Replication - Science topic

What is the appropriate ANOVA model for the following experimental design: the effect of four different concentrations of compound X on microorganisms? Each X concentration has three jars and three replicates are collected from each pot. Samples are drawn weekly for 18 weeks.

I'm planning an experimental design to investigate the gene expression of mussels under thermal stress conditions, to compare their responses.

The experiment includes a control tank and a treatment tank, with enough mussels to sample 4 time points (60 mussels per time point per treatment).

In our facilities, I have two separate recirculation systems (with heaters and coolers) with two tanks each.

Since the two tanks of one recirculating system are connected they cannot be considered as isolated tanks and therefore they are not replicates. I'm planning to distribute the control samples between the two tanks of system 1 and the treatment samples between the two tanks of system 2, and at each time point sample from both tanks of one system and pooling them together (e.g. 30 samples from tank 1 and 30 samples from tank 2 of the control system)

My question is: do I need to have tank replicates for this experiment? Or can I sample three groups of 60 mussels per timepoint and per treatment between the two tanks of one system? i.e. have sample replicates within the control temperature and the treatment temperature tanks at each timepoint?

Thanks!

I have a compound (C23N3OH27) to repeat some results with a molecular weight of 361.48. The problem is that the results are not being the same, I am evaluating cell viability (K562 and KG1) with resazurin (24 hours of plating 20.000 cells/100uL, 24 hours of treatment 100uL, 4 hours of resazurin 20uL) and the results lead us to believe that it does not induce death in any of the cases. concentrations tested (30 uM, 20uM, 10uM, 5uM, 1uM), I have already evaluated cellular metabolism, resazurin, interaction of the compound with resazurin and none explains the reason for not repeating the results. I am suspicious that it could be my dilution, I used a table from a colleague that performs the calculation automatically. Could someone help me to do the dilution directly just so I can assess if it's correct? I have 5g powder of the compound which was diluted in 2305.34uL of 100% DMSO, which according to the table gave me a solution of 6,000uM, I don't know if that's correct.

Thanks for all contributions!

I have attached the dilution table below.

I want to measure the root volume of ramie plants grown in pots. I have replicated the trial, and I want to calculate the root volume of each plant in each replication. The procedure is unclear, and I want to get the expert's suggestions and guidelines. Any easy way to do this?

Dear all,

I'm working on the finer details of my experimental design, and have some questions regarding bridging channels for TMT based experiments.

I have two conditions to test, across nine biological replicates, in order to run as one 18-plex TMT-pro experiment.

I am aware of the use of one or more bridging channels being used with pooled samples to combine multiple TMT mixtures, however a colleague has mentioned that a bridging channel should also be considered for normalisation if only one set is used.

Does anyone have any experience using a bridging channel for normalisation in a single mixture? Is it worth sacrificing one or more biological replicates for?

I will be using MSstatsTMT for normalisation and summarisation.

Sam

Hi all,

I am optimising ThT assay protocol for a-syn aggregation. Even if I perform 5 replicates, the graphs are having different lag times for the same sample. I am not too sure if there is a better way to ensure reproducibility between replicates?

Currently I have tried with 100uM and 20uM of wild type monomeric alpha synuclein protein shaking at 800cpm, with an without addition of NaCl as well. The ThT concentration I use in the final solution is 20uM.

Kindly refer to the image attached for 20uM monomer with 100mM NaCl (as referenced from literature). Kindly ignore the timestamp, as these were all transferred from a different plate reader (so t=0 is actually after around 48 hours of shaking elapsed).

Or is it ok to just take the average of these curves? This does not sound right to me as they all have different lag times and it would not be fair to just take the average of them.

Hope someone can advise!

Thanks and Regards,

Mathangi

I need to conduct a feeding trial on the broiler, 5 dietary treatments 3 replications. How many broilers should be placed totally and in each single treatment?

How do you behave when you have, among your biological replicate, mixed sample distribution?

In my case I have 2 out of 3 biological replicated that are normally distributed, while the third one is not.

What kind of statistics shall I use?

Thank you for your help

I am particularly interested in replicating, or creating research connected to primary music education for children in Jamaica. I have some Orff Training and lots of experience teaching, although I'll be training student teachers, I'd like to collaborate to share best practice.

