Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation

Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging st...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wu, Xiaotian [verfasserIn] Teo, Yee Voan [verfasserIn] Neretti, Nicola [verfasserIn] Wu, Zhijin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Single cell RNA seq Penalized LDA Aging Blood cells

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: BMC genomics - BioMed Central, 2000, 23(2024), Suppl 4 vom: 18. Sept.
Übergeordnetes Werk:	volume:23 ; year:2024 ; number:Suppl 4 ; day:18 ; month:09

Links:	Volltext

DOI / URN:	10.1186/s12864-024-10763-8

Katalog-ID:	SPR057367418

Internformat


LEADER	01000naa a22002652 4500
001	SPR057367418
003	DE-627
005	20240919064729.0
007	cr uuu---uuuuu
008	240919s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s12864-024-10763-8 \|2 doi
035			\|a (DE-627)SPR057367418
035			\|a (SPR)s12864-024-10763-8-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 570 \|a 610 \|q VZ
084			\|a 12 \|2 ssgn
084			\|a BIODIV \|q DE-30 \|2 fid
084			\|a 42.20 \|2 bkl
084			\|a 44.48 \|2 bkl
100	1		\|a Wu, Xiaotian \|e verfasserin \|4 aut
245	1	0	\|a Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
264		1	\|c 2024
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2024
520			\|a Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.
650		4	\|a Single cell RNA seq \|7 (dpeaa)DE-He213
650		4	\|a Penalized LDA \|7 (dpeaa)DE-He213
650		4	\|a Aging \|7 (dpeaa)DE-He213
650		4	\|a Blood cells \|7 (dpeaa)DE-He213
700	1		\|a Teo, Yee Voan \|e verfasserin \|4 aut
700	1		\|a Neretti, Nicola \|e verfasserin \|4 aut
700	1		\|a Wu, Zhijin \|e verfasserin \|0 (orcid)0000-0002-9596-9134 \|4 aut
773	0	8	\|i Enthalten in \|t BMC genomics \|d BioMed Central, 2000 \|g 23(2024), Suppl 4 vom: 18. Sept. \|w (DE-627)326644954 \|w (DE-600)2041499-7 \|x 1471-2164 \|7 nnns
773	1	8	\|g volume:23 \|g year:2024 \|g number:Suppl 4 \|g day:18 \|g month:09
856	4	0	\|u https://dx.doi.org/10.1186/s12864-024-10763-8 \|m X:SPRINGER \|x Resolving-System \|z kostenfrei \|3 Volltext
912			\|a SYSFLAG_0
912			\|a GBV_SPRINGER
912			\|a FID-BIODIV
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
936	b	k	\|a 42.20 \|q VZ
936	b	k	\|a 44.48 \|q VZ
951			\|a AR
952			\|d 23 \|j 2024 \|e Suppl 4 \|b 18 \|c 09

