Transcript Slide 1

Figures and Tables
For Your Publication
How to Present Your Data
Do you have enough data to
write a paper?
• Does your data tell a story?
• Does it support a main point?
• Prior to completing your experiments, make
sure you anticipate how many data points you
will need to support your main point
– Anticipate appropriate statistical analyses
– Anticipate how you will show your data (table/figure)
Do you have enough data to
write a paper?
• Start with organizing your data into
figures and tables.
• Most publications have 5 figures.
Deciding what data to use
• Ethically what can you do when some
samples have problems?
• Issues of technique versus outlying
values versus inconsistent results.
• When were samples collected?
• When were samples processed?
Figure Options
1) Pictorial presentation
• Graph
Data in connected series
• Chart
Data in separate series
• Picture
Must be seen (Photos)
• Diagram
Model to show concepts
2) Tables
Data in an array
What is the most effective
way to display your data?
• What type of data do you have?
– Microarray data?
• Results of hundreds of genes must be separated
into tables or charts to make any sense.
– A lot of data points?
• Results from 500 samples must be summarized
in a way that is easy to understand.
• Try to use a variety in the types of figures.
– Boring to have 6 identical type of graphs.
Tables or graphs?
300
Table 2. Blood glucose
levels
Breakfast
Lunch
Dinner
250
Diabetic
Time
(hour)
Normal
(mg/dl*)
Diabetic
(mg/dl)
midnight
2:00
4:00
6:00
8:00
10:00
noon
2:00
4:00
6:00
8:00
10:00
100.3
93.6
88.2
100.5
138.6
102.4
93.8
132.3
103.8
93.6
127.8
109.2
175.8
165.7
159.4
72.1
271.0
224.6
161.8
242.7
219.4
152.6
227.1
221.3
* decaliters/milligram
Blood
200
Glucose
Level
150
(mg/dl)
100
Normal
50
0
12:00
6:00 am
12:00
6:00 pm
12:00
Hour
Figure 11. Blood glucose levels over time
for normal individual and diabetic
subjects
Graphs are
often easier to
understand
Each table or figure should
stand alone
• Understandable by itself
– Should not have to read the text
– Its title and descriptions should be enough
Ref: V. McMillan
Tables
• Use a table to present many numerical
values
• Don’t pack in too much information!
• Don’t include columns that have the
same value throughout. You can
include that information in a caption or
in the text.
Ref: V. McMillan
Not all data should be in the
table
Table 3. Indicator Bacteria and Viral Results
Water
Sample
Date
Total Coliform
(MPN/100ml)
Fecal Coliform Enterovirus
(MPN/100ml)
Del Mar1
10/02/05
1000
200
+
-
Del Mar2
11/04/05
800
260
-
-
Del Mar3
12/14/05
1200
300
+
-
Tijuana1
10/08/05
1500
400
+
-
Tijuana2
11/09/05
1300
320
+
-
Tijuana3
12/12/05
1050
280
-
-
Abbreviations: MPN, most probable number; HAV, Hepatitis A Virus
+/- indicates the presence or absence of virus as detected by RT-PCR
HAV
Table format
• Columns and rows
– Organize a table so that the similar items
read down, not across
• Table title
• Footnotes
• Look at the format in other papers as a
guideline
Ref: V. McMillan
Table footnotes
• Footnotes are BRIEF explanations about data
including
- Exceptions
- Abbreviations
- Statistics
- p-values for data were >0.05.
• Do not write out information that belongs in
the results!
Table example
Table 1. Detection of Infectious DEN2 in Tissues by Indirect Plaque Assays
Mice
strains
Serum
Liver
Spleen
Lymph
nodes
Brain
Spinal
cord
Day 3 p.i.a
WT129
-
-
-
-
-
-
A129
+
+
+
+
+/-
+/-
G129
-
-
-
-
-
-
AG129
+
+
+
+
+
+
WT129
-
-
-
-
-
-
A129
-
-
-
-
+/-
+/-
G129
-
-
-
-
-
-
AG129
-
-
-
-
+
+
Day 7 p.i.a
Abbreviations: p.i.= post-infection
+Virus was detectable.
-Virus was undetecable.
