Bulk Tissue Deconv. Cell Fractions

I would like to apply the bulk-tissue deconvolution algorithm in your recent paper (Wang et al., 2018) using our own single cell RNA-Seq data and Gandal et al., 2018’s bulk tissue RNA-Seq. I couldn’t find code related to the deconvolution steps in the Gernsetin Lab github page (https://github.com/gersteinlab/PsychENCODE-DSPN) or on the PsychEncode resources page. I only found results to the cell fraction calculations. Would you be able to point me towards how I can apply this algorithm?

We used non-negative least square method for deconvolution and implemented it using R function nnls (https://www.rdocumentation.org/packages/lsei/versions/1.2-0/topics/nnls) For example nnls(C, bi) estimates the cell fractions for ith tissue sample, where C is cell type gene expression matrix (row: gene, column: cell type), and bi is the gene expression vector for ith tissue sample.

Mouse Transcribed Pseudogene Data

I’m currently working on how pseudogenes can act as competitive endogenous RNAs in humans, and would like to expand my study to include mice. I recently read a paper from your lab, Comparative analysis of pseudogenes across three phyla, and in the supplementary information you mention that you identified 878 transcribed pseudogenes in the mouse genome. Is there a list of these pseudogenes as well as their associated parent genes available on either the pseudogene.org website or on a different website?

I think this draft list should be on the psicube site .

Questions about using PseudoPipe

First of all I must show great respect to your brilliant work on developing the PseudoPipe software.
Now I am working on my graduate paper, and need to use this software. But I met some problems, so any guide or assistance from you would be appreciated.
I just download the software package from your website and unpack it in my home directory(that is ~/), but when I test it according to your manual, it reported errors as below:
I have tried several ways to fix it ,even trying to modify the source code, but failed. I’ve been driven somehow crazy haha.
Can you please provide some suggestions? thanks in advance!

It looks like your installation is not referencing python properly. Please edit the env.sh file with the appropriate source/path for python in your system.

According to your suggestion, now I have finished all the environment variable setting in env.sh, but I still got error while running the software(as the below Fig.1)
So I try to fix the code of pseudopipe.sh , and I finally made it run just by modifying the "source setenvPipelineVars" into "source ./setenvPipelineVars" at line 141. And I got the final result file(as Fig. 2) by running your sample data. Is the result correct?
Don’t know if anybody reported similar error before. If not, I hope it would contribute to improving your powerful software. And it would be great if you can also display on your manual or README what the standard output and final result file look like when testing the sample data.

The results look right. Thank you for your suggestions, we will take them into account in a future update of the pipeline.

Good luck with your analysis.

Question about deconvolution analysis in PsychENCODE paper

I have a question about the deconvolution method used in the flagship PsychENCODE paper Comprehensive functional genomic resource and integrative model for the human brain. I would like to perform a similar analysis on my own bulk samples using the single cell expression profiles used in the paper, however it is unclear how these profiles are formed.

Specifically, supplementary file DER-23 lists the cell type fractions for 24 cell types. These coefficients presumably came from solving the following:

B = C * W

Where B is the marker gene by samples matrix, C is the marker gene by cell type matrix, and W is the appropriate weights matrix. How do I go about obtaining or reproducing the 24 cell type profiles? From what I can tell, these profiles were not released along with the other supplemental data sets.

If you could please answer my question or forward this email on the appropriate author(s), I would appreciate it.

Sorry for the late reply. I think the profiles you want are on resource.psychencode.org

Requesting information about cQTL and fQTL data from PsychENCODE

I am writing in regards to the datasets posted on PsychENCODE website. I noticed that full summary statistics for QTL maps are posted for eQTLs and isoQTLs, but cQTLS and fQTLs only have top SNP information. Is there a chance you could upload full summary stats for cQTLs and fQTLs as well?

We calculated cQTLs and fQTLs differently from eQTLs and isoQTLs. So we only have top SNP information for cQTLs and fQTLs.

