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In the discussions, several participants refer to the publication's methods, such as generalized PCA and the implications of multinomial modeling on UMI counts, with comments like, 'the multinomial model adequately describes negative control data, and there is no need to model zero inflation.' The tone is predominantly technical, with many users expressing excitement and confidence in the proposed methods, contributing to an overall perception of high interest and potential impact.
Many discussions reflect a general support for the methods proposed in the publication, though there are some questions raised about specific aspects.
The discussions indicate a high level of curiosity about the application of the multinomial model in single-cell RNA sequencing, suggesting that the publication is relevant to current research interests.
While some posts delve into the technical details of the methods, others appear more superficial, indicating moderate engagement overall.
There is a strong belief that the advancements discussed in the publication can significantly influence the field of scRNA-Seq.
The solution is to do the math, as @will_townes et al. do here: https://t.co/BT3wF5L0Ko With modern computing, there is no problem implementing and using more sophisticated, and more suitable, versions of PCA such as @will_townes et al. propose. 14/
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM residuals as a fast approximation. In this nice paper @hippopedoid et al. now show that an improved version of scTransform is equivalent to the approximation. https://t.co/bEPX3xkTRF
But, as @GorinGennady regularly points out, there are some questions to ask. One of them is whether PCA throws out biological noise? That's for another thread. Here I'll just talk about normalization, as discussed at length in https://t.co/BT3wF5L0Ko by @will_townes et al. 6/
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
"Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data https://t.co/CcxM9shgjj Other great work on statistical models in scRNA-seq from @rafalab and @sandakano: https://t.co/9cSiOBEdUL #singlecellgenomicsday
This video focuses on enhancing feature selection and dimension reduction techniques for single-cell RNA sequencing (scRNA-Seq) data using a multinomial model. It discusses innovative methods like generalized principal…
Will Townes proposes a simpler method for analyzing scRNA-seq data using unique molecular identifiers (UMIs). By utilizing a PCA-like procedure informed by generalized linear models (GLMs), this approach mitigates…
✨ Comparto desde nuestro `Journal Club` esta presentacion donde exploro `Feature selection and dimension reduction for single-cell RNAseq`. Si te interesa cómo aplicar modelos multinomiales en scRNAseq, ¡dale un vistazo!
RT @metapredict: Widely used methods for the analysis of scRNA-Seq lack statistically rigorous justification based on a plausible data gene…
Widely used methods for the analysis of scRNA-Seq lack statistically rigorous justification based on a plausible data generating a mechanism for UMI counts https://t.co/YnPbgNddih
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model | Genome Biology | Full Text https://t.co/DMm02q0NoU
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
RT @tangming2005: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/h8g3k6g3Dp
RT @lpachter: The solution is to do the math, as @will_townes et al. do here: https://t.co/BT3wF5L0Ko With modern computing, there is no pr…
RT @lpachter: The solution is to do the math, as @will_townes et al. do here: https://t.co/BT3wF5L0Ko With modern computing, there is no pr…
The solution is to do the math, as @will_townes et al. do here: https://t.co/BT3wF5L0Ko With modern computing, there is no problem implementing and using more sophisticated, and more suitable, versions of PCA such as @will_townes et al. propose. 14/
But, as @GorinGennady regularly points out, there are some questions to ask. One of them is whether PCA throws out biological noise? That's for another thread. Here I'll just talk about normalization, as discussed at length in https://t.co/BT3wF5L0Ko by @will_townes et al. 6/
@lpachter And what do you think about generalized PCA as a dimensionality reduction method for single cell RNA-seq data? That seems quite principled to me... Also to project SNP data (using binomial error structure then)... https://t.co/ApEUKg5GN9 https://t.co/uLek7JXRVC
@anshulkundaje @mbeisen @NimwegenLab For single cell RNA-seq data - is generalized PCA not potentially better / more principled? Maybe they should use that for SNP data as well in fact (with binomial error structure then)... https://t.co/ApEUKg5GN9 https://t.co/uLek7JXRVC
RT @michaelhoffman: PN discussing dimension reduction followed by PCA as discussed in "Feature selection and dimension reduction for single…
PN discussing dimension reduction followed by PCA as discussed in "Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model". cc @stephaniehicks @rafalab #JSM2023 https://t.co/ZD3P88A9fG
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/iz7aBtnWvG
@NimwegenLab @ShahLabUCLA @jisaacmurray @MadS100tist Interesting point! So, if I understand well, your hypothesis here is that the real distribution of mRNA counts is log-normal, but the observed distribution is not. Evidence suggests that capture in scRNA-seq experiments corresponds to binomial sampling https://t.co/rn9L0j3ALb
@MadS100tist @slavov_n @arjunrajlab Also, as far as we know the sampling noise in scRNA-seq is binomial https://t.co/hqViG35JIp https://t.co/45i1vpIv4k. There is no way that that kind of sampling could generate power-law like densities from non-power-law distributions. And we show that by looking at control data
RT @WomenInStat: Townes et al., 2019 wrote a nice paper showing this. They show " PCA on log-normalized UMI counts can lead to distorted lo…
RT @WomenInStat: Townes et al., 2019 wrote a nice paper showing this. They show " PCA on log-normalized UMI counts can lead to distorted lo…
Townes et al., 2019 wrote a nice paper showing this. They show " PCA on log-normalized UMI counts can lead to distorted low-dimensional factors and false discoveries." and recommend glmPCA of raw counts, aka PCA of the deviance or Pearson Residuals https://t.co/w1WfzNHCbY
@hippopedoid @rafalab @JanLause We did some basic comparisons and didn't see a strong distinction (Fig S9,S10: https://t.co/tCdltE0FwX ). Aesthetically I prefer deviance resid bc they are more symmetrical, but Pearson has the compute advantage. I appreciate the more thorough evaluations you all have been doing!
