN	0.2	0.4	NaN	0.2	NaN	0.1	NaN	NaN	NaN	...	0.1	NaN	NaN	0.3	0.6	0.2	NaN	NaN	0.1	NaN
3	0.2	0.1	NaN	NaN	0.1	0.1	0.1	0.1	NaN	NaN	...	NaN	NaN	NaN	NaN	0.1	NaN	NaN	NaN	0.1	0.1
4	0.1	NaN	NaN	NaN	0.1	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	0.2	NaN	NaN	0.2	NaN	0.3	NaN	NaN
37840	0.2	NaN	NaN	NaN	NaN	NaN	0.2	NaN	NaN	NaN	...	0.2	NaN	0.3	NaN	NaN	0.1	NaN	NaN	NaN	NaN
37841	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37842	2.0	3.0	0.4	0.3	3.0	2.0	2.0	0.2	NaN	0.8	...	0.4	2.0	0.2	0.1	0.2	0.4	1.0	0.1	0.4	0.2
37843	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	0.1	NaN	NaN	NaN	NaN	NaN	NaN

37844 rows × 35 columns

Calculate how many Genes where not expressed after pollination compared to Total

len_all = len(results.index)

after_pollination = after_pollination.dropna(axis=0, how='all')
len_pollination = len(after_pollination.index)
print(len_all- len_pollination)

Calculate how many more Genes where expressed compared to sowing

print(len_pollination - len_sowing)

Count how many Genes where expressed after pollination by days after pollination and Organism Part

after_pollination.count()

seed, 2 days after pollination             26907
seed, 4 days after pollination             27090
leaf, 6 days after pollination             26035
internode, 6 days after pollination        25390
seed, 6 days after pollination             26935
seed, 8 days after pollination             27191
seed, 10 days after pollination            27919
leaf, 12 days after pollination            25413
internode, 12 days after pollination       26289
seed, 12 days after pollination            25672
endosperm, 12 days after pollination       24969
seed, 14 days after pollination            26669
endosperm, 14 days after pollination       23174
seed, 16 days after pollination            25538
plant embryo, 16 days after pollination    24076
endosperm, 16 days after pollination       22740
leaf, 18 days after pollination            25620
internode, 18 days after pollination       25385
seed, 18 days after pollination            26032
plant embryo, 18 days after pollination    25016
endosperm, 18 days after pollination       24444
pericarp, 18 days after pollination        25738
seed, 20 days after pollination            26429
plant embryo, 20 days after pollination    24794
endosperm, 20 days after pollination       23508
seed, 22 days after pollination            26463
plant embryo, 22 days after pollination    24485
endosperm, 22 days after pollination       23009
leaf, 24 days after pollination            25702
internode, 24 days after pollination       25442
seed, 24 days after pollination            26432
plant embryo, 24 days after pollination    25769
endosperm, 24 days after pollination       22742
leaf, 30 days after pollination            26485
internode, 30 days after pollination       25341
dtype: int64

Compare Genes from Leaf by Days

after_pollination.filter(
    regex='leaf').count()

leaf, 6 days after pollination     26035
leaf, 12 days after pollination    25413
leaf, 18 days after pollination    25620
leaf, 24 days after pollination    25702
leaf, 30 days after pollination    26485
dtype: int64

Compare Genes from Endosperm by Days

after_pollination.filter(
    regex='endosperm').count()

endosperm, 12 days after pollination    24969
endosperm, 14 days after pollination    23174
endosperm, 16 days after pollination    22740
endosperm, 18 days after pollination    24444
endosperm, 20 days after pollination    23508
endosperm, 22 days after pollination    23009
endosperm, 24 days after pollination    22742
dtype: int64

Zwischenfazit zu den Ergebnis-Daten 🏁

Nach der Aussaat

Die Messungen der Genexpression wurden an mehreren Tagen durchgeführt. Jeweils 3, 6 und 7 Tage nach der Aussaat.

