How to calculate "Tissue specificity score" using JSD
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4.2 years ago
Ashley ▴ 90

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse cellular processes. Determining the function of individual lncRNAs remains a challenge. Recent advances in RNA sequencing (RNA-Seq) and computational methods allow for an unprecedented analysis of such transcripts. Our catalogue unifies previously existing annotation sources with transcripts we assembled from RNA-Seq data across human 24 tissues and cell types.

We want to find that lncRNA expression is strikingly tissue specific compared to coding genes. I'm using JS divergence to evaluate the tissue specificity. Recently, I read a paper "Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses". However, I don't know how to calculate for python code as follows:

import os
from scipy.stats import entropy
from numpy.linalg import norm
import numpy as np
def JSD(P, Q):
    _P = P / norm(P, ord=1)
    _Q = Q / norm(Q, ord=1)
    _M = 0.5 * (_P + _Q)
    return 0.5 * (entropy(_P, _M) + entropy(_Q, _M))
RNA-Seq next-gen sequencing python • 2.9k views
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Please reformat the question to make it readable. It looks like the non-code is formatted as code and vice versa.

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How to best go about it will depend a bit on the exact input you have. Here's what we're doing for JSD calculation in deepTools. Our requirements are a bit odd, since we have a very spiky distribution, which is why all of the interpolation stuff is done.

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2.1 years ago
elsoja ▴ 130

In case you're still interested in that, I developed a Python package that computes several tissue-specificity metrics, including JSD.

http://github.com/apcamargo/tspex

I'm finishing a web version of it that will be online soon (I hope).

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Thank you very much.

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You're welcome! In the next few days I plan to release a new version with a CLI interface and a full tutorial explaining how to use the package inside Python.

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4.2 years ago
YaGalbi ★ 1.5k

I recently developed a tissue specificity pipeline based on the Tau algorithm as recommended by a benchmark paper published this year. I had RNA-seq expression data from 23 tissues and found plenty of tissue specific protein coding genes but NO tissue specific long lnc-rna.

I highly recommend studying the benchmark paper as your starting point.

EDIT: Apologies, I should qualify - by tissue specific I mean " only expressed in 1 tissue" (out of 23). Many may show selectivity for a few (>1) tissues but I found no lincRNA that are specific for a single tissue. (Thank you EagleEye)

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Hi kennethcondon2007,

I have small suggestion. Long noncoding RNAs have been studied extensively in recent years. There are large number of studies and approaches have shown that lncRNAs act in tissue specific manner.

I Kindly request you to reconsider you pipeline and evaluate with different datasets.

Some benchmarking article references:

http://genome.cshlp.org/content/24/4/616.long?view=long&pmid=24429298

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185964/?report=classic

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0570-4

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Thank you EagleEye - I have edited my answer above. I'll certainly be reading those papers over the weekend.

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4.1 years ago
Ashley ▴ 90

It's confused to me. I'm poor in math and computer, I prefer to some softwares for this goal. Thank you very much!

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Shannon Entropy is one of the metrics benchmarked in the paper I cite above.

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Thank you very much, I'll try it later! Good Luck!

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