In January 2018 our lab purchased a modest but good enough espresso machine and coffee grinder. The following table shows the coffee beans that we have used so far to make espresso in our lab.

NoCoffeeStore & PriceYear
1GO'MORRON
by
KAFFA ROASTERY
(Finnish)
Contributed
by
MF
2018

2Lavazza
medium
roast
Espresso
(100% arabica)
7.95 eu
per
500 gr
From Prisma*
2018
3Salimbene
Superbar
caffe
(80% arabica and 20% Robusta)
22,90 eu
(+ 6,50 eu per post)
per
1 Kg
From
www.espresso-international.com
2018
4A NANNINI
Espresso
Classica
(55% Arabica and 45% Robusta)
9,90 eu
(+ 6,50 eu per post)
per
500 gr
From
www.espresso-international.com
2018
5Espresso 7
30% Robusta
by
Cafetoria roastery
located in Helsinki
28 eu
per
1 Kg
from
Cafetoria roastery
2018
6Caffe Venezia
100% Arabica
from
Netherlands
9.95 eu
for 1 KG
from
Kmarket
in Helsinki
2018
7Dromans
Kenyan cofee
Dark roast
Souvenir from
Kenya by
MA
2018
8Habanero
(mild flavor)
17.5 euros
per 500 grams
or 35 euros
per 1 kilo,
Bought from
Robert's cofee
in Redi
2019
9Original 70/30 Gourmet Supreme espresso
(Medium roast,
70% Arabica
30%Robusta)
4 euros
per 100 gr
Bought From Mocca Mate,
Hakaniemen kauppahalli
2019
10Brasil Bourbon estate35 euros
per 1kilo

Bought From Stockmann downtown
2019
11Arvid Nordquist - Reko Helä Bönor


100% Arabica
5.49 euros
per 450 grams

Bought from Viikki's Prisma
2019
12Illy 100% arabica9.59 euros
per 250 grams

Bought from Kmarket in downtown
2019

* The shops mentioned in the table are either located in Helsinki or are European websites that do post products to Helsinki.
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In this post I show how groupScatterPlot(), function of the rnatoolbox R package can be used for plotting the individual values in several groups together with their mean (or other statistics). I think this is a useful function for plotting grouped data when some groups (or all groups) have few data points ! You may be wondering why to include such function in the rnatoolbox package ?! Well ! I happen to use it quit a bit for plotting expression values of different groups of genes/transcripts in a sample or expression levels of a specific gene/transcript in several sample groups. These expression value are either FPKM, TPM, LCPM, or PSI values (Maybe I should go through these different normalizations later in a different post 😐!). But of course its application is not restricted to gene expression or RNAseq data analysis.

For the test, I first generate a list with three random values. The values are generated randomly using normal distribution, featuring different means and standard deviations.

library(rnatoolbox)
datList<- list(
  l1=rnorm(n=30, mean = 10, sd = 3),
  l2=rnorm(n=20, mean = 0, sd = 1),
  l3=rnorm(n=25, mean = 10, sd = 1)
)


Then I plot the grouped values. By default the mean function is used to add a summary for the values. However, other functions (e.g. median) can be defined as the FUN parameter.


png(
  "/proj/pehackma/ali/test/test_rnatoolbox/test_groupedScatterPlot_3.png",
  width=500, height=500, pointsize=21)
groupScatterPlot(l=datList, col=rainbow(3),
                 lty=1, lwd=1.5,
                 ylab="Test values")
dev.off()



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I am a Postdoc researcher at the Neuromuscular Disorders Research lab and Genetic Determinants of Osteoporosis Research lab, in University of Helsinki and Folkhälsan RC. I specialize in Bioinformatics. I am interested in Machine learning and multi-omics data analysis. My go-to programming language is R.
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