Die Probenentnahme 3 Tage nach der Aussaat erfolgte an folgenden Organismus-Teilen:

Wurzel
Differenzierungszone der Primärwurzel
meristematische Zone und Streckungszone
Stele
Rindenparenchym der Wurzel

Am 6. Tag nach der Aussat wurden folgende Teile untersucht:

Koleoptile
Primärwurzel

Einen weiteren Tag später fanden die letzten Messungen dieses Entwicklungsstadiums in folgenden Teilen statt:

Primärwurzel
Wurzel
Seminalwurzel
Pfahlwurzel Zonen 1 - 4

Nach der Aussaat wurden 7213 Gene weniger expremiert, verglichen mit allen Wachstumsstadien in Summe.
Mit Anzahl der Tage nach Aussaat stieg außerdem die Anzahl der expr. Gene.
Nach 3 Tagen lag die Anzahl bei 22893 - 25025, bei 6 Tagen bei 25631-25759 und bei 7 Tagen bei 23977-26809.
Es ist ein deutlicher Anstieg erkennbar. Unklar ist, ob dieser tatsächlich durch die Anzahl vergangener Tage und somit durch die Wachstumsphase bedingt ist, oder auf die unterschiedlichen Probenentnahmeorte zurückzuführen ist.
Zwei Proben wurden an verschiedenen Tagen in der selben Zone entnommen. Diese legen die Vermutung nahe, dass die höhere Anzahl der Tage nach Aussaat zu einer höheren Expremierung führt. Bei den Proben handelt es sich um die folgenden:

Tag	Primärwurzel	Wurzel
3		24582
6	25631
7	26315	26103

Nach der Bestäubung

Die Messungen wurden nach der Bestäubung jeweils im Abstand von 2 Tagen, bis Tag 30 durchgeführt. Mit fortschreitendem Wachstumsstadium wurden die Messungen auch an verschiedenen Orten durchgeführt. Getestet wurden z.B.:

Samen
Blatt
Internodium
Endosperm
Pflanzenembryo
Perikarp

Verglichen mit der Aussaat wurden 5136 Gene mehr exprimiert. Die Anzahl der exprimierten Gene scheint hier keinen direkten Zusammenhang mit der Anzahl vergangener Tage im Entwicklungsstadium zu haben. Dies lässt sich aber nicht sicher sagen, da an verschiedenen Orten die Proben entnommen wurden.
Die Proben der Blätter wurden im regelmäßigen Abstand von 6 Tagen genommen. Die Daten zeigen hier, dass es keinen linearen Zusammenhang gibt.

Tage nach der Bestäubung	Anzahl exprimierter Gene
6	26035
12	25413
18	25620
24	25702
30	26485

Um sicher zu gehen, dass dies nicht nur eine Eigenschaft der Blätter ist, habe ich zusätzlich die Daten des Endosperms tageweise gefiltert. Auch hier zeigt sich, dass es keinen linearen Zusammenhang gibt.

Tage nach der Bestäubung	Anzahl exprimierter Gene
12	24969
14	23174
16	22740
18	24444
20	23508
22	23009
24	22742

import numpy as np
from pandas import DataFrame
import seaborn as sns
# %matplotlib inline
results = results.fillna(0)
results.set_index('Gene ID', inplace=True)

endosperm_after_pollination = after_pollination.filter(regex='endosperm')
endosperm_after_pollination.columns = endosperm_after_pollination.columns.str.replace("days after pollination", "")
endosperm_after_pollination.columns = endosperm_after_pollination.columns.str.replace("endosperm, ", "")


seed_after_pollination = after_pollination.filter(regex='seed')
seed_after_pollination.columns = seed_after_pollination.columns.str.replace("days after pollination", "")
seed_after_pollination.columns = seed_after_pollination.columns.str.replace("seed, ", "")


leaf_after_pollination = after_pollination.filter(regex='leaf')
leaf_after_pollination.columns = leaf_after_pollination.columns.str.replace("days after pollination", "")
leaf_after_pollination.columns = leaf_after_pollination.columns.str.replace("leaf, ", "")

internode_after_pollination = after_pollination.filter(regex='internode')
internode_after_pollination.columns = internode_after_pollination.columns.str.replace("days after pollination", "")
internode_after_pollination.columns = internode_after_pollination.columns.str.replace("internode, ", "")

plant_embryo_after_pollination = after_pollination.filter(regex='plant embryo')
plant_embryo_after_pollination.columns = plant_embryo_after_pollination.columns.str.replace("days after pollination", "")
plant_embryo_after_pollination.columns = plant_embryo_after_pollination.columns.str.replace("plant embryo, ", "")

