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0:00

Introduction

1:30

Fixing the autoplot

3:30

Fixing the gate

6:10

Adjusting the gate

7:30

Getting help

9:30

Faceting

13:10

Gridding

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Flow cytometry

Rstats

flowCore

ggcyto

00:00:01

hello and welcome to the short

00:00:03

introduction how to analyze your flow

00:00:05

cytometry data in r

00:00:07

this episode is about gg cyto gt cyto is

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used to visualize your flow cytometry

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data

00:00:13

so previously we've looked at using auto

00:00:16

plot

00:00:17

and some quick plotting tools but now

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we'll have a look to see how we can have

00:00:21

some more control over the visual

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visualization so i've loaded the same

00:00:25

data as we had previously

00:00:27

and we will have a quick look at it now

00:00:29

so i've

00:00:30

used the code from the other videos

00:00:35

and now we just have a quick look at it

00:00:37

to confirm what it is

00:00:40

so we can see it's a flow set of nine

00:00:42

experiments i've taken the first nine

00:00:43

files

00:00:46

from the f4 fcmzv

00:00:49

full repository um set which is um

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i think it's flowcap3 i can use

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fs apply and look at the file names

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i can look at the column names i.e the

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floor rules

00:01:04

i can see there's 12 of them

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and i can look at the gating hierarchy

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i've produced

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just here so this is the same as we did

00:01:14

the other day

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and we can look at the auto plot outputs

00:01:18

so this is just using the default

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settings

00:01:21

and we'll show the foreign side scatter

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the single gating and

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the um was a 50 versus p texas red

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so one of the issues with the auto plot

00:01:34

is that you don't have any control over

00:01:35

where the labels are

00:01:36

on the scaling necessarily um or

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of the layout in general it just creates

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a quick plot like you can see above my

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head

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so let's first of all fix this plot so

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we want to fix the scaling

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and we want to add gate names and we

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probably want to adjust the positions of

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the gates

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so to do this we'll be using ggcyto now

00:01:58

before we start with ggcyto

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i should point out that you need to look

00:02:02

up ggplot so ggcyto

00:02:04

is a visualization program that's based

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on another visualization programming

00:02:08

called ggplot2 now

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gg plot 2 is very useful very powerful

00:02:15

and you won't get a lot out of gg cyto

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with the one for plus temperature

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without understanding how gd plot

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2 works the easiest way to do this

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is to just google ggplot2

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tutorials and you'll see

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a lot of these tutorials

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sadly i think my work there we go

00:02:42

so if we go to the tidyburst which is

00:02:44

its official web page i assume

00:02:48

it gives you some basic usage and

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some learning points um the first one is

00:02:54

very good

00:02:56

it goes through this is the an online or

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book

00:03:00

and you can use some default data loaded

00:03:02

into r and teach you how to do some

00:03:05

dot plots how to change the points how

00:03:08

to change the colors

00:03:10

again if you go back to google there's

00:03:12

lots of other ones including the

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complete course and i would advise looking

00:03:17

through this if you want to do any data

00:03:18

visualization and all

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you basically need to know how to use

00:03:22

ggplot2

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okay so let's pretend you've looked at

00:03:27

ggplot2

00:03:30

and we're now going to look at gigi cyto

00:03:32

so we want to fix this plot that's above

00:03:35

my head

00:03:37

so the nomenclature the way you use

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gigi cyto is the same as 3d plot

00:03:43

and so we're going to call our plots p

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we're going to load

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gg cyto we're going to load our gate

00:03:50

instead which is called auto gs so i'm

00:03:52

looking at line 16 here

00:03:54

aes stands for aesthetics and that is

00:03:57

what we want the plots we want to plot

00:03:58

the x axis as

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x fit c area the y axis is xp texas red

00:04:03

area

00:04:04

and the subset is singlets

00:04:08

so press ctrl enter it loads you don't

00:04:11

see anything

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if i were to type in p you will be able

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to see what is produced which is just an

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empty plot

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because i've just produced an empty pot

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i haven't asked it to put any data in

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so into p i will be adding geometric hex