Hi all, I am interested in performing bulk RNA sequencing on primary human cells that have been cultured in absence/presence of certain types of drugs. I know n=6 is quoted as an acceptable number for cell lines and genetically identical mouse samples, but I can imagine the number of replicates needs to be higher when using primary human cells taken from a variety of donors. I am struggling to find any comparable published studies so I was wondering if anyone here had an idea/some experience with this? Many thanks!

I am a beginner with the use of SAS and Specially Orthogonal contrast. My experiment involve 4 rate of Nitrogen (23,46,69 and 92 kg N) at 3 time of application plus a control for bread wheat. The trail was at field by RCBD with three replication. The different responses are labeled as variables 1-39 as depicted in the SAS command I just prepared.

My treatments are:-

N-rates= 4

N application time =3

Control=1

Total treatments= 13

Thank you for your recommendation!

We are trying to compare results from a cell culture analysis. we have done 3 or 4 replicates per condition which is common practice.

Student's t-test is often used in similar publications, however, I'm not sure if is the best option, as 3 replicates per condition we cannot assume a normal distribution.

Which is the optimal statistical test you would use in such experimental conditions?

how far can Artificial Intelligence simulate and replicate human capabilities? Can it extend to the human abilities such as discovery and Inspiration?

Is scientific approach capable of answering this question at present or should we employ a rational reasoning approach? what would be that rational reasoning approach then?

Hi All

Due to the high cost of RNA-seq per sample. Do you think that it will be correct if I bulk three-four biological replicates and send this bulk for RNA-seq?

The FACE experiment is relatively expensive, making it difficult to include multiple replicates for each treatment. This presents a challenge: Are the final carbon cycling results due to the treatments themselves or the inherent variations among the sample points?

Does this require comparing with baseline data? How to do it?

I believe physics masters programs need innovation to overcome the slow progress in solving theoretical problems in physics in the form of ph.D replicating standards.

Imagine a student who has to write an essay about the paradoxes of dpevial relativity, literature teview akin ph.D and outline ideas to explsin them with his own indights

Or a similar topic about unitary quantum mechanics/spacetime quantization approaches duch CST.

Or a topic about how space has time properties, how its ststic vs dynamic fluidity give rise to spacetime phenomena.

Although some mofules would have more trafitional mathematical emphasis, 60% would have conceptual pre- ph.D level.

In my study there are 2 intervention groups and 1 control group. In the ANCOVA I take trait scores as covariates and state scores as AV (so I compare the differences in the groups after the intervention).

Is there a statistical way to check if there was a change in the control group? I am replicating a study, they assumed there was no effect in the control group - but what if I am not sure (because the control design could have an effect). Within the ANCOVA, I only know that the results differ, but not whether the changes differ, right?

I was challenged to identificate what kind of microscopy technique was used in five different images. The only information provided was the image, without other information, including scale bars that was omitted.

One of the images was assigned as TEM image from a freeze-fracture replication of a cell, but I have a doubt about it, as it resembles a SEM secundary electron image of a common surface of porous inorganic surface.

I'm not sure if is possiIble to differentiate this two kind of images just with the images and no other information. I'm not a microspist or a specialist in cell biology, but I'm a user of electron microscopy technique in materials science research field.

Can someone help me with this question?

Hello, currently I am observing the increase of water activity and moisture content of 4 different formulas over the course of 6 months, with each formula having 3 replicates, meaning a total of 12 samples per month or 72 samples for 6 months. Ideally I would like to use two-way ANOVA as this looks like a factorial design with 2 factors, one factor has 4 levels and the other has 6 levels. All the replicates are normally distributed. However, upon using Levene's test for equality of variances, I found that some replicates are not homoscedastic.

The questions are:

- If Levene's mean test shows that some data do not have equal variances, but Levene's median test (Brown-Forsythe) shows that all data have equal variances, can I use BF's median test and move on using two-way ANOVA? If so, are there any changes regarding what post-hoc test I use?

- If it turns out I cannot use BF median test as replacement for Levene's mean test, thus the assumption of equal variances is violated, can I still use ANOVA? If yes, should I use the mean of the replicates for the ANOVA test and what post-hoc test is appropriate? If no, what other tests can I use and what post-hoc test is appropriate?

I want to do a meta-analysis on how plant diversity affects soil carbon storage. For some studies, plant diversity was considered a categorical variable. The authors set plant diversity gradients and many replicates. These results have mean, replicates, and standard error. For other studies, plant diversity was considered a continuous variable. The authors investigated dozens of plots, which had different species richness and soil carbon storage, and no replicates and mean. The results were represented using a scatter diagram plus a regression line. I wonder how I should solve this problem. Should my research only include those studies with replicates, mean and standard error, discarding the latter?

