Figures and Tables For Your Publication

1. Figures and TablesFor Your Publication How to Present Your Data

2. 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)

3. Do you have enough data to write a paper? • Start with organizing your data into figures and tables. • Most publications have 5 figures.

4. 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?

5. 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

6. 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.

7. Table 2. Blood glucose levels Breakfast 300 250 200 150 100 50 0 Dinner Lunch Diabetic Blood Glucose Level (mg/dl) Normal (mg/dl*) 100.3 93.6 88.2 100.5 138.6 102.4 93.8 132.3 103.8 93.6 127.8 109.2 Diabetic (mg/dl) 175.8 165.7 159.4 72.1 271.0 224.6 161.8 242.7 219.4 152.6 227.1 221.3 Time (hour) midnight 2:00 4:00 6:00 8:00 10:00 noon 2:00 4:00 6:00 8:00 10:00 Normal 12:00 6:00 am 12:00 6:00 pm 12:00 Hour * decaliters/milligram Figure 11. Blood glucose levels over time for normal individual and diabetic subjects Tables or graphs?

8. Graphs are often easier to understand

9. 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

10. 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

11. Not all data should be in the table Table 3. Indicator Bacteria and Viral Results Abbreviations: MPN, most probable number; HAV, Hepatitis A Virus +/- indicates the presence or absence of virus as detected by RT-PCR

12. 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

13. 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!

14. Serum Liver Spleen Lymph nodes Brain Spinal cord Table example Table 1. Detection of Infectious DEN2 in Tissues by Indirect Plaque Assays Mice strains Day 3 p.i.a WT129 - - - - - - A129 + + + + +/- +/- G129 - - - - - - AG129 + + + + + + Day 7 p.i.a WT129 - - - - - - A129 - - - - +/- +/- G129 - - - - - - AG129 - - - - + + 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 108 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.

15. Figure format • Different types • Graph • Chart • Picture (gels, flow cytometry) • Diagram • All have • Figure Legend (title and a brief description of experiment)

16. 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

17. 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

18. 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

19. Response to Amoxicillin Percentage of Mice Figure 1. Percentage of mice that responded to amoxicillin treatment. Three mice were treated with 0.5 mg/ml amoxicillin for 7 days. Incorrect if N= 3 mice!!

20. 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.

21. 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

22. Other Types of Figures: p=0.0171 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. A. 1 0 0 (n = 30) W T 1 2 9 8 0 (n = 21) G 1 2 9 (n = 22) A G 1 2 9 6 0 % Survival 4 0 p<0.0001 2 0 . 0 0 5 1 0 1 5 2 0 2 5 3 0 Days Post-Infection

23. 100mM 50mM 250mM 500mM 5DLuc3D + + + + M + + + + 5DLuc 97.4 66.2 MW KDa 45.0 * 31.0 * 21.5 14.4 RNA Inhibitor Concentration Gels 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 *.

24. METABOLISM ENERGY CELL CYCLE & DNA PROCESSING TRANSCRIPTION PROTEIN SYNTHESIS PROTEIN FATE CELLULAR TRANSPORT & TRANSPORT MECHANISMS CELLULAR COMMUNICATION / SIGNAL TRANSDUCTION CELL RESCUE, DEFENSE AND VIRULENCE Pie-charts UP-REGULATED 30 MIN DOWN-REGULATED 30 MIN 1% 14% 10% 10% 2% 23% 3% 6% 2% 11% 5% 25% 30% 6% 13% 2% 3% 2% 0% 1% 7% 3% 4% 5% 2% 0% 0% 2% 3% 4% 1% CELL FATE SUBCELLULAR LOCALIZATION PROTEIN ACTIVITY REGULATION TRANSPORT FACILITATION CLASSIFICATION NOT YET CLEAR-OUT UNCLASSIFIED PROTEIN & NOT PRESENT IN S. C. CONTROL OF CELLULAR ORGANIZATION REGULATION OF / INTERACTION WITH CELLULAR ENVIRONMENT PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT 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.

25. How to display the data? Raw Data from Leishmania paper A bit confusing in a table. How to present?

26. 500 400 300 200 100 0 0 0.5 1 1.5 2 2.5 3 3.5 Active ACL Asymptomatic Control 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. IL-10 Levels (pg/ml) PBMC Samples

27. 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