u/jadexiaohui

I’m working with a game where, for example, a player has to hit a moving ball with a racket. Every time they successfully hit the ball, the racket becomes smaller, making the next hit progressively more difficult. Once they miss, the racket resets to its original size.

Each round lasts 30 minutes, and I would like to compare performance across rounds.

I also have a chance/control condition where the racket doesn’t move, so some hits can happen simply by chance.

I would like to get a single normalized hit-rate/performance value for each round that tells me whether the player performed well or poorly overall.

Ideally, this value would take into account:
\\\\- The racket getting smaller after each successful hit

\\\\- The racket resetting after a miss

\\\\- The fixed 30-minute duration

\\\\- Different numbers of hits and misses across rounds

\\\\- The chance-level performance from the stationary-racket condition

What would be the best way to calculate this as one normalized value? I’m particularly interested in something that accounts for the increasing difficulty as the racket gets smaller, rather than just calculating hits / total attempts. I have tried to calculate a score using the difference between the actual number of hits and the expected value of hits, but I think it does not take into account the paddle starting from where they last hit the ball (if it did not miss).

Also, if anyone knows of similar studies or experiments that have used a comparable normalized performance/hit-rate metric, I’d really appreciate any references or examples. Thank youu

Hi everyone, so I’m quite a newbie to this, and I’ve been using the NetworkX library in python to analyse functional connectivity networks in neuroscience. Based on my understanding, the algorithms for both metrics only work for positive edge weights, however, negative edge weights are quite commonly found in my data.

Hence, what is the best way to handle the negative edge weights? The options I have are to either filter them out and only perform the calculations on the positive-signed graph, or use the absolute values of all weights.

Thank you.

Hi everyone, it’s my first time publishing a paper, and since my team was thinking of submitting it to Nature, Cell and Science, I was wanting to ask:

1. Do I need to provide all of the code used along with the raw data for the first peer review process?

2. How rigorously do reviewers from these journals actually go through the code and raw data in detail?

For context, I was involved in the bioinformatics part of the paper.

Thank you in advance.

Hi all, I’m using a mixed-effects model to compare four cell culture groups and their characteristics (e.g., firing rate) over time.

Three monolayer groups were recorded for ~100 days in vitro, while the organoid group was recorded for much longer (~500 days in vitro) because it survives longer than the monolayers. I’m concerned that the unequal observation periods may affect the group comparisons.

Would a mixed-effects model still be appropriate here? If so, how should I account for the unequal follow-up periods? I would prefer not restricting the analysis to a common time range.

I’ve also considered simply comparing the mean values between groups to simplify the analysis, but this would ignore the effects of days in vitro.

Thank you!

Hi everyone, I would like to ask if there are statistical tests that could be used as an alternative for ANOVA and tukey post-hoc test in situations when the data is not dependent, such as when I am comparing between multiple timepoints where the object being measured is the same.

I am aware that I can use a linear mixed-effects model, but I prefer not to in this situation as it can be quite difficult to interpret.

Question is in the post title.

Thank you very much

Hi everyone, I currently have a dataset where there’s 3 groups. Each group has 3 independent values, and I want to compare each group against each other.

As I’m a little concerned about the sample size, would it better to use:

1. Pairwise welch t-test with p-value correction, or
2. ANOVA with tukey test

Thank you in advance for the help!

Hi everyone, I’m a biostatistician who is currently working on a project where I have to use an existing snRNA hippocampal atlas (dataset A) onto another snRNA dataset (dataset B). Both datasets are publicly available.

I am trying to find out if dataset B contains neocortical neuronal subpopulations, as I’m not too familiar with the biological side of things, so I am hoping to get some insights from those who regularly work with hippocampal neurons in the lab. Dataset A does contain neocortical subpopulations, but I will have to remove them from the analysis should dataset B does not actually contain cells from them. Below is the description of what was dissected from the original paper where I found dataset B.

“In addition, fresh surgically resected human hippocampal tissue from 10 patients between the ages of 2 to 61 years old were used for ex vivo slice culture,”

“We used a modified SPLiT-seq approach for nuclei isolation and snRNA-seq53,54. Nuclei isolation from snap-frozen hippocampal tissue was performed as previously described with minor modifications54,55. Briefly, after a visual inspection to include the dentate gyrus by its distinct anatomical structure, tissue was minced with a razor blade and Dounce (Fisher Scientific, 8853000002)”

Does anyone know any reliable way to know if any neocortical subpopulations are expressed in dataset B?