Can some one suggest me some researches from Project Management Journals which must have variables, conceptual framework and research methodology so that I can replicate in some other demographic area?

Hello everybody! I did a small REST2 simulation (only 6 replicates and 400 ps) and would like to know if these results are "acceptable".

Repl average probabilities:

Repl 0 1 2 3 4 5

Repl .14 .18 .14 .15 .25

Repl number of exchanges:

Repl 0 1 2 3 4 5

Repl 30 36 28 28 47

Repl average number of exchanges:

Repl 0 1 2 3 4 5

Repl .15 .18 .14 .14 .23

Thanks!

Hello everyone,

I am trying to determine relative expression values for specific genes in different life-stages of my organisms (adult, larval, and microfilaria). For each of the three life-stages, I have three biological replicates and performed all PCRs in triplicate. I have two reference genes to compare with my genes of interest, but I am unsure how to calculate fold expression changes if there are no treatment groups/control groups with the ΔΔCt method since I am only assessing life-stage differences in expression. Also, with two reference genes, I am unsure at which point in the analysis I need to account for this.

Any advice would be greatly appreciated!

Dear All,

I have three columns in stata, the db column, dgdpmillionpkr and the districts. The dgdpmillionpkr has 110 observation. The db has two id values, db = 1 for the first 110 observations of dgdpmillionpkr and db=2 for the replicated 110 observations appended below the first 110 observation. I want to declare first 110 of districts as string and for the remaining 110 dgdpmillionpkr value of the respective districts, How I can code/do this in stata? Actually I want to show labels(districts name) and its values in order to visualize my dgdpmillionpkr data in map. I am stucked here, any help will be greatly appreciated. Thanks

I'm performing RNA-seq data analysis. I want to do healthy vs disease_stage_1, Healthy vs disease_stage_2, and Healthy vs disease_stage_3. In the case of healthy, disease_stage_1, disease_stage_2, and disease_stage_3 data sets, I have 19, 7, 8, and 15 biological replicates respectively.

Does this uneven number of replicates affect the data analysis?

Should I Use an even no of datasets like for every dataset, 7 biological replicates (As the lowest number of replicates here is 7)?

I have been working with A. tumefaciens for several months. I have generated different vectors (with Kanamycin resistance as bacterial selection maker) that I would like to test in A. rhizogenes (strain K599). I transformed my cells by electroporation following this strain's suggested settings, but I am not getting any resistant colony. I think that the origin of replication in my vectors that works in tumefaciens might not be compatible with rhizogenes. I am using the pVS1 origin of replication. I have considered testing RK2 or pRIA4 instead. I wonder if someone from the community has worked with K599 and has any suggestions.

I am currently undergoing my end of year research project which is testing if DNA can be found in a secondary transfer when multiple transfers have occurred, one of my replicates have a Ct value of 0, do I include this or find an average of the remaining replicates as when I have calculated out the fold change of my replicates it has a value of 20.29

I am trying to conduct a replication study (Hierarchical multiple regression), evidently I cant find anything to replicate. (I have found a number of overseas studies or studies involving other demographics).

It appears a gap exists in the literature.

If anyone can find a study for my research I would be much obliged.

I am doing a research and am doing 5 replicates. My data is as follow. These are measurements of rice shoots and the variables (41mm,41mmm etc) are the distance of the well from the source in a 6 well dishpack. I wanted to use SPPS to do my post hoc Tukey test to quicken the job as to not do it manually.... However I do not know how to do it properly. If there are any videos or tutorials I can follow, it would be much appreciated.

Hi everyone, I have some trouble finding the correct method for statistical analysis. I was thinking about a two-tailed paired T test, but that only considers the mean value of my replicates and not the distribution of the individual replicates as well.

My data set consists of 4 groups that are divided based on percentages (together 100%).

These groups are dependent on one variable (control, A, B, C, D, E and F) and I want to know whether condition A, B, C etc. is significantly different from the control.

I have 3 replicates of the experiment (with some measurement variance).

I have 300 wheat lines phenotyped in two years with two treatment with four replication (model CRD). Could you please have anyone please tell what is best suited R package for BLUP calculation?