+/-Virus was detected in only some mice or some experiments.
aMice were intravenously inoculated with 10 8 PFU of DEN2, PL046 strain. At Day 3 p.i., serum and
tissues from various sites were harvested and processed for indirect plaque assay. Each group
consisted of 3-5 mice per time point. This experiment was performed 3 times, and similar results
were obtained in all experiments.
Figure format
• Different types
– Graph
– Chart
– Picture (gels, flow cytometry)
– Diagram
• All have
– Figure Legend (title and a brief description
of experiment)
Four parts to figure legends
1. Title
•
One sentence to identify the main point of the figure.
2. Brief experimental details
•
Enough details so that the reader can understand figure.
3. Definitions
•
Symbols or bar patterns that are not explained in figure.
Antigen present
Control
4. Statistical information
•
Number of samples, p-values, etc.
Ref: V. McMillan
Graphs
• Use to show a trend or pattern
• Generally a graph is not necessary
when trends or relationships are not
statistically significant
• If a cause and effect relationship:
– X axis is the independent variable
– Y axis is the dependent variable
Ref: V. McMillan
Graphing a small study
• Beware of presenting a small amount of data
in graphs
– Showing data in graphs can be a dramatic and
effective way to show an effect or trend but if your
data set is too small you can’t say that you are
seeing a potential trend or a real effect. So,
showing it in a graph can be misleading.
• If you do use a graph with a small number of
samples, clearly state how many data points
you used
Response to Amoxicillin
70%
Percentage of Mice
60%
50%
40%
30%
20%
Incorrect if N= 3 mice!!
10%
0%
No Response
Response
Figure 1. Percentage of mice that responded to amoxicillin treatment.
Three mice were treated with 0.5 mg/ml amoxicillin for 7 days.
Use of a best-fit line
•Direct
correlation
•Assumption that
the y-axis
endpoint is
dependent upon
the x-axis
variable
Fig 5. A comparison of the detection of anti-dengue virus IgG in the 12 serum samples is
shown by ELISA in OD plotted against the percentage of detection by sensor chip. This
percentage is the number of sensors reporting the presence of a bead as denoted with a
relative signal magnitude (RSM) above the detection threshold. Dengue virus antigen was
used to coat microtiter and sensor ship wells. This comparison demonstrates a strong
association between each OD value and its corresponding sensor percentage, with a squared
correlation coefficient (R2) of 0.966.
No best-fit line
• Plot points only
• Either variable
could be on the
x-axis
• No assumptions
are being made
about which
variable is
independent
Ref: V. McMillan
Other Types of Figures:
A.
100
WT129
% Survival
80
G129
AG129
60
(n = 30)
(n = 21)
(n = 22)
p=0.0171
40
20
0
p<0.0001
.
0
5
10
15
20
25
Days Post-Infection
30
Figure 1. Survival of mice lacking the IFN and/or  IFN receptor genes following
infection with dengue virus. A wild type mouse strain (WT129), a transgenic strain deficient
for the gamma IFN receptor only (G129), as well as a transgenic strain deficient for the alpha/beta
and gamma IFN receptor genes in combination (AG129) were infected with 108 PFU of dengue
virus (DEN2). Survival over time was determined. The statistical significance (p-values) of the
differences in survival between the transgenic and wildtype strains are provided.
Gels
RNA Inhibitor Concentration
100mM
50mM
5DLuc3D
5DLuc
+
+
M
+
250mM
+
+
500mM
+
+
+
97.4
MW
KDa
66.2
45.0
31.0
*
*
21.5
14.4
Figure 1. Effect of dengue virus 5' and 3' UTRs on protein translation in the presence and
absence of inhibitor. Luciferase reporter constructs containing both the dengue virus 5' and 3' UTR
(5DLuc3D) or the dengue virus 5’UTR only (5DLuc) were transfected into cells. Cells were subsequently
exposed to various concentrations of an RNA inhibitor. Cell extracts were harvested and analyzed by
SDS-Gel electrophoresis followed by Coomassie blue staining. Luciferase protein products of 28 Kda
and 43 Kda are noted with an *.