RT @drisso1893: It turns out log(x+1) can do weird things to count data, e.g. exacerbating zero inflation (https://t.co/DIE13uwUsZ) and lea…
RT @drisso1893: It turns out log(x+1) can do weird things to count data, e.g. exacerbating zero inflation (https://t.co/DIE13uwUsZ) and lea…
It turns out log(x+1) can do weird things to count data, e.g. exacerbating zero inflation (https://t.co/DIE13uwUsZ) and leading to artificial clusters (https://t.co/AKZXi1Sn7l). [3/6] https://t.co/UJAEAY1bD3
RT @rafalab: In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM resid…
RT @rafalab: In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM resid…
RT @rafalab: In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM resid…
RT @rafalab: In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM resid…
RT @rafalab: In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM resid…
In https://t.co/7eElyZn5VE @sandakano introduced glmpca for scRNA-Seq: using a generalized version of PCA and PCA on GLM residuals as a fast approximation. In this nice paper @hippopedoid et al. now show that an improved version of scTransform is equivalent to the approximation. https://t.co/bEPX3xkTRF
Moreover, this analytic solution is equivalent to the rank-one GLM-PCA developed in Townes et al. 2019 (https://t.co/aGCMkN1RZ9) by @sandakano @stephaniehicks @rafalab. This nicely unifies these two papers that were published back-to-back in @GenomeBiology a year ago. /5
Author Correction: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/E1UQ3WYeJH https://t.co/XspaizXy2X
RT @LGMartelotto: “The multinomial model adequately describes negative control data, and there is no need to model zero inflation.” Feature…
@omnislip @davisjmcc scran is my normalization method of choice, as to my knowledge it's the only one that effectively corrects for composition biases in scRNA-seq data. It still distorts the data with log-transformation accoding to this article: https://t.co/ZG9C86Fya6
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @scell_papers: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/SX6viLNyun
RT @samuel_marsh: "Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data http…
RT @samuel_marsh: "Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data http…
RT @samuel_marsh: "Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data http…
RT @samuel_marsh: "Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data http…
"Droplet scRNA-seq is not zero-inflated" @vallens blog>preprint>publication highlights key aspect of scRNA-seq data https://t.co/CcxM9shgjj Other great work on statistical models in scRNA-seq from @rafalab and @sandakano: https://t.co/9cSiOBEdUL #singlecellgenomicsday
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @nextgenseek: @tangming2005 @rafalab @lgatt0 https://t.co/001pGh4JQE
@tangming2005 @rafalab @lgatt0 https://t.co/001pGh4JQE
https://t.co/ReFeWUU7xC "Townes, F.W., Hicks, S.C., Aryee, M.J. et al. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol 20, 295 (2019) doi:10.1186/s13059-019-1861-6"
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model | Genome Biology https://t.co/F6ezv6BSFo #bioinformatics https://t.co/roOhxNOIHl
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
multinomial methods outperform log normalization of single-cell UMI data for downstream clustering analysis https://t.co/BeaKKAAdBa
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @LGMartelotto: “The multinomial model adequately describes negative control data, and there is no need to model zero inflation.” Feature…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
“The multinomial model adequately describes negative control data, and there is no need to model zero inflation.” Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model | Genome Biology | Full Text https://t.co/dR6fi5ONd1
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model by @rafalab @GenomeBiology https://t.co/JLpHbtb2Oh
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
Top @RNomics #tweeted story: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model | Genome Biology | Full Text https://t.co/wPew8mzPFl, see more https://t.co/mx2YzvfCax
RT @GenomeBiology: Townes, @rafalab and co investigate scRNA-seq data with UMIs. UMI counts follow a multinomial distribution and are not z…
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model | Genome Biology | Full Text https://t.co/xN5uR3HFYJ https://t.co/QW9KEqdtZ9 Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq d…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @satijalab: Interested in statistical models for scRNA-seq? Happy holidays! Two papers in @GenomeBiology: * GLM-PCA from @sandakano and…
RT @razoralign: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/atjuHozo8t http…
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model https://t.co/atjuHozo8t https://t.co/6mREg1zjUF
RT @jsantoyo: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. https://t.co/kdmEKehCEA
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/Rl5kAiH71w
Top @RNomics #tweeted story: Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model | bioRxiv https://t.co/6bx2TpklE8, see more https://t.co/mx2YzvfCax
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/AXdbdrhCeW
RT @biorxivpreprint: Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/OSJAPmXWo…
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/9DAqmn6PVX
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model: https://t.co/1JMfD0OsLO
RT @biorxivpreprint: Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/OSJAPmXWo…
RT @biorxiv_genomic: Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/fisC558pU…
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/fisC558pUk #biorxiv_genomic
Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model https://t.co/OSJAPmXWoF #bioRxiv