Use Sceptre to compare gene expression between two selected cell populations

[williampu66]

We are interested in using Sceptre to compare the expression of selected genes between two different single cell populations, but not within the context of a CRISPRi screen. It seems likely that the underlying Sceptre algorithm would work but the method for designating the cell populations would need to be modified from the gRNA in a CRISPRi screen to the metadata within the Seurat object. Is this something that is feasible to do? Thank you.

[timothy-barry]

Hi Bill,

We would be happy to help with this analysis task. Would you mind providing a bit more information about your data? In particular, how many genes, how many conditions, how many cells per condition, and any relevant covariates (e.g., batch or donor source)?

The DE functionality of sceptre is based on statistical method called the "permuted score test." We formally introduced the permuted score test in a recent arXiv preprint. (The version of the permuted score test introduced in the arXiv preprint is a bit more sophisticated than the one implemented in sceptre.) I plan on writing a standalone R function that implements the permuted score test. The function would take as input a list of gene expression vectors, a list of "treatment" vectors indicating the condition of each cell or sample, and an optional covariate matrix. The function would perform a DE analysis, returning the set of DE genes and their p-values. I believe that a function along these lines might be what you are looking for. Perhaps I could provide you with a prototype of this function?

Tim

[williampu66]

It would be fantastic to be able to try your prototype function. We have a list of about 20 candidate genes that we want to compare between sets of cells that are designated by metadata within our Seurat object. These cells were within the same batch of cells and the same scRNAseq expt, so we don’t expect covariates. Many thanks for your help. Bill From: Tim ***@***.***> Date: Thursday, January 23, 2025 at 1:34 PM To: Katsevich-Lab/sceptre ***@***.***> Cc: Pu, William ***@***.***>, Author ***@***.***> Subject: Re: [Katsevich-Lab/sceptre] Use Sceptre to compare gene expression between two selected cell populations (Discussion #173) [EXTERNAL] * External Email - Caution * Hi Bill, We would be happy to help with this analysis task. Would you mind providing a bit more information about your data? In particular, how many genes, how many conditions, how many cells per condition, and any relevant covariates (e.g., batch or donor source)? The DE functionality of sceptre is based on statistical method called the "permuted score test." We formally introduced the permuted score test in a recent arXiv preprint<https://urldefense.com/v3/__https:/arxiv.org/abs/2501.03530__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i1zEVKDSA$>. (The version of the permuted score test introduced in the arXiv preprint is a bit more sophisticated than the one implemented in sceptre.) I plan on writing a standalone R function that implements the permuted score test. The function would take as input a list of gene expression vectors, a list of "treatment" vectors indicating the condition of each cell or sample, and an optional covariate matrix. The function would perform a DE analysis, returning the set of DE genes and their p-values. I believe that a function along these lines might be what you are looking for. Perhaps I could provide you with a prototype of this function? Tim — Reply to this email directly, view it on GitHub<https://urldefense.com/v3/__https:/github.com/Katsevich-Lab/sceptre/discussions/173*discussioncomment-11933707__;Iw!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i07E3jsng$>, or unsubscribe<https://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/BB5MNKDBZAKUQD2KLIY3GD32MEY3LAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJTGM3TANY__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i2Dmvj6dw$>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[timothy-barry]