Pie-charts
UP-REGULATED 30 MIN
1%
6%
DOWN-REGULATED 30 MIN
14% 10%
10%
23%
3%
2%
2%
11%
5%
30%
6%
13%
2%
3%
2%
0%
1%
7% 5%
3%
4% 0%
2% 0%
2%
3%
4%
METABOLISM
1%
ENERGY
CELL CYCLE & DNA PROCESSING
CELL FATE
TRANSCRIPTION
SUBCELLULAR LOCALIZATION
PROTEIN SYNTHESIS
PROTEIN ACTIVITY REGULATION
PROTEIN FATE
TRANSPORT FACILITATION
CELLULAR TRANSPORT & TRANSPORT MECHANISMS
CLASSIFICATION NOT YET CLEAR-OUT
CELLULAR COMMUNICATION / SIGNAL TRANSDUCTION
UNCLASSIFIED PROTEIN & NOT PRESENT IN S. C.
CELL RESCUE, DEFENSE AND VIRULENCE
CONTROL OF CELLULAR ORGANIZATION
REGULATION OF / INTERACTION WITH CELLULAR ENVIRONMENT
PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT
25%
Figure 1. Effect of cycloheximide upon gene expression in Saccharomyces
Cerevisiae. The wildtype yeast strain (YAS1180) was grown in the presence of 50 ng/ml
cycloheximide for 30 minutes at 30C. Cells were harvested and total RNA was extracted. RNA
expression of different classes of genes was determined using the Affymetrix GeneChip Expression
System for Saccharomyces cerevisiae.
How to display the
data?
Raw Data from
Leishmania paper
A bit confusing
in a table.
How to present?
Pt
#
Case
IFN-g
SLA
pg/ml
1
2
7
8
12
22
23
24
25
26
27
28
29
30
31
36
37
5
6
9
15
18
19
20
21
34
3
4
10
11
13
14
16
17
32
33
35
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Active ACL
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Asymptomatic
Neg ctrl
Neg ctrl
Neg ctrl
Neg ctrl
Neg Ctrl
Neg ctrl
Neg ctrl
Neg ctrl
Neg Ctrl
Neg Ctrl
Neg ctrl
1140
<125
<125
629
>2000
<125
<125
<125
<125
162
<125
<125
<125
<125
<125
882
1568
<125
320
960
460
210
<125
1440
500
488
<125
<125
<125
<125
980
<125
<125
220
553
337
<125
IFN-g
PHA
IL-2
SLA
IL-2
PHA
pg/ml
pg/ml
pg/ml
>2000
870
755
<125
>2000
>2000
250
1045
<125
697
426
<125
777
313
1018
720
>2000
<125
196
>2000
>2000
>2000
>2000
>2000
>2000
>2000
>2000
>2000
106
>2000
>2000
>2000
>2000
>2000
1709
1909
300
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
411
600
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
700
120
<100
600
1100
720
<100
200
<100
<100
<100
<100
400
<100
300
1093
741
<100
<100
250
1500
700
350
1000
1850
<125
<100
<100
<100
>2000
550
>2000
1800
>2000
<125
<125
294
IL-10
SLA
pg/ml
50
<125
<125
140
<125
<125
80
<125
<125
<125
250
250
460
320
250
217
150
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
<125
130
<125
<125
<125
<125
<125
<125
<125
IL-10
PHA
pg/ml
>2000
208
600
1160
>2000
930
<125
154
100
468
210
225
260
770
240
>2000
482
340
690
720
>2000
1450
>2000
>2000
>2000
>2000
538
300
235
>2000
>2000
>2000
>2000
>2000
<125
>2000
>2000
IgE
ng/ml
600
40
190
220
140
40
75
140
320
250
226
367
480
134
57
432
341
210
380
150
100
<15
90
140
65
615
480
330
140
250
40
90
180
100
510
554
348
500
IL-10 Levels (pg/ml)
400
300
200
100
0
0
0.5
Active1ACL
1.5
2
2.5
Asymptomatic
3
Control
3.5
PBMC Samples
Figure 1. IL-10 produced by PBMCs in response to stimulation with
the Leishmania antigen. Peripheral blood mononuclear cells (PBMCs) collected
from people with active atypical cutaneous leishmaniasis (ACL) infection, people with
asymptomatic ACL, and uninfected people (control) were stimulated with 2 mg of soluble
Leishmania antigen (SLA). IL-10 levels were measured by ELISA.
Get Started!!!
• Try different ways of organizing your data
• Ask advice from colleagues
• Look at other articles and see how other
researchers present information that is similar
to your data