Indexfelder

author_variant	x w xw y v t yv yvt n n nn z w zw
matchkey_str	article:14712164:2024----::osbodeltpsnaigrdcinsnpnlzdae
hierarchy_sort_str	2024
bklnumber	42.20 44.48
publishDate	2024
allfields	10.1186/s12864-024-10763-8 doi (DE-627)SPR057367418 (SPR)s12864-024-10763-8-e DE-627 ger DE-627 rakwb eng 570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Wu, Xiaotian verfasserin aut Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213 Teo, Yee Voan verfasserin aut Neretti, Nicola verfasserin aut Wu, Zhijin verfasserin (orcid)0000-0002-9596-9134 aut Enthalten in BMC genomics BioMed Central, 2000 23(2024), Suppl 4 vom: 18. Sept. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:23 year:2024 number:Suppl 4 day:18 month:09 https://dx.doi.org/10.1186/s12864-024-10763-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 VZ 44.48 VZ AR 23 2024 Suppl 4 18 09
spelling	10.1186/s12864-024-10763-8 doi (DE-627)SPR057367418 (SPR)s12864-024-10763-8-e DE-627 ger DE-627 rakwb eng 570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Wu, Xiaotian verfasserin aut Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213 Teo, Yee Voan verfasserin aut Neretti, Nicola verfasserin aut Wu, Zhijin verfasserin (orcid)0000-0002-9596-9134 aut Enthalten in BMC genomics BioMed Central, 2000 23(2024), Suppl 4 vom: 18. Sept. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:23 year:2024 number:Suppl 4 day:18 month:09 https://dx.doi.org/10.1186/s12864-024-10763-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 VZ 44.48 VZ AR 23 2024 Suppl 4 18 09
allfields_unstemmed	10.1186/s12864-024-10763-8 doi (DE-627)SPR057367418 (SPR)s12864-024-10763-8-e DE-627 ger DE-627 rakwb eng 570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Wu, Xiaotian verfasserin aut Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213 Teo, Yee Voan verfasserin aut Neretti, Nicola verfasserin aut Wu, Zhijin verfasserin (orcid)0000-0002-9596-9134 aut Enthalten in BMC genomics BioMed Central, 2000 23(2024), Suppl 4 vom: 18. Sept. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:23 year:2024 number:Suppl 4 day:18 month:09 https://dx.doi.org/10.1186/s12864-024-10763-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 VZ 44.48 VZ AR 23 2024 Suppl 4 18 09
allfieldsGer	10.1186/s12864-024-10763-8 doi (DE-627)SPR057367418 (SPR)s12864-024-10763-8-e DE-627 ger DE-627 rakwb eng 570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Wu, Xiaotian verfasserin aut Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213 Teo, Yee Voan verfasserin aut Neretti, Nicola verfasserin aut Wu, Zhijin verfasserin (orcid)0000-0002-9596-9134 aut Enthalten in BMC genomics BioMed Central, 2000 23(2024), Suppl 4 vom: 18. Sept. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:23 year:2024 number:Suppl 4 day:18 month:09 https://dx.doi.org/10.1186/s12864-024-10763-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 VZ 44.48 VZ AR 23 2024 Suppl 4 18 09
allfieldsSound	10.1186/s12864-024-10763-8 doi (DE-627)SPR057367418 (SPR)s12864-024-10763-8-e DE-627 ger DE-627 rakwb eng 570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Wu, Xiaotian verfasserin aut Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213 Teo, Yee Voan verfasserin aut Neretti, Nicola verfasserin aut Wu, Zhijin verfasserin (orcid)0000-0002-9596-9134 aut Enthalten in BMC genomics BioMed Central, 2000 23(2024), Suppl 4 vom: 18. Sept. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:23 year:2024 number:Suppl 4 day:18 month:09 https://dx.doi.org/10.1186/s12864-024-10763-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 VZ 44.48 VZ AR 23 2024 Suppl 4 18 09
language	English
source	Enthalten in BMC genomics 23(2024), Suppl 4 vom: 18. Sept. volume:23 year:2024 number:Suppl 4 day:18 month:09
sourceStr	Enthalten in BMC genomics 23(2024), Suppl 4 vom: 18. Sept. volume:23 year:2024 number:Suppl 4 day:18 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Single cell RNA seq Penalized LDA Aging Blood cells
dewey-raw	570
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Wu, Xiaotian @@aut@@ Teo, Yee Voan @@aut@@ Neretti, Nicola @@aut@@ Wu, Zhijin @@aut@@
publishDateDaySort_date	2024-09-18T00:00:00Z
hierarchy_top_id	326644954
dewey-sort	3570
id	SPR057367418
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057367418</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240919064729.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240919s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12864-024-10763-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057367418</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12864-024-10763-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Xiaotian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single cell RNA seq</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Penalized LDA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Blood cells</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Teo, Yee Voan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Neretti, Nicola</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Zhijin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9596-9134</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC genomics</subfield><subfield code="d">BioMed Central, 2000</subfield><subfield code="g">23(2024), Suppl 4 vom: 18. Sept.</subfield><subfield code="w">(DE-627)326644954</subfield><subfield code="w">(DE-600)2041499-7</subfield><subfield code="x">1471-2164</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:Suppl 4</subfield><subfield code="g">day:18</subfield><subfield code="g">month:09</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12864-024-10763-8</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.20</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.48</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2024</subfield><subfield code="e">Suppl 4</subfield><subfield code="b">18</subfield><subfield code="c">09</subfield></datafield></record></collection>