Hi Bill, This is a tight week for me, with a deadline in early February. Apologies for the delay, but I will try to get back to you with the function as soon as possible. Tim On Jan 23, 2025, at 4:58 PM, williampu66 ***@***.***> wrote: It would be fantastic to be able to try your prototype function. We have a list of about 20 candidate genes that we want to compare between sets of cells that are designated by metadata within our Seurat object. These cells were within the same batch of cells and the same scRNAseq expt, so we don’t expect covariates. Many thanks for your help. Bill From: Tim ***@***.***> Date: Thursday, January 23, 2025 at 1:34 PM To: Katsevich-Lab/sceptre ***@***.***> Cc: Pu, William ***@***.***>, Author ***@***.***> Subject: Re: [Katsevich-Lab/sceptre] Use Sceptre to compare gene expression between two selected cell populations (Discussion #173) [EXTERNAL] * External Email - Caution * Hi Bill, We would be happy to help with this analysis task. Would you mind providing a bit more information about your data? In particular, how many genes, how many conditions, how many cells per condition, and any relevant covariates (e.g., batch or donor source)? The DE functionality of sceptre is based on statistical method called the "permuted score test." We formally introduced the permuted score test in a recent arXiv preprint<https://urldefense.com/v3/__https:/arxiv.org/abs/2501.03530__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i1zEVKDSA$>. (The version of the permuted score test introduced in the arXiv preprint is a bit more sophisticated than the one implemented in sceptre.) I plan on writing a standalone R function that implements the permuted score test. The function would take as input a list of gene expression vectors, a list of "treatment" vectors indicating the condition of each cell or sample, and an optional covariate matrix. The function would perform a DE analysis, returning the set of DE genes and their p-values. I believe that a function along these lines might be what you are looking for. Perhaps I could provide you with a prototype of this function? Tim — Reply to this email directly, view it on GitHub<https://urldefense.com/v3/__https:/github.com/Katsevich-Lab/sceptre/discussions/173*discussioncomment-11933707__;Iw!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i07E3jsng$>, or unsubscribe<https://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/BB5MNKDBZAKUQD2KLIY3GD32MEY3LAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJTGM3TANY__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i2Dmvj6dw$>. You are receiving this because you authored the thread.Message ID: ***@***.***> — Reply to this email directly, view it on GitHub<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_Katsevich-2DLab_sceptre_discussions_173-23discussioncomment-2D11935327&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=d1v_WzzVGvOcjsx-QSqHbX6hdozHewJMVh7ESOXo9zU&m=zr9ZIZJC9SXe_yWb6OJy1eBEIzouCwuwKt9-td3vKUwg7BmPDmykiGZ6iAucTVfN&s=VZnRaXCknq5C8J3BFIdFx5nif-3-9mzXXregEe4FAUs&e=>, or unsubscribe<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AH6QVNMKWHCPUHV5HXZ2BGT2MFQYRAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJTGUZTENY&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=d1v_WzzVGvOcjsx-QSqHbX6hdozHewJMVh7ESOXo9zU&m=zr9ZIZJC9SXe_yWb6OJy1eBEIzouCwuwKt9-td3vKUwg7BmPDmykiGZ6iAucTVfN&s=nkxwiyCc97ObehWOGxMpj83-_7wC5FqPLzmVZmie4m0&e=>. You are receiving this because you commented.Message ID: ***@***.***>

[williampu66]

