<p>I have a matrix of gene expression for single cell (cells in column and genes in row) in 9 time points with about to 200 cells for each time point. I have also a matrix of gene expression in bulk RNA-seq with the same time point and 2 replications. </p>

<p>I have pooled reads from cells in each time point to have a simulated bulk RNA-seq. now I have two matrices; Real bulk RNA-seq in 9 time points and two replication and another matrix a simulated bulk RNA-seq with 9 time points and two replications. I have plotted a heat map of them to show the pattern of gene expression is similar in both matrices so single cell RNA-seq is good enough. Actually I have used the real bulk RNA-seq in a similar study as a gold standard for checking my single cell RNA-seq. My problem is this the pattern of gene expression in 0 time point is very different in two datasets. I should note that 0 time point comes from Fluidigm and another 8 time points come from icell8. I should be able TO adjust this heat map to look similar enough. </p>

<p>So I thought to show similarity by plotting cumulative fraction of genes detectable in varying fractions of cells (lets say 20%, 40%, ...), segregated by bulk RNA-seq expression quartile. Something like a plot that x axis is percent of cells and y axis is percent of genes but I think I should solve the fundamental problem in a heat map.</p>

<p>I have single cell (matrix with T) and bulk RNA-seq (matrix with R). Whatever I am plotting, time point 0 in single cell RNA-seq seems more yellow and dose not allow a good similarity between single cell and bulk RNA-seq. How I can make zero time point less yellow so another time points become more yellow?</p>

CC BY-SA 4.0
2018-07-06T08:35:53.960Z
How do you rank each cell by number of genes expressed?
llrs
CC BY-SA 4.0
2018-07-06T09:19:43.983Z
Thank you, actually I am not sure, if I have FPKM values for bulk RNA-seq, I don't   know how to show how many cells express a quartile of genes in bulk RNA-seq
Zizogolu
CC BY-SA 4.0
2018-07-06T10:36:10.213Z
Sorry, I think I misunderstood the question: what has to do the bulk RNA-seq with identifying the variability of genes in **individual** cells? What do you try to show ? You expect that this graph (however it is made) will help you to find/learn that ...?
llrs
CC BY-SA 4.0
2018-07-06T14:53:49.890Z
You right the question is very ambiguous,  I want to check similarity of two sets of samples
Zizogolu
CC BY-SA 4.0
2018-07-06T15:00:32.713Z
First make sure what you want help with in this question. Then please [edit] the question with more details. Do you want to compare the bulk RNA to the scRNA? Or you want to check the similarity at certain timepoints or between replications?
llrs
CC BY-SA 4.0
2018-07-06T23:17:21.633Z
I'm kind of surprised that your method of comparing data from two totally different sample prep methods worked at all, not that it differs significantly at only one timepoint.
swbarnes2
CC BY-SA 4.0
2018-07-07T11:19:28.270Z
What do you want? A method to normalize between technologies so that your time point 0 looks more similar between scRNA and bulk or plot the similarity  between bulk and scRNA? Also I assume that this data wasn't corrected by batch/technology effect or it is?
llrs
CC BY-SA 4.0
2018-07-07T11:26:08.840Z
I have not done batch correction. I only have pooled 200 cells in each time point to simulate a bulk RNA-seq and I hoped when comparing that with real RNA-seq from the same experiment the data look similar. But likely 0 time point is very different coming from two sequencing methods. after pooling my cells I normalised them with deseq2 like I have done for real bulk RNA-seq. for 8 time points looks good but for zero as this platform gives more read counts. how I manipulate my matrices to make a more similar heat maps or how could I remove difference between 0 time point and another time points
Zizogolu
CC BY-SA 4.0
2018-07-07T13:09:55.903Z
Sorry you are right about different sample prep but when I am calculation Pearson correlation between each time point I am seeing a high correlation between data. If this high correlation is by chance? correlation after log transforming both of datasets
Zizogolu
<r><scrnaseq><transcriptome>
Variability in genes across platforms
# Variability in genes across platforms

(Asked by: username_0 on 2018-07-05)

I have a matrix of gene expression for single cell (cells in column and genes in row) in 9 time points with about to 200 cells for each time point. I have also a matrix of gene expression in bulk RNA-seq with the same time point and 2 replications.

I have pooled reads from cells in each time point to have a simulated bulk RNA-seq. now I have two matrices; Real bulk RNA-seq in 9 time points and two replication and another matrix a simulated bulk RNA-seq with 9 time points and two replications. I have plotted a heat map of them to show the pattern of gene expression is similar in both matrices so single cell RNA-seq is good enough. Actually I have used the real bulk RNA-seq in a similar study as a gold standard for checking my single cell RNA-seq. My problem is this the pattern of gene expression in 0 time point is very different in two datasets. I should note that 0 time point comes from Fluidigm and another 8 time points come from icell8. I should be able TO adjust this heat map to look similar enough.

So I thought to show similarity by plotting cumulative fraction of genes detectable in varying fractions of cells (lets say 20%, 40%, ...), segregated by bulk RNA-seq expression quartile. Something like a plot that x axis is percent of cells and y axis is percent of genes but I think I should solve the fundamental problem in a heat map.

I have single cell (matrix with T) and bulk RNA-seq (matrix with R). Whatever I am plotting, time point 0 in single cell RNA-seq seems more yellow and dose not allow a good similarity between single cell and bulk RNA-seq. How I can make zero time point less yellow so another time points become more yellow?

**Tags:** r, scrnaseq, transcriptome
> **Comments:**
> **username_1**: How do you rank each cell by number of genes expressed?  
> **username_0**: Thank you, actually I am not sure, if I have FPKM values for bulk RNA-seq, I don't   know how to show how many cells express a quartile of genes in bulk RNA-seq  
> **username_1**: Sorry, I think I misunderstood the question: what has to do the bulk RNA-seq with identifying the variability of genes in **individual** cells? What do you try to show ? You expect that this graph (however it is made) will help you to find/learn that ...?  
> **username_0**: You right the question is very ambiguous,  I want to check similarity of two sets of samples  
> **username_1**: First make sure what you want help with in this question. Then please [edit] the question with more details. Do you want to compare the bulk RNA to the scRNA? Or you want to check the similarity at certain timepoints or between replications?  
> **username_2**: I'm kind of surprised that your method of comparing data from two totally different sample prep methods worked at all, not that it differs significantly at only one timepoint.  
> **username_1**: What do you want? A method to normalize between technologies so that your time point 0 looks more similar between scRNA and bulk or plot the similarity  between bulk and scRNA? Also I assume that this data wasn't corrected by batch/technology effect or it is?  
> **username_0**: I have not done batch correction. I only have pooled 200 cells in each time point to simulate a bulk RNA-seq and I hoped when comparing that with real RNA-seq from the same experiment the data look similar. But likely 0 time point is very different coming from two sequencing methods. after pooling my cells I normalised them with deseq2 like I have done for real bulk RNA-seq. for 8 time points looks good but for zero as this platform gives more read counts. how I manipulate my matrices to make a more similar heat maps or how could I remove difference between 0 time point and another time points  
> **username_0**: Sorry you are right about different sample prep but when I am calculation Pearson correlation between each time point I am seeing a high correlation between data. If this high correlation is by chance? correlation after log transforming both of datasets  

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## 0 answers