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um points and the binning is 256 so

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similar to what we do with the order

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plot

00:04:33

and again if i go to p now i'll see i've

00:04:35

now got some data

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very nice

00:04:42

i can add some getting so i'm using

00:04:44

geometric gate

00:04:46

gating set to get population paths four

00:04:49

to seven

00:04:50

so i know it's four to seven because of

00:04:52

what we did earlier

00:04:53

the last one the last video and

00:04:56

the geometric gate is the way of gg's

00:04:59

height of finding gates

00:05:01

so i've done that and now if i press p i

00:05:03

should hopefully see the gate appear

00:05:07

fantastic and i want to add some

00:05:10

statistics so i've got geometric

00:05:12

statistics

00:05:13

and i get the paths again and i choose

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percentage

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and the gate name and i do this

00:05:20

adjusting so let's let's remove this

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adjusting for now

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and look at p again

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and now we've got the percentage gates

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at those percentages on the gates which

00:05:36

is fantastic

00:05:38

it would be nice to move them slightly

00:05:40

so i could read that adjust

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so we have the xy coordinates and we can

00:05:45

we can get a nudge them left and right

00:05:47

as as we need to so let's see what this

00:05:50

does now this is actually going to do an

00:05:51

overlay of an overlay

00:05:53

because i've reapplied it to the same

00:05:55

plot so i'm going to get

00:05:57

multiple hits which isn't perfect so i

00:05:59

can start again by just going up to the

00:06:00

top one

00:06:04

ctrl enter ctrl enter control enter

00:06:06

control enter

00:06:08

and we can look at p and the gate is

00:06:11

uh the gate labels in a slightly more

00:06:13

sensible place

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i'm reasonably happy with that you can

00:06:16

spend forever adjusting these and you're

00:06:18

more than welcome to do so

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so this plot doesn't look too bad

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um but this the the scaling could be

00:06:28

worse

00:06:29

so to adjust the scaling use something

00:06:31

called my powers which is my parameters

00:06:33

so you can use gt cytoparameter set and

00:06:36

you can choose the limits of the x and y

00:06:38

so the limits are currently three and

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five for the y and three and five

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for the x

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which which to be honest it looks pretty

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good to me

00:06:49

so if i try that so i create the

00:06:51

variable called my powers

00:06:52

then i apply my powers to p

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they can load p again and hopefully

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we'll see a slightly different shape

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there we go maybe a bit too much in the

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right now

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but as you saw with the auto plotting it

00:07:07

could be very wrong

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so this is something to pay attention to

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and the labels are terrible

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because these are our safe um name name

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types which have the dots and no spaces

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no slashes

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so i'm just going to relabel them so i'm

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going to use the p plus labs

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x is 84 y cd3

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i'll load it again and then we got

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a nice nice plot if you need any

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assistance

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or any help the same is true for

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anything in

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um r you require type a question mark

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and type in type in gd plot or my sorry

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gdcito

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i've been saying gdpr too much

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and you'll get the help file on the

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right i'm talking about how gt

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cyto works so example

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mr code and of course again you can go

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to the internet

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and type in gg title

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and you'll be directed to the the

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bioconductor page

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so the github and you'll find lots of

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lots of help

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um it's actually been moved to something

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called the cytoverse

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which has been set up to um to keep

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consistency between a lot though the

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more popular flower citation packages

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and so if you come here you can go to

00:08:28

examples

00:08:31

ggcyto and you've got lots of help files

00:08:40

and you can scroll down and look at

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different ways of interacting with gg

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cyto

00:08:45

but we'll carry on ourselves

00:08:48

so we had we had a single plot here

00:08:52

if we wanted to do similar to auto plot

00:08:54

and have all the plots

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we can just remove the subset so we were

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looking at just one file

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we can do the same but with the entire

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flow set or gating set in this case

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and we should get a nice multi-plot

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image with all of the types so you see

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it's a bit of a mess now so we'd need to

00:09:20

change something so we can change the

00:09:21

font size and the positioning

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or we could always just make the plot