pericarp_after_pollination = after_pollination.filter(regex='pericarp')
pericarp_after_pollination.columns = pericarp_after_pollination.columns.str.replace("days after pollination", "")
pericarp_after_pollination.columns = pericarp_after_pollination.columns.str.replace("pericarp, ", "")


seed_after_pollination

	2	4	6	8	10	12	14	16	18	20	22	24
0	5.0	6.0	5.0	4.0	4.0	3.0	3.0	2.0	2.0	1.0	2.0	2.0
1	42.0	40.0	30.0	29.0	28.0	41.0	31.0	23.0	23.0	19.0	25.0	23.0
2	NaN	0.2	0.2	NaN	0.1	NaN	0.1	NaN	NaN	NaN	0.1	0.2
3	0.2	0.1	0.1	0.1	0.1	NaN	NaN	NaN	0.2	0.1	NaN	NaN
4	0.1	NaN	0.1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
37838	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37839	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.2
37840	0.2	NaN	NaN	NaN	0.2	NaN	0.5	NaN	0.2	0.1	0.2	0.1
37842	2.0	3.0	3.0	2.0	2.0	0.8	1.0	0.3	0.5	0.4	0.4	0.4
37843	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

35767 rows × 12 columns

Hauptkomponentenanalyse

from sklearn.decomposition import PCA
from sklearn import preprocessing
import matplotlib.pyplot as plt

scaled_data = preprocessing.scale(results.T)
pca = PCA()
pca.fit(scaled_data)
pca_data = pca.transform(scaled_data)

per_var = np.round(pca.explained_variance_ratio_ * 100, decimals=1)
labels = ['PC' + str(x) for x in range(1, len(per_var)+1)]

plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels)
plt.ylabel('Percentage of Explained Variance')
plt.xlabel('Principal Component')
plt.xticks(rotation=90)
plt.title('Scree Plot')
plt.show()

Ergebnis: Die ersten 2 Hauptkomponenten bilden nahezu ausschließlich die Varianzen ab

pca_df = pd.DataFrame(pca_data, columns=labels, index=results.columns)

plt.rcParams["figure.figsize"] = (15,15)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.title('PCA Graph')
plt.xlabel(f'PC1- {per_var[0]}%')
plt.ylabel(f'PC2- {per_var[1]}%')

pca_df
for sample in pca_df.index:
    plt.annotate(sample.partition(',')[0], (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]),  rotation=45)
    

plt.show()

Find Top 10 PCA seperating Genes

loading_scores = pd.Series(pca.components_[0], index=results.index)
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
top_10_genes = sorted_loading_scores[0:10].index.values
loading_scores[top_10_genes]

Gene ID
Zm00001eb059170   -0.012067
Zm00001eb246940   -0.012066
Zm00001eb216140   -0.012039
Zm00001eb077390   -0.012038
Zm00001eb423880   -0.012003
Zm00001eb301640   -0.011932
Zm00001eb077380   -0.011899
Zm00001eb232720   -0.011895
Zm00001eb395490   -0.011878
Zm00001eb285560   -0.011875
dtype: float64

Show the Dataframe rows with the top 3 Genes to prove the PCA

df = results.loc[["Zm00001eb059170", "Zm00001eb246940", "Zm00001eb216140"]]
df

	root, 3 days after sowing	differentiation zone of primary root, 3 days after sowing	meristematic zone and elongation zone, 3 days after sowing	stele, 3 days after sowing	cortical parenchyma of root, 3 days after sowing	coleoptile, 6 days after sowing	primary root, 6 days after sowing	primary root, 7 days after sowing	root, 7 days after sowing	seminal root, 7 days after sowing	...	seed, 24 days after pollination	plant embryo, 24 days after pollination	endosperm, 24 days after pollination	leaf, 30 days after pollination	internode, 30 days after pollination	thirteenth leaf, whole plant fruit formation stage 30 to 50%	thirteenth leaf, whole plant flowering stage	pre-pollination cob, whole plant flowering stage	anthers, whole plant flowering stage	silks, whole plant flowering stage
Gene ID
Zm00001eb059170	10.0	10.0	15.0	11.0	9.0	13.0	8.0	11.0	11.0	14.0	...	10.0	20.0	7.0	4.0	11.0	2.0	1.0	18.0	4.0	11.0
Zm00001eb246940	23.0	19.0	32.0	30.0	10.0	31.0	22.0	22.0	23.0	23.0	...	30.0	50.0	26.0	9.0	24.0	5.0	6.0	48.0	10.0	35.0
Zm00001eb216140	6.0	3.0	7.0	5.0	3.0	9.0	6.0	5.0	5.0	5.0	...	4.0	8.0	3.0	1.0	4.0	0.8	0.8	9.0	2.0	4.0