I've grown kenaf plants at four different Cd concentrations: 0, 100, 250, and 400 uM. Using SPAD, I measured the content of chlorophyll in the third leaf from the top. Even though there is a noticeable decrease in plant growth under Cd stress compared to the control, I have noticed an increase in chlorophyll content under all Cd treatments compared to the control. Please assist me in interpreting these results. I grew plants in a hydroponic culture tray with three replicates, each tray containing 12 plants. I chose three plants from each tray to test the chlorophyll content.

Hi I am aiming to replicate an aerosol assisted CVD metod of fabricating BiFeO3, In this method it calls for 18 vol% nitric acid to disolve the Bi precursor in triple distilled water. I was wondering if it would be possible to used citric acid at higher concentration, as I already have this in stock and it is less intense in terms of safety? I appolgise if this is a stupid question, I'm not a chemist.

Hi all,

I just tried using a pre-coated ELISA plate for the first time, and I have a question about the results. I started by doing a dilution curve for two samples to see what would be the best dilution for my target protein. The absorbance of all the technical replicates are very consistent, and after I used the std. curve to calculate the concentrations they are still consistent. However, after multiplying the concentrations I got by their dilution factors, I got the highest concentrations in the highest dilution, with the concentrations decreasing as the dilution decreased. I added a photo to show what I mean. What could be happening here? It seems like a technical error but I don't know what the source would be.

Thanks.

I have been reading many papers but struggling to find a clear explanation for how to interpret the data apart from lower FP - faster tumbling, higher FP slower tumbling. I've not been taught on this topic, but have been given data to analyse as part of my honours project.

The data is on florescence polarisation with heparin, I also have random spikes in data, where FP is reading low at a certain concentration of heparin but one replicate will read a very high FP from the rest of the replicates in that concentration, is there an explanation for this?

Any help would be greatly appreciated. Thankyou.

Hello,

I have performed some recombineering protocols and realised that the chances of my plasmid being in a multimeric state are quite high.

I previously designed 7 primer pairs that will produce alternating amplicons of 500 and 700 bp around my recombineered plasmid (which is 35kb) just so that I could get an idea that no weird recombination events occurred when looking at it in a gel.

Anyways, I did the 7 PCR reactions on a control with the original plasmid, and they produced the expected pattern, but when performing it on my miniprep-purified plasmid I was obtaining a lot of bands of all sorts of sizes (larger and shorter than expected amplicon). Funny thing is that these multiple bands seemed to follow the same pattern in all my replicates (different pattern for each primer of course, but same throughout the different colonies tested) which makes me rule out the possibility of salt contaminants affecting primer binding etc. I thought it might be bacterial genomic contamination that was being amplified, so I performed a CsCl-ethidium bromide density gradient to purify it and sent it off for sequencing.

But now Im wondering, would a multimeric plasmid yield multiple bands if amplified with a single pair of primers?

By the way, I can't run it on a gel to assess if it's multimeric because of its large size 35kb, although I am going to ask if anyone at my lab has a pulse field gel electrophoresis just in case.

Thanks!

Hello to everyone,

I've had several discussions with my colleagues about setting up field experiments to be replicated in different environments.

We agree that each experiment must have exactly the same experimental design to ensure data comparability.

I've been told that these experiments must also have the exact same randomization, I don't agree because I believe that it is the experimental design itself that ensures data comparability. Below I attach a drawing to better explain the issue:

In the attached file, I have the same experimental design between locations, with the same randomization within subplots. Shouldn't we randomize the treatments (i, ii, iii and iv) within each subplot? Does it make sense to have an exact copy of experimental fields?

Thanks in advance!

How do you replicate the Finland education model for a country like India and make it better for personalisation.

Dear All

I have scored plant height and spike length in three replication in two years.

The analysis of anove was

genotypes+replicaiton+years+ R*G+R*Y+R*G*Y

I have found high significant correlation between the two years and no significant interaction G*Y

the correlation between 2021 and 2022 for plant height is 0.99. I really astonished how I have high significant differences between the two years in Ph and found such high correlation between the two years for pH. the same trend also was found for SL

I have been doing a lot of qPCRs and one of my genes gets a lot of "No Ct value"s so a colleague suggested going back into the Aria software and changing it, so the two technical replicates are not automatically averaged but instead shown as two individual values to see if there was any values there. And for some there were which is great.

but my question is, is this a widely accepted things to do?