Thank you so much in advance for the help!

Hi everyone, I’m a biostatistician who is currently working on a project where I have to use an existing snRNA hippocampal atlas (dataset A) onto another snRNA dataset (dataset B). Both datasets are publicly available.

I am trying to find out if dataset B contains neocortical neuronal subpopulations, as I’m not too familiar with the biological side of things. Dataset A does contain neocortical subpopulations, but I will have to remove them from the analysis should dataset B does not actually contain cells from them. Below is the description of what was dissected from the original paper where I found dataset B.

“In addition, fresh surgically resected human hippocampal tissue from 10 patients between the ages of 2 to 61 years old were used for ex vivo slice culture,”

“We used a modified SPLiT-seq approach for nuclei isolation and snRNA-seq53,54. Nuclei isolation from snap-frozen hippocampal tissue was performed as previously described with minor modifications54,55. Briefly, after a visual inspection to include the dentate gyrus by its distinct anatomical structure, tissue was minced with a razor blade and Dounce (Fisher Scientific, 8853000002)”

Does anyone know any reliable way to know if any neocortical subpopulations are expressed in dataset B?

Thank you so much in advance for the help!

Hi everyone, for those who regularly perform scRNA sequencing analysis, how do you all find cell type specific marker genes?

I have been trying to find cell type specific markers from all my clusters in my scRNA data with sc.tl.rank_genes_groups (a scanpy function), which performs a Wilcoxon test to find the genes that are dominantly expressed in one cluster compared to the other clusters, however, I’ve noticed that some of the genes provided in the output are actually highly expressed in more than one cluster, especially among small and similar cluster populations. I think this may due to the averaging effect of the method, which makes it great for cell type populations that are drastically different compared to those that aren’t. Hence, I was wondering if there’s a more accurate method of finding genes that are specific to a single cell type.

Thank you in advance for the advice!

Hi everyone, I’m planning to compare a bulk RNA-seq dataset with a series of pseudobulked scRNA-seq datasets from different developmental timepoints to determine which developmental stage the bulk dataset most closely resembles.

My initial plan was to restrict the analysis to genes shared across all datasets and calculate Pearson/Spearman correlations between the bulk and pseudobulk expression profiles (since that was what was done in most studies I’ve read). For example, if my bulk dataset shows the strongest correlation with a pseudobulk dataset from a particular developmental stage in another species, I would interpret this as suggestive evidence that the bulk sample has a transcriptional profile most similar to that stage.

However, I’m concerned that correlation may be sensitive to differences in QC, normalization, sequencing depth, and gene filtering between the datasets. I was therefore considering alternative approaches, such as calculating correlations using only highly variable genes or using other distance/similarity metrics.

Hence, I was wondering if there are established and well-accepted methods that would be more appropriate for this type of cross-dataset developmental stage analysis?

Thank you!

I am considering using a hippocampal snRNA-seq atlas as the reference for label transfer onto a hippocampal scRNA-seq dataset. Could the differences between the nuclear and whole-cell transcriptions affect the accuracy of the label transfer?

The mitochondrial percentages appear to be similar between the two datasets so far (3% and 5% respectively per sample). Would this be sufficient, or are there other factors I should be concerned about?

Hi everyone, I am currently new to snRNA seq downstream analysis and I have a question regarding cell type deconvolution.

For my research, I have samples of cortical cells ranging from DIV 0-500, and I have performed bulk RNA seq with them. To enhance my analysis, I have used a snRNA seq dataset online gathered from adult cortical cells to perform deconvolution, where the snRNA seq is used as a reference dataset to estimate the cell type compositions from the bulk RNA seq dataset.

Hence, I have two questions:

1. Is it right to use a snRNA dataset from adult cortical cells even though my cortical cells only range from DIV 0-500?
2. Can I use the snRNA dataset to estimate the cell type compositions for DIV 0 accurately, eve

n though the gene profiles at DIV 0 and DIV 500 are very different?

I have tried to find a snRNA seq dataset online which spans from these DIV ranges but to no avail, hence, I would prefer using the dataset that I have now if possible. Thank you!!