3 rows × 92 columns

Ergebnisse der PCA 🏁

Die ersten 2 Hauptkomponenten bilden nahezu ausschließlich die Varianzen ab (11-16% jeweils!) Alle anderen Hauptkomponenten zeigen Varianzen von unter 10%.

Die PCA zeigt deutliche Cluster der unterschiedlichen Pflanzenteile. Die Samenproben korrelieren deutlich miteinander. Auch die Endosperm- und Blatt-Proben bilden deutliche Cluster. Samen- und Endospermproben-Cluster liegen nahe bei einander, was suggeriert, dass diese Pflanzenteile sich ähnlich in ihrer Genexpression verhalten. Diese Cluster unterscheiden sich deutlich von den Blattproben. Der große Clusterabstand deutet an, dass sich die Genexpression in den Blättern deutlich anders verhält, als bei den Samen. Generell trennen sich die Blattcluster von allen anderen Probenorten deutlich ab.

Nach Untersuchung welche Gene die Cluster am stärksten trennen kamen folgende Ergebnisse heraus:

Diese 10 Gene bilden den größten Expressionsunterschied laut Hauptkomponentenanalyse. Sie unterscheiden sich damit am stärksten in den Blättern verglichen mit allen anderen Pflanzenteilen. Die Selektion dieser Spalten bestätigt die PCA zusätzlich. Die genannten Gene werden deutlich weniger innerhalb der Blätter als in allen anderen Pflanzenteilen exprimiert.

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.ipynb_checkpoints		.ipynb_checkpoints
E-MTAB-4342-atlasExperimentSummary.Rdata		E-MTAB-4342-atlasExperimentSummary.Rdata
E-MTAB-4342-experiment-design.tsv		E-MTAB-4342-experiment-design.tsv
E-MTAB-4342-query-results.fpkms.tsv		E-MTAB-4342-query-results.fpkms.tsv
E-MTAB-4342-query-results.tpms.tsv		E-MTAB-4342-query-results.tpms.tsv
README.md		README.md
output_73_0.png		output_73_0.png
output_75_0.png		output_75_0.png
projekt.ipynb		projekt.ipynb
projekt.md		projekt.md

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Life Science Computing

Datensatz

Inhaltsverzeichnis

Preparation

Imports

Load Data

Exploring and Processing the Experimental Data

Print all Columns

Check if Columns are equal

Delete Data

Delete all Columns with less than 2 different Values

Delete double Columns

Delete Sample Characteristic Ontology Columns

Delete useless Cols

Exploring the Leftover-Data

Zwischenfazit zu den Experiment-Daten 🏁

Kultursorte

Probenort / Organismus Teil

Entwicklungsstadien 🌱

Exploring and Processing the Results FPKM (Fragments Per Kilobase Million)

Print all Columns

Check if Columns are equal

Delete Data

Delete double Columns

Exploring the Leftover-Data

Data: After Sowing

Calculate how many Genes where not expressed after sowing compared to Total

Count how many Genes where expressed after sowing by days after sowing and Organism Part

Data: After Pollination

Calculate how many Genes where not expressed after pollination compared to Total

Calculate how many more Genes where expressed compared to sowing

Count how many Genes where expressed after pollination by days after pollination and Organism Part

Compare Genes from Leaf by Days

Compare Genes from Endosperm by Days

Zwischenfazit zu den Ergebnis-Daten 🏁

Nach der Aussaat

Nach der Bestäubung

Hauptkomponentenanalyse

Find Top 10 PCA seperating Genes

Show the Dataframe rows with the top 3 Genes to prove the PCA

Ergebnisse der PCA 🏁

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