Hi,

I found a plasmid have pSa ori and colE1 ori. Does any scientist know how this kind of plasmid replication in E.coil? If it will make a mistake during replication? Thanks for your kind help.

While computing descriptive statistics should I use means or replications.

I've used a drug that I am suspecting increases cell migration. I've got data of area at 0 hour and 24h of a scratch assay. I've got many replicates but can't seem to figure out the best strategy for statistical analysis. Any suggestion with some brief explanation?

I recently performed qRT-PCR using samples and internal control (ACT8). I am a bit concerned that the sd value of sample technical replicates is around 0.5.

Is the data still valid? Although the difference of value of technical replicates is less than 0.3

I am looking for collaboration in science teaching and learning specifically in physics because we have amassed many materials that can be replicated and implemented. I am looking at countries that has perennial problems on science equipment and modalities. advanced classrooms will not benefit in my materials. We developed materials in the wake of also scarcity of modalities. we are willing to share it for free. We also look forward to publishing the result of implementation for dissemination to a greater users.

I want to use Alpha Lattice Design in SAS. It is multi location and multi year trial. 4 locations, 2 years (4x2=8 environments) genotypes are 45.

2 replications

5 blocks/rep.

Thanks in adcance.

I'm trying to replicate an older protocol that used Promega PCR Master Mix (2x), using the master mix I have on hand (AmpliTaq Gold 360)

I have performed TaqMan qPCR assay for miRNA in tissue samples and found that my two technical replicates are always aligned while my third replicate goes way off in value. Why is it so?

The number of samples is 30 with three replicates.

Hi :)

I'm trying to replicate a protocol contained in the following paper: doi:10.1152/jn.00511.2016

I'd need to measure the median frequency of spontaneous oscillations of the membrane potential. To do so, I would like to calculate the discrete Fourier transform from a recording of spontaneous Vm oscillations and then the median frequency from a first-order interpolation of the cumulative probability of the power-spectral densities from 0.1 to 100 Hz.

I don't know how to perform this kind of calculations in Origin Pro software or Matlab: could you please help me with suggestions? Is there any simple code you know to start from?

Thanks,

The Background of the Question and a Suggested Approach

Consider that, e.g., a tensile strength test has been performed with, say, three replicate specimens per specimen type on an inhomogeneous or anisotropic material like wood. Why do the strength property determinations typically not consider the number of collected data points? As a simplification, imagine, e.g., that replicate specimen 1 fails at 1.0 % strain with 500 collected data points, replicate 2 at 1.5 % strain with 750 data points and replicate 3 at 2.0 % strain with 1 000 data points. For the sake of argument, let us assume that the replicates with a lower strain are not defective specimens, i.e., they are accounted for in natural variation(s). Would it not make sense to use the ratio of the collected data points per replicate specimen (i.e., the number of data points a given replicate specimen has divided by the total number of data points for all replicates of a given specimen type combined) as a weighing factor to potentially calculate more realistic results? Does this make sense if one were to, e.g., plot an averaged stress-strain curve that considers all replicates by combining them into one plot for a given specimen type?

Questioning of the Weighing

Does this weighing approach introduce bias and a significant error(s) in the results by emphasising the measurements with a higher number of data points? For example, suppose the idea is to average all repeat specimens to describe the mechanical properties of a given specimen type. In that case, the issue is that the number of collected data points can vary significantly. Therefore, the repeat specimen with a higher number of data points is emphasised in the weighted averaged results. Then again, if no weighing is executed, then, e.g., there are 500 more data points between replicates 1 and 3 in the above hypothetical situation, i.e., the averaging is still biased since there is a 500 data point difference in the strain and other load data and, e.g., replicate 3 has some data points that neither of the preceding replicates has. Is the “answer” such that we assume a similar type of behaviour even when the recorded data vary, i.e., the trends of the stress-strain curves should be the same even if the specimens fail at different loads, strains, and times?

Further Questions and Suggestions

If this data point based weighing of the average mechanical properties is by its very nature an incorrect approach, should at least the number of collected data points or time taken in the test per replicate be reported to give a more realistic understanding of the research results? Furthermore, when averaging the results from repeat specimens, the assumption is that the elapsed times in the recorded data match the applied load(s). However, this is never the case with repeat specimens; matching the data meticulously as an exact function of time is tedious and time-consuming. So, instead of just weighing the data, should the data be somehow normalised concerning the elapsed time of the test in question? Consider that the overall strength of a given material might, e.g., have contributions from only one repeat specimen that simply took much longer to fail, as is the case in the above hypothetical example.