Information about datasets from PsychENCODE

I am writing to ask where I might be able to find a list of all of the datasets generated for the PsychENCODE project. Specifically, we would like to know how many single-cell and bulk RNA-seq datasets were generated, and what the sex and age is of the samples used to generate these datasets. I was not able to find this information in the supplementary materials from your 2018 Science paper or on the PsychENCODE website, but perhaps I am missing something. Before we start the application process to access the raw data, it would be very helpful to have this information.

You should be able to find a list of all datasets used for Wang et al.
(’18) from resource.psychencode.org . Please contact Prashant (copied)

Please refer to http://resource.psychencode.org/Datasets/RawData/RAW-01_PEC_Table_of_Datasets.xlsx

This contains the set of datasets associated with the analysis in the Wang et al paper, focusing on the adult samples. Of course, this is a subset of the total PsychENCODE datasets. I can see if there is a simple resource for you to access to get the information from the superset. I will let you know soon.

I am going to answer your question in two parts. Here is Part 1:

The metadata for the prenatal and adult single-nuclei datasets is available at http://development.psychencode.org/ under the "Processed Data" heading, "Single cell/nucleus RNA-seq". The ages and sexes of the sampled individuals can be found in the .xlsx files with the labels "QC" appended.

Can you tell me if additional single-cell RNA-seq datasets will be generated in the next phase of this project and what the timeframe might be?

There will definitely be a significant expansion of the single-cell/nucleus datasets in the next phase, though it is as yet uncertain as to how long that would take. I am hesitant to take a guess right now, but please check back in a couple of months and we may have a better answer.

psych encode derived data types

I am looking at
Derived Data Types
there are a couple of gene expression matrices. What are the columns (samples), ie which ones are which cases and which controls in the header file:

What is the difference between
besides the fact that one has 43,886 lines and the other has 57,821 lines and that one has 1,932 columns and the other 1,867 columns.

1) "What are the columns (samples), ie which ones are which cases and which controls in the header file"

I am unable to pass on this information, as our DCC mentioned that diagnosis information would only be available upon application to Synapse for approval by the NIMH and investigators. Please contact them for access approval.

"What is the difference between

The difference in the numbers is simply between FPKM and TPM units in expression.
"besides the fact that one has 43,886 lines and the other has 57,821 lines"
There is very likely a difference in the thresholding of gene expression applied to these two datasets. I have reached out to my colleague who processed these matrices and will get back to you with a more definite response soon.

4) "that one has 1,932 columns and the other 1,867 columns."

The column number differences arise from the following: 1931 is the original number of DFC samples considered, which includes both adult and non-adult individuals. Once we filtered out the 65 non-adult samples, we obtained the 1866 individuals in the second matrix. Unfortunately, this was not made clear on the website. I will be updating this soon.

But wait a minute, these files are useless without at least knowing who the cases and who the controls are?

Here is the rationale:
PsychENCODE placed restrictions on the dissemination of metadata. While adhering to those restrictions, we endeavored to put out as many of the processed datasets from our analyses as possible to allow for reproduction or downstream usage. This includes several intermediate files. Some may require protected data obtained with the permission of the consortium to perform downstream analyses, but even then the files on our website are in a format that would aid such analyses, and that are not available elsewhere.

For completeness, here are the answers to your original questions:
1) Method for generating DER-01: Using the original FPKM file, we filtered on >=10 individuals with >0.1 FPKM (though GTEx also applied a filter of requiring raw read counts greater than 6 — we did not have the raw data from GTEx, so we didn’t apply a filter on raw read counts).

2) Quantile normalization was performed to bring the expression profile of each sample onto the same scale.

3) To protect from outliers, inverse quantile normalization was performed for each gene, mapping each set of expression values to a standard normal.
2) Method for generating DER-02: The TPM file was converted directly from the original FPKM file