00:09:26

bigger in general and would look a bit

00:09:27

better

00:09:32

but there's a lot of power and a lot of

00:09:33

flexibility and and

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what you have to do is look through the

00:09:36

help files and find out

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all the different things you can do such

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as changing the colors changing the

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densities

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um changing the axis changing the titles

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and one of the things you can do is

00:09:49

something called facetime so

00:09:50

faceting is a way of sub categorizing

00:09:54

your plots

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or your data in general and this um

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takes about something called the phenol

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data so the phenotype is a

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is some as a extra bit of data in

00:10:04

flow core on the floor set so if i type

00:10:07

in p data and auto gs auto gsb in my

00:10:10

gating set i can see my pheno data is

00:10:13

actually empty all it has

00:10:14

in the bottom left here are the file

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names

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however in the download from flow

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repository there's also a csv

00:10:23

file that csv file contains lots of

00:10:25

useful information such as

00:10:27

patient and treatment so what i'm going

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to do is load that csv file so i've got

00:10:32

csv file read csv and this is the csv

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file

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from flow repository

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and if we i can look at the csv file

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and we can see we've got file name um

00:10:48

treatment group and what what they're

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looking for and they said this is from

00:10:51

floccaps3 as i said

00:10:53

and what we need to do is we need to

00:10:54

merge our phenotype

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data in a flow set with a csv file

00:11:01

so to do this we use the merge command

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the merge command is just

00:11:05

a default r command and you can find

00:11:08

the instructions in the help file

00:11:14

merge two data frames so what we're

00:11:17

doing is we're going to merge

00:11:18

our final data from our gating set with

00:11:22

the csv file

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we're going to merge them using the name

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and the fcs file

00:11:27

name so the fss file name is from

00:11:31

the the flow set and the name is from

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the

00:11:35

cs3 panel oh actually it's the other way

00:11:37

around

00:11:39

so now i'll create something called new

00:11:40

data and if i open that

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it's now just my nine files now we have