Hello all,

I would like to export to an excel file all the data that has been recorded in my records modules in different excel files or in the same one, but different sheets per replication. Would you have an idea on how to do so ?

Thank you in advance !

I have 2 experimental groups, each with 3-5 biological replicates.

I want a statistical test that can compare parameter x between the two experimental groups.

Within each biological replicate I have ~2000-4000 cells that I want to factor into the analysis. How should I do this? Taking the mean for each biological replicate and performing a t test to compare the experimental groups seems inappropriate since the variance of the data is not accounted for. Similarly, factoring in all the data points and performing t tests between the groups seems inappropriate.

Any advice would be much appreciated.

what is the threshold values for selecting or rejecting the gene as reference gene? how much intergroup variability can be tolerated?

Is it proposed to use replicates (and if yes, how many) when doing spatial omics, using the same type of tissues but from different animals within the same phylum?

Experimental set-up:

I have recorded plant performance values in triplicate on two plants from a 4x3x2 factorial designed experiment, over 7 days

As I have only two biological replicates per treatment, does this negate any meaningful statistics?

Thanks very much for the communities help!

Often, we see work published and replicated by open-access review articles. Shouldn't we focus on original ideas to promote science?

It's a bit of a silly question but is there an explanation why E. coli has so many termination sites? Logically, one or two would be enough.

Is it just in case on of the replication fork progresses much faster than the other one, and so the two forks don't meet in the middle?

Maybe a weird question but I was wondering...

Hi! I'm trying to replicate this synthesis for Copper nanoparticles using as a capping agent PEG 10,000, however I'm having trouble finding the right amount of each reagent in grams, can anyone please help me with that?

"In a typical preparation process, CuCl2·2H2O aqueous solution was prepared by dissolving CuCl2·2H2O (10 mmol) in 50 ml deionized water. A flask containing CuCl2·2H2O aqueous solution was heated to 80 ◦C in an oil bath with magnetic stirring. A 50 ml L-ascorbic acid aqueous solution of various concentrations (0.4, 0.6, 0.8 and 1.0 M) was added dropwise into the flask while stirring. The mixture was kept at 80 ◦C until a dark solution was obtained. The resulting dispersion was centrifuged at 8000 rpm for 15 min. The supernatant was placed under ambient conditions for 2 months"

I have noticed that there are single microscopic slide/slip chambers (Cytodyne, Flexflow, IBIDI) and many studies have used these chambers. I wondered how it is possible to have more robust data by using a single fluid flow chamber (1 replicate) and a control?

I am studying the replication of a management process in the big four audit companies (EY, PwC, KPMG, and Deloitte). This process is relatively similar across these companies. So, is it a single case of replicating the process in the big four? or is it a multiple case study?

Note: I am not looking for variances between cases as there aren't any, I am looking into how the process is replicated in these firms and i am considering them as a one unit.

I'm currently designing a new experiment to measure RNA expression of several genes in mice samples. I normally use the 2-ddCt method to compare Control versus treated animals (5 vs 5 animals for instance) and regular T test (using the 5 biological replicates per group). But in this case I'd like to evaluate the expression of some genes only in liver NK cells, and the quantity of RNA I can get is really small, so I'm polling 3 livers together as one pooled sample. If I have only 6 animals, I will get only 2 biological replicates, and therefore the stats will be really poor (Control 1 and 2 average/ Treated 1 and 2 average). I repeated the same RT-PCR twice with those same samples and used 5 replicates per sample in each PCR. Is that of any help to get a better statistical analysis? What is the best (but correct) way to statistically analyse that data? Could I analyse each RTPCR separately (technical replicates) and then compare those to each other? Or is it correct to use the 5 replicates as 5 "samples" for the T test analysys? Any suggestions would be really appreciated.

Within a project about geographical traceability of horticultural products, we would like to apply classification models to our data set (e.g. LDA) to predict if it is possible to correctly classify samples according to their origin and based on the results of 20-25 different chemical variables.

We identified 5 cultivation areas and selected 41 orchards (experimental units) in total. In each orchard, 10 samples were collected (each sample from a different tree). The samples were analyzed separately. So, at the end, we have the results for 410 samples.

