Exercises 4 - Pandas and connectomics

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

Exercise 4.1: Make a pandas DataFrame

You are given three lists representing data in a contact book (names, birthdays, phone numbers).

1. Turn the contact the book into a DataFrame with the names as index and two columns - birthday and phone number.

2. Access the data in the DataFrame to print the phone number and birthday of Alan.

3. Access the data in the DataFrame to print all phone numbers.

4. Access the data in the DataFrame to print the Yolanda’s birthday.

5. Save the phone book as a csv file

names = ['Alice', 'Yolanda', 'Tom', 'Francoise', 'Alan']
phone_numbers = [432246234, 911, 984703, 123156, 190998010]
birthdays = ['1.3.2001', '4.8.2004', '17.9.1998', '24.12.2012', '8.11.1935']

# your solution here

birthday    8.11.1935
phone       190998010
Name: Alan, dtype: object
Alice        432246234
Yolanda            911
Tom             984703
Francoise       123156
Alan         190998010
Name: phone, dtype: int64
911

Exercise 4.2: Make a DataFrame 2

You are given a dictionary with element names and their atomic numbers.

1. Make a DataFrame with the element names as index and the atomic numbers as a column.

2. Access the data in the DataFrame to print the atomic number of gold.

3. Save the DataFrame to an excel file.

atomic_numbers = {'zinc': 30, 'gold': 79, 'vibranium': 1000, 'radium': 88}
print(atomic_numbers)

# your solution here

{'zinc': 30, 'gold': 79, 'vibranium': 1000, 'radium': 88}
zinc           30
gold           79
vibranium    1000
radium         88
Name: atomic number, dtype: int64
atomic number    79
Name: gold, dtype: int64

Exercise 4.3: Connectome analyses using pandas

Johnston’s Organ Neurons (JONs) are antennal mechanosensory neurons that occur in 6 subtypes (A-F) with different preferences for antennal movements. The JON A and B subtypes respond to fast antennal vibrations induced by sound, and hence act as the fly’s auditory receptor neurons. Other JON subtypes respond to other features of antennal movement, for instance, JON C and E encode antennal position.

In this exercise, we will use the connectome to:

1. Determine the number of neurons in each JON subpopulation (JO-A … JO-F).

2. Determine whether a neuron that triggers startle and escape responses to odors, visual and acoustic stimuli is directly connecting to JONs.

Exercise 4.3a: Loading and inspecting the data

But first, let’s load the data. They are saved in two csv files:

• ‘dat/flywire/classification.csv.gz’: Contains information on all cells (including cell types)

• ‘dat/flywire/connections.csv.gz’: Synaptic connections and transmitter types

Load them and familiarize yourself with the different columnes:

# your solution here

	root_id	flow	super_class	class	sub_class	cell_type	hemibrain_type	hemilineage	side	nerve
0	720575940627005443	intrinsic	optic	L1-5	NaN	L4	NaN	NaN	right	NaN
1	720575940615995398	intrinsic	optic	L1-5	NaN	L4	NaN	NaN	right	NaN
2	720575940621762567	afferent	ascending	AN	NaN	NaN	NaN	NaN	right	CV
3	720575940624384007	afferent	sensory	olfactory	NaN	NaN	ORN_VA1v	NaN	left	AN
4	720575940614422540	intrinsic	central	NaN	NaN	NaN	AOTU032,AOTU034	LALa1_posterior	right	NaN
...	...	...	...	...	...	...	...	...	...	...
127974	720575940621500404	intrinsic	visual_projection	NaN	NaN	NaN	LLPC1,LLPC2a,LLPC2b,LLPC2c,LLPC2d,LLPC3	NaN	right	NaN
127975	720575940651646966	intrinsic	central	NaN	NaN	NaN	NaN	LB0_anterior	center	NaN
127976	720575940630151162	intrinsic	optic	optic_lobes	NaN	NaN	NaN	NaN	left	NaN
127977	720575940629364731	intrinsic	optic	optic_lobes	NaN	NaN	NaN	NaN	right	NaN
127978	720575940623859711	afferent	sensory	visual	NaN	R1-6	NaN	NaN	right	NaN

127979 rows × 10 columns

	pre_root_id	post_root_id	neuropil	syn_count	nt_type
0	720575940596125868	720575940608552405	LOP_R	5	ACH
1	720575940596125868	720575940611348834	LOP_R	7	ACH
2	720575940596125868	720575940613059993	LOP_R	5	GLUT
3	720575940596125868	720575940616986553	LOP_R	5	ACH
4	720575940596125868	720575940620124326	LOP_R	8	ACH
...	...	...	...	...	...
3794610	720575940660868737	720575940607206786	ME_L	9	GABA
3794611	720575940660868737	720575940608664873	ME_L	6	GABA
3794612	720575940660868737	720575940611462242	ME_L	6	GABA
3794613	720575940660868737	720575940622913063	ME_L	23	GABA
3794614	720575940660868737	720575940626553546	ME_L	6	ACH

3794615 rows × 5 columns

Exercise 4.3b: How many JONs exist per subtype?

For each of the 6 JON subtypes (A, B, C, D, E, F) find the number of neurons instances in the connectome and plot the number of neurons for each subtype as a bar plot (one bar per subtype, bar height indicates number of neurons for that subtype).

Roughly like so:

# your solution here

Exercise 4.3c: Are JONs connected to the giant-fibre neuron? If so how many neurons per subtype?

The giant-fibre neuron (GFN) is a large neuron that triggers startle and escape responses to different modalities - odors, visual stimuli but also sudden loud sounds. The giant fibre has an axon with a large diameter and is directly connected to motor neurons in the ventral nerve chord to rapidly trigger escape.

Is it also directly connected to sensory neurons, in particular JONs?

1. Find the GFN in the connectome - there should be two, one on each side.

2. For each of the GFNs, identify it’s inputs, and determine how many of each JON subtypes provide direct input to it. Plot this as a bar plot.

Hint The cell type of the GFN is “DNp01” (DN=Descending Neuron)

FInd the GFNs in the connectome:

# your solution here

	root_id	flow	super_class	class	sub_class	cell_type	hemibrain_type	hemilineage	side	nerve
13301	720575940622838154	efferent	descending	DN	NaN	DNp01	Giant Fiber	putative_primary	left	CV
122666	720575940632499757	efferent	descending	DN	NaN	DNp01	Giant Fiber	putative_primary	right	CV

For each of the GFNs, identify it’s inputs, and determine how many of each JON subtypes provide direct input to it. For each of the GFNs, plot the number of JONs of each subtype in the inputs as a bar plot.

Roughly like so:

# your solution here

Exercise 4.3d: Bonus: Plot the synapse count for each of the JON inputs to the GFN

# your solution here

Exercise 4.4: Groupby (Bonus)

In the lecture, we used groupby and aggregate to compute the mean response for each animal.

Extend the code from the lecture by computing not only the mean but also the standard deviation for each animal, and plotting them using error bars.

df = pd.read_csv('dat/pd_groupby.csv')
display(df.head())

# your solution here

	animal	pre	post
0	0.0	2.211986	3.107796
1	0.0	2.204871	2.830730
2	0.0	2.560633	3.483980
3	0.0	1.476412	2.801709
4	0.0	3.662519	3.447005