author	Wu, Xiaotian
spellingShingle	Wu, Xiaotian ddc 570 ssgn 12 fid BIODIV bkl 42.20 bkl 44.48 misc Single cell RNA seq misc Penalized LDA misc Aging misc Blood cells Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
authorStr	Wu, Xiaotian
ppnlink_with_tag_str_mv	@@773@@(DE-627)326644954
format	electronic Article
dewey-ones	570 - Life sciences; biology 610 - Medicine & health
delete_txt_mv	keep
author_role	aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1471-2164
topic_title	570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation Single cell RNA seq (dpeaa)DE-He213 Penalized LDA (dpeaa)DE-He213 Aging (dpeaa)DE-He213 Blood cells (dpeaa)DE-He213
topic	ddc 570 ssgn 12 fid BIODIV bkl 42.20 bkl 44.48 misc Single cell RNA seq misc Penalized LDA misc Aging misc Blood cells
topic_unstemmed	ddc 570 ssgn 12 fid BIODIV bkl 42.20 bkl 44.48 misc Single cell RNA seq misc Penalized LDA misc Aging misc Blood cells
topic_browse	ddc 570 ssgn 12 fid BIODIV bkl 42.20 bkl 44.48 misc Single cell RNA seq misc Penalized LDA misc Aging misc Blood cells
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	BMC genomics
hierarchy_parent_id	326644954
dewey-tens	570 - Life sciences; biology 610 - Medicine & health
hierarchy_top_title	BMC genomics
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)326644954 (DE-600)2041499-7
title	Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
ctrlnum	(DE-627)SPR057367418 (SPR)s12864-024-10763-8-e
title_full	Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
author_sort	Wu, Xiaotian
journal	BMC genomics
journalStr	BMC genomics
lang_code	eng
isOA_bool	true
dewey-hundreds	500 - Science 600 - Technology
recordtype	marc
publishDateSort	2024
contenttype_str_mv	txt
author_browse	Wu, Xiaotian Teo, Yee Voan Neretti, Nicola Wu, Zhijin
container_volume	23
class	570 610 VZ 12 ssgn BIODIV DE-30 fid 42.20 bkl 44.48 bkl
format_se	Elektronische Aufsätze
author-letter	Wu, Xiaotian
doi_str_mv	10.1186/s12864-024-10763-8
normlink	(ORCID)0000-0002-9596-9134
normlink_prefix_str_mv	(orcid)0000-0002-9596-9134
dewey-full	570 610
author2-role	verfasserin
title_sort	mouse blood cells types and aging prediction using penalized latent dirichlet allocation
title_auth	Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
abstract	Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. © The Author(s) 2024
abstractGer	Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. © The Author(s) 2024
abstract_unstemmed	Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells. © The Author(s) 2024
collection_details	SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	Suppl 4
title_short	Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation
url	https://dx.doi.org/10.1186/s12864-024-10763-8
remote_bool	true
author2	Teo, Yee Voan Neretti, Nicola Wu, Zhijin
author2Str	Teo, Yee Voan Neretti, Nicola Wu, Zhijin
ppnlink	326644954
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s12864-024-10763-8
up_date	2024-09-19T04:49:54.506Z
_version_	1810598612968669184
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057367418</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240919064729.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240919s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12864-024-10763-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057367418</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12864-024-10763-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Xiaotian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. Results In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. Conclusions pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single cell RNA seq</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Penalized LDA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Blood cells</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Teo, Yee Voan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Neretti, Nicola</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Zhijin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9596-9134</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC genomics</subfield><subfield code="d">BioMed Central, 2000</subfield><subfield code="g">23(2024), Suppl 4 vom: 18. Sept.</subfield><subfield code="w">(DE-627)326644954</subfield><subfield code="w">(DE-600)2041499-7</subfield><subfield code="x">1471-2164</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:Suppl 4</subfield><subfield code="g">day:18</subfield><subfield code="g">month:09</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12864-024-10763-8</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.20</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.48</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2024</subfield><subfield code="e">Suppl 4</subfield><subfield code="b">18</subfield><subfield code="c">09</subfield></datafield></record></collection>
score	7.397312

Nicht das Richtige dabei?

Schreiben Sie uns!

Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?