The question is: the 10 samples per orchard have to be considered pseudoreplicates since they belong to the same experimental unit (even if collected from indepedent trees)? Should the LDA be performed considering 41 replicates (the 41 orchards, taking the average of the 10 samples) or should we run it for the whole dataset?

Thank you for your help.

I want to replicate the results of newey and west Hac ols regression results of eviews in r.....I have used neweywest () function in r but error is there...

I want to apply The default setting which eviews follow for newey and west ...but I can't understand how to apply it...plz help me with the code

We've consulted definitions of repeat and replicate measures, but thought we'd put this question to the Researchgate world nonetheless.

The hypothetical case involves 30 pairs of subjects, chosen at random, in 30 regions of the world, with none of them knowing of the others. In each trial, Subject A taps out a number of identical sounds on table top -- e.g. "tap tap tap", so that Subject B can hear the sounds. After two seconds, Subject B taps a number of taps in response -- either the same or different number. Some of the 30 pairs of subjects exchange only one series of taps, while others more series of taps. In total, there are 100 sent-and-responded pairs in the dataset. Does this mean there are 100 repeated measurements and 30 replicates? Or is it the other way around? Or something else?

Consider FACS data from two groups (A and B, to be compared), each containing N biological replicates (in total 2N FACS plots). The outcome of the analysis is either a cell is positive or negative for a marker. At the end, one can make following contigency table by adding (or averaging) cells from N replicates for each groups:

Group A Group B

number of postive cells A pos B pos

number of negative cells A neg B neg

I guess, one can perform Fisher's exact test to check if the positive cells (or negative cells) are more likely to be in any one of the two groups.

1) If one does so, what is the use of biological replicates?

2) Should one average the number of cells per group or add cells from N replicates in each group?

3) Is there any other appropiate way to to perform such analysis: For example calculating percentage of positive cells for each replicate and then checking if the mean percentage (from N replicates) differs significantly different between the two groups?

I want to analyze my RT-qPCR experiments. I want to compare the expression of genes in cell type A, B and C and I have this cell types from the individuals 1, 2 and 3 (this are my biological replicates n=3). I have a reference gene (housekeeping gene, GAPDH), but in this case I dont have a control sample. For example I could normalize all values for each individual 1, 2 or 3 on cell type A from each individual. But this feels wrong because the standard deviation for cell type A will be nearly 0 and a statistical comparison with this cell type will not be possible.

Would it be also possible to choose the lowest value I measured in all samples from all three biological replicates and then normalize every value to it? Or to apply the deltadelta ct method and just normalize the values to the reference gene (housekeeping gene) without normalizing it to a control sample?

Dear all,

we have TMT10plex data on four samples and one pooled reference sample, structure is

TMT1: Pool-Pool-S1-S1-S2-S2-S3-S3-S4-S4

TMT2: Pool-Pool-S1-S1-S2-S2-S3-S3-S4-S4

....so 4 replicates for both the pooled reference and for each of the samples, however split to two individual TMT runs (with two technical replicates, for the record). What is the best framework to join this for getting significance of enrichment? After all we have 4x2 replication, which I would like to use. Any suggestions or references highly appreciated!

Christof

Looking for advice as to how to reduce variability between technical replicates when performing ELISA's of lysates of supernatants from bacterial recombinant expression strains. Currently incubating plates statically at room temp for 1 hour after adding standards and biological samples. Would extending this incubation or mild shaking improve results?

Hello,

I want to create a glycerol stock in a 96-well format so I can directly replicate it into a 96-well plate for growing and future experiments. I expect to do the replications fairly frequently. I'd really appreciate it if people can share what solution works the best for them in a similar situation.

In particular:

- what plate works better: deep well (2 mL) or the normal (~ 200 uL) ones

- what to close the plates with: AlumaSeal? Just a lid? Sealing Mat (https://ecatalog.corning.com/life-sciences/b2c/US/en/Genomics-%26-Molecular-Biology/PCR-Consumables/Storage-and-Sealing-Mats/Axygen%C2%AE-ImpermaMat-Sealing-Mats/p/axygenImpermaMatSealingMats - do they even exist for non-deep well plates?)

- do you fully thaw the plate before using or do you scrap from the frozen? If latter, how does resealing work on the cold and potentially wet surface?

- Any other tips to ensure no cross-contamination between the wells?

Thank you in advance!

Nina