00:11:47

the treatments associated with them the

00:11:48

sample description of the patient name i

00:11:50

assume

00:11:51

and the characteristic which is all part

00:11:52

of the training set

00:11:55

um there is one caveat is that for the

00:11:58

the female data you must

00:11:59

the role names must be the name of the

00:12:01

fcs file

00:12:03

so when you do the merge it don't really

00:12:05

removes yet this raw data

00:12:07

no name data so we'll have to apply it

00:12:09

again using this row names

00:12:11

command and then we tell

00:12:15

that our gate except for phenotype data

00:12:17

should be this new

00:12:19

um a data set we've made or um data

00:12:22

frame sorry

00:12:25

and there we go so we've now got

00:12:27

phenotyping data with the

00:12:29

file name the treatment the sample

00:12:31

description

00:12:32

and this is really useful because this

00:12:34

means we can start sub categorizing our

00:12:35

plots

00:12:36

so here i'm making plots called pt using

00:12:39

gdcyto

00:12:40

so i'm just going to do cd4 and cd8 and

00:12:42

the singlets

00:12:46

i will do a geometric hex exactly the

00:12:48

same as before

00:12:50

and i'm going to choose the my

00:12:51

parameters i know will work for this

00:12:54

but what i'm going to do now is

00:12:56

something called facet gridding

00:12:57

so i'm going to plot my plots

00:13:00

of sample treatment versus sample

00:13:02

description

00:13:05

and you'll see how useful this is in a

00:13:06

second

00:13:12

and so here we've got my nine plots my

00:13:15

cd4 versus cd3

00:13:17

but what i have here is the sample

00:13:20

description which is probably the

00:13:21

patient and the treatment or the

00:13:25

uh what what gene they're looking for i

00:13:27

don't know a huge amount about this data

00:13:28

as you can probably tell

00:13:30

but this can be very useful because you

00:13:32

can imagine having treatment versus

00:13:34

patient or responder versus

00:13:36

non-responder

00:13:37

or cell type versus versus gene

00:13:40

and this way you can very nicely

00:13:43

visualize your data

00:13:44

and of course you can then add gates to

00:13:45

this and the statistics and you'll find

00:13:48

this very useful but you just have to work

00:13:50

make sure that you've got some good

00:13:52

um annotated data to start with

00:13:56

a very quickly look at back gating so

00:13:59

this is how you put a pretty color on

00:14:01

top of another plot to show the

00:14:03

the gate so we'll call this p3 gg cider

00:14:06

again cd3 versus cd4

00:14:08

and the subset is root so with gigi cyto

00:14:10

with a gatling set you always have to

00:14:12

have

00:14:13

a gate and so this is the root so this

00:14:15

is the top gate this is the no gate

00:14:19

and geometric hex again because it looks

00:14:21

good but i'm going to do 128

00:14:23

pins for no particular reason limits

00:14:26

again i know this works

00:14:29

and i'm going to do something called now

00:14:31

949 geometric overlay

00:14:34

so i'm going to overlay my cd3 and cd4

00:14:36

plus

00:14:37

plus gate and so this is my gate from my

00:14:39

gating set

00:14:41

and i want to overlay with dots the size

00:14:43

of 0.01

00:14:45

and the alpha which is the opacity or

00:14:46

how transparent it is at 0.05

00:14:49

and the color orange if you're american

00:14:52

you're more than welcome to get rid of

00:14:53

the u

00:14:54

and you're probably going to want the

00:14:56

alpha this opacity to be quite

00:14:58

low and so you can see the density

00:15:00

underneath

00:15:02

this is going to take a little while on

00:15:04

my computer um

00:15:06

i'm not sure why so let's just fast

00:15:08

forward the video

00:15:11

alright you can see that what has

00:15:12

appeared above my head now is um

00:15:14

a nice little orange back gate onto the

00:15:17

the cd4 and uh cd

00:15:19

three positive population onto my

00:15:21

platform earlier

00:15:23

um and there's obviously a lot more

00:15:24

complexity of the gtg title than just

00:15:26

doing these

00:15:27

simple plots um and the power is you can

00:15:30

do this over thousands of iterations

00:15:32

and create very consistent plot types

00:15:36

um if you want to export them by the way

00:15:38

you can just use this command called

00:15:39

ggsave

00:15:41

so i'm going to save gg save plot one

00:15:44

and we use p2 which is the one we did

00:15:46

earlier which is the faceted nine plot

00:15:51

image and if i go to my files

00:15:55

i'll be able to load top one

00:15:58

and i can then see the one i saved

00:16:02

for help as ever you can use gtc

00:16:05

question mark

00:16:06

and you can realize you can change the

00:16:07

dpis the size the file types

00:16:10

um and of course you can always run this

00:16:12

into loops

00:16:14

and default loops onto functions and so

00:16:16

you can

00:16:17

do this over and over again i strongly

00:16:20

recommend doing a gg plot tutorial and then

00:16:24

looking at the help files for gigi

00:16:25

saturn

00:16:27

alright thank you i hope this has been

00:16:29

of help and

00:16:30

i look forward to seeing you again soon

00:16:33

goodbye

Description:

This video will cover creating plots using ggcyto and will briefly discuss metadata and pData. R Script: https://github.com/hally166/Cytometry-R-scripts/blob/master/ggcyto_tutorial.R Data used: https://flowrepository.org/id/FR-FCM-ZZZV ggcyto: https://www.bioconductor.org/packages/release/bioc/html/ggcyto.html ggplot2: https://ggplot2.tidyverse.org/ Cytoverse: https://cytoverse.org/ https://www.babraham.ac.uk/science-facilities/flow-cytometry

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How can I save a frame from a video "(4) Flow Cytometry Data Analysis in R: Visualisation"?

This feature is available in the UDL Helper extension. Make sure that "Show the video snapshot button" is checked in the settings. A camera icon should appear in the lower right corner of the player to the left of the "Settings" icon. When you click on it, the current frame from the video will be saved to your computer in JPEG format.

What's the price of all this stuff?

It costs nothing. Our services are absolutely free for all users. There are no PRO subscriptions, no restrictions on the number or maximum length of downloaded videos.