Much appreciate. Thank you. Bill From: Tim ***@***.***> Date: Tuesday, January 28, 2025 at 9:26 AM To: Katsevich-Lab/sceptre ***@***.***> Cc: Pu, William ***@***.***>, Author ***@***.***> Subject: Re: [Katsevich-Lab/sceptre] Use Sceptre to compare gene expression between two selected cell populations (Discussion #173) [EXTERNAL] * External Email - Caution * Hi Bill, This is a tight week for me, with a deadline in early February. Apologies for the delay, but I will try to get back to you with the function as soon as possible. Tim On Jan 23, 2025, at 4:58 PM, williampu66 ***@***.***> wrote: It would be fantastic to be able to try your prototype function. We have a list of about 20 candidate genes that we want to compare between sets of cells that are designated by metadata within our Seurat object. These cells were within the same batch of cells and the same scRNAseq expt, so we don’t expect covariates. Many thanks for your help. Bill From: Tim ***@***.***> Date: Thursday, January 23, 2025 at 1:34 PM To: Katsevich-Lab/sceptre ***@***.***> Cc: Pu, William ***@***.***>, Author ***@***.***> Subject: Re: [Katsevich-Lab/sceptre] Use Sceptre to compare gene expression between two selected cell populations (Discussion #173) [EXTERNAL] * External Email - Caution * Hi Bill, We would be happy to help with this analysis task. Would you mind providing a bit more information about your data? In particular, how many genes, how many conditions, how many cells per condition, and any relevant covariates (e.g., batch or donor source)? The DE functionality of sceptre is based on statistical method called the "permuted score test." We formally introduced the permuted score test in a recent arXiv preprint<https://urldefense.com/v3/__https:/arxiv.org/abs/2501.03530__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i1zEVKDSA$>. (The version of the permuted score test introduced in the arXiv preprint is a bit more sophisticated than the one implemented in sceptre.) I plan on writing a standalone R function that implements the permuted score test. The function would take as input a list of gene expression vectors, a list of "treatment" vectors indicating the condition of each cell or sample, and an optional covariate matrix. The function would perform a DE analysis, returning the set of DE genes and their p-values. I believe that a function along these lines might be what you are looking for. Perhaps I could provide you with a prototype of this function? Tim — Reply to this email directly, view it on GitHub<https://urldefense.com/v3/__https:/github.com/Katsevich-Lab/sceptre/discussions/173*discussioncomment-11933707__;Iw!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i07E3jsng$>, or unsubscribe<https://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/BB5MNKDBZAKUQD2KLIY3GD32MEY3LAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJTGM3TANY__;!!NZvER7FxgEiBAiR_!r6nT3bjAsdghz2C6wZHTPCiH0LdXyG45HB_7tDzvnojexLdvB2IPiDkNMSkDm2ZcPIXNPZoR4zA9N1Xh97jjFnIv5i2Dmvj6dw$>. You are receiving this because you authored the thread.Message ID: ***@***.***> — Reply to this email directly, view it on GitHub<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_Katsevich-2DLab_sceptre_discussions_173-23discussioncomment-2D11935327&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=d1v_WzzVGvOcjsx-QSqHbX6hdozHewJMVh7ESOXo9zU&m=zr9ZIZJC9SXe_yWb6OJy1eBEIzouCwuwKt9-td3vKUwg7BmPDmykiGZ6iAucTVfN&s=VZnRaXCknq5C8J3BFIdFx5nif-3-9mzXXregEe4FAUs&e=>, or unsubscribe<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AH6QVNMKWHCPUHV5HXZ2BGT2MFQYRAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJTGUZTENY&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=d1v_WzzVGvOcjsx-QSqHbX6hdozHewJMVh7ESOXo9zU&m=zr9ZIZJC9SXe_yWb6OJy1eBEIzouCwuwKt9-td3vKUwg7BmPDmykiGZ6iAucTVfN&s=nkxwiyCc97ObehWOGxMpj83-_7wC5FqPLzmVZmie4m0&e=>. You are receiving this because you commented.Message ID: ***@***.***> — Reply to this email directly, view it on GitHub<https://urldefense.com/v3/__https:/github.com/Katsevich-Lab/sceptre/discussions/173*discussioncomment-11984873__;Iw!!NZvER7FxgEiBAiR_!vMccFZT9r3U-jPr_B49eHvBTeAvCZBx7k63QiRlmda8r728BFyPdHgJpFhjRda8-TTsGUzf47J_n-w6lXfem5uJleHkB1SgtPA$>, or unsubscribe<https://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/BB5MNKH3XHJGZDNGU2TMB5L2M6HSJAVCNFSM6AAAAABVVNLO26VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTCOJYGQ4DOMY__;!!NZvER7FxgEiBAiR_!vMccFZT9r3U-jPr_B49eHvBTeAvCZBx7k63QiRlmda8r728BFyPdHgJpFhjRda8-TTsGUzf47J_n-w6lXfem5uJleHmg7oqG9A$>. You are receiving this because you authored the thread.Message ID: ***@***.***>