1 d embryonic Grh targets by ChIP-seq and gene
expression analysis.
2 pression were uncovered through whole-genome
expression analysis.
3 DCoX to perform multi-factor differential co-
expression analysis.
4 n tomography, electroneurography, and ELOVL5
expression analysis.
5 RACE) to adjust for the effect of SCNA in co-
expression analysis.
6 d the sensitivity of subsequent differential
expression analysis.
7 Real-time PCR was performed for gene
expression analysis.
8 to standard approaches of differential gene
expression analysis.
9 f cell states invisible to conventional gene
expression analysis.
10 advance research on large-scale differential
expression analysis.
11 luorescence, coimmunoprecipitation, and gene
expression analysis.
12 in the evaluation and interpretation of IRF5
expression analysis.
13 Microarrays were performed for gene
expression analysis.
14 polypharmacology through systems-based gene
expression analysis.
15 propagate this into downstream differential
expression analysis.
16 y integrating systems pharmacology with gene
expression analysis.
17 his library, yielding sensitive differential
expression analysis.
18 using both physiological end points and gene
expression analysis.
19 a and provides outputs for differential gene
expression analysis.
20 a defined niche coupled with cell cycle gene
expression analysis.
21 emokines shown to be upregulated in the gene
expression analysis.
22 cell genomic DNA PCR and fluorescent marker
expression analysis.
23 ective method for obtaining tissue for PD-L1
expression analysis.
24 unding effect of RNA quality in differential
expression analysis.
25 ization of qPCR data is crucial for accurate
expression analysis.
26 lexible thin-film sensor for label free gene
expression analysis.
27 cancer subtypes as well as isoform specific
expression analysis.
28 ew insights to the area of differential gene
expression analysis.
29 e runs of balanced sampling for differential
expression analysis.
30 Genome-wide differential gene
expression analysis (
18,863 probes) resulted in a p valu
31 In differential gene
expression analysis,
about 70% of the significantly diff
32 within an organism and for comparative gene
expression analysis across organisms.
33 ng through the cilium, as determined by gene
expression analysis after fluid flow-induced shear stres
34 The proposed network-based differential gene
expression analysis algorithm dwgLASSO can achieve bette
35 ation, model reconstruction and differential
expression analysis,
all delivered through an updated pr
36 Gene
expression analysis also confirmed the selective down-re
37 Gene
expression analysis also identified several other signif
38 Gene
expression analysis also indicated the presence of nonta
39 developed for single-factor differential co-
expression analysis and applied in a variety of studies.
40 Both PPM1D
expression analysis and cDNA sequencing in EBV LCLs of i
41 e ontology enrichment analysis, differential
expression analysis and comparison of experiments.
42 Expression analysis and conditional deletion studies sho
43 Differential
expression analysis and Gene ontology enrichment reveale
44 Furthermore, our gene-
expression analysis and genetic mapping results suggest
45 Using gene
expression analysis and native gel electrophoresis we ch
46 d acute leukemias, largely derived from gene
expression analysis and next-generation sequencing that
47 Genome-wide
expression analysis and numerical simulation experiments
48 netic and pharmacological manipulation, gene
expression analysis and time-lapse imaging of zebrafish
49 eing, there does not exists any differential
expression analysis approach for RNA-seq data in literat
50 ability measurements as well as intronic RNA
expression analysis are consistent with a transcriptiona
51 It is unclear whether co-
expression analysis at the single-cell level will provid
52 ge Bayes Factor approach for Differential Co-
expression Analysis (
BFDCA) for DC analysis.
53 We used gene
expression analysis,
biochemical methods, transmission e
54 le studies focusing on not only differential
expression analysis,
but also quantitative transcriptome
55 Conventional differential gene
expression analysis by methods such as student's t-test,
56 Expression analysis by quantitative reverse transcriptio
57 cules conferred a protective phenotype, gene
expression analysis by RNA sequencing found that Roxadus
58 tools for their impact on differential gene
expression analysis by RNA-Seq.
59 However, in vivo
expression analysis by RNAscope and immunohistochemistry
60 Gene
expression analysis by RT-qPCR revealed enrichment of p5
61 ctivation was characterized by targeted gene
expression analysis,
caspase-1 and NF-kappaB studies, cy
62 ublicly available transcriptomic studies, co-
expression analysis combining multiple transcriptomic st
63 robustness to batch effects in differential
expression analysis,
compared to existing methods.
64 First, a differential gene
expression analysis comparing two distinct biological st
65 Global gene
expression analysis,
data intersection, pathway analysis
66 Differential
expression analysis (
DEA) is one of the main instruments
67 Differential
expression analysis demonstrated an increased steady-sta
68 Expression analysis demonstrated that RIP1, RIP3, and ML
69 Genome-wide
expression analysis demonstrated that the absence of G9a
70 Additionally,
expression analysis demonstrates that nodule leghemoglob
71 While differential gene
expression analysis did not demonstrate differences in P
72 FA together with RNA-sequencing differential
expression analysis establishes the vanillin catabolic p
73 Differential
expression analysis following nuclear RNA-seq of neutrop
74 We conducted phylogenetic and
expression analysis for eight SEP-like GERBERA REGULATOR
75 Furthermore, gene
expression analysis for RBM20-dependent splice variants
76 entification for ChIP-seq, differential gene
expression analysis for RNA-seq, nucleosome positioning
77 ation could be used as part of a specific co-
expression analysis framework.
78 RNA
expression analysis from isolated fibre cells reveals th
79 CaMYB31
expression analysis from placental tissue of pungent and
80 b servers are valuable and widely used, many
expression analysis functions needed by experimental bio
81 This study used bioinformatics,
expression analysis,
gene ablation, and specific pharmac
82 We use gene silencing, gene
expression analysis,
genetic mapping and population geno
83 pts to climb chasms of unsurmountable width;
expression analysis guided us to C2 optic-lobe interneur
84 Differential gene-
expression analysis has allowed us to confirm the releva
85 In the past decade, gene
expression analysis has become a universally applied tec
86 Co-
expression analysis has been employed to predict gene fu
87 Gene
expression analysis has led to the identification of 254
88 Differential
expression analysis has long been used for this purpose;
89 Differential co-
expression analysis helps further detect alterations of
90 biopsy specimens were taken for whole-genome
expression analysis,
histology, and T-cell isolation.
91 Ribonucleic acid
expression analysis identified changes in metabolic path
92 Gene
expression analysis identified distinct surface markers
93 keratinocytes in AhR-knockout mice, and gene
expression analysis identified many barrier-associated g
94 NanoString gene
expression analysis identified multiple HuR-regulated ge
95 Conclusion Gene
expression analysis identified multiple pathways upregul
96 Global gene
expression analysis identified that PELP1-cyto expressio
97 tified in the C. hongkongensis, Differential
expression analysis identified the miRNAs that play impo
98 Gene
expression analysis identified the potent cell cycle inh
99 rom the following sources: (i) systematic co-
expression analysis, (
ii) detection of shared selective
100 Gene
expression analysis,
immunohistochemical studies of MM p
101 PHD
expression analysis in 124 colorectal cancer patients re
102 Expression analysis in 18 different cancers and matched
103 We used genome-wide gene
expression analysis in clinical samples to identify miR-
104 A RNA-seq
expression analysis in different Arachis species showed
105 Here, we performed an extensive gene
expression analysis in ER+ breast cancer cell lines, to
106 highlight the potential of single-cell gene-
expression analysis in human preimplantation development
107 Gene
expression analysis in human prostate cancer samples fou
108 g RedFinder algorisms to perform a stability
expression analysis in i) normal colon cells, ii) colon
109 e high-throughput tool for hypothesis-driven
expression analysis in large numbers of genes (10 to 500
110 miRNA
expression analysis in NiggV-infected mice showed signif
111 -/-) mice and used quantitative PCR for gene
expression analysis in terminally differentiated osteocl
112 Allele-specific
expression analysis in the C. maxima x C. moschata inter
113 Immune gene
expression analysis in the renal clear cell carcinoma co
114 ghput mapping of the m(6)A transcriptome and
expression analysis in the Yhtdc2 mutant testes reveal a
115 affected by VPA, we performed a genome-wide
expression analysis in yeast cells grown in the presence
116 Microarray gene
expression analysis indicated downregulation of NF-E2 re
117 Expression analysis indicated that BAM2 is more closely
118 hrough the Calvin Benson cycle; accordingly,
expression analysis indicated that GLXI is transcription
119 Gene
expression analysis indicated that the host cell respond
120 Expression analysis indicated that the transcript level
121 Expression analysis indicates that the OsBZ8 gene is hig
122 Differential gene
expression analysis indicates that there are multiple pa
123 Through fine-mapping, association analysis,
expression analysis,
insertional mutagenesis and transge
124 integrated features to support differential
expression analysis,
interactive heatmap production, pri
125 When disease-specific gene
expression analysis is integrated, DGE-NET prioritizes k
126 iferation; however, a comprehensive NOX gene
expression analysis is missing for all major model syste
127 However, co-
expression analysis is often treated as a black box with
128 A critical assumption of gene
expression analysis is that mRNA abundances broadly corr
129 A frequent requirement of single cell
expression analysis is the identification of novel patte
130 Co-
expression analysis is widely used to predict gene funct
131 By gene
expression analysis,
KRAS is shown to be associated with
132 l treatments, we performed leaf RNA-seq gene
expression analysis,
LC-MS metabolomics and total phenol
133 Expression analysis,
leveraged by a de novo assembled tr
134 s selected by conventional differential gene
expression analysis method, the top 10 significant genes
135 dwgLASSO with conventional differential gene
expression analysis method.
136 formance than conventional differential gene
expression analysis methods by integrating information a
137 Importantly, using gene
expression analysis (
N = 60) and immunohistochemistry (N
138 By
expression analysis,
next-generation sequencing, and imm
139 al model of Parkinson's disease through gene
expression analysis of >30 batches of grafted human embr
140 Expression analysis of >300 surface proteins enabled ide
141 Quantitative
expression analysis of 2,068 Mtb genes from the predicte
142 In a postmortem
expression analysis of 33 individuals affected with schi
143 Expression analysis of amyloid precursor protein transge
144 Single-cell gene
expression analysis of B6 Ifnb(+/+) versus B6 Ifnb(--) T
145 Further, gene
expression analysis of CD34(+) cells derived from fetal
146 Genome-wide DNA methylation and
expression analysis of cell line models of acquired AI r
147 Expression analysis of components of the elastogenesis m
148 to glandular sepal tips; thus, differential
expression analysis of contrasting floral tissue transcr
149 Gene
expression analysis of dyW-/- E17.5 muscles identified a
150 Expression analysis of earleaf samples in a subtropical
151 Gene-
expression analysis of effector-memory CD8(+) dT demonst
152 For instance, gene
expression analysis of genes associated with the hypotha
153 d a high-throughput (HT) phenotypic and gene
expression analysis of HemSCs, and analyzed HemSC-derive
154 Furthermore, gene
expression analysis of human melanoma samples revealed t
155 d maturation was NK cell intrinsic, and gene
expression analysis of human NK cell developmental subse
156 f incisor renewal and illustrate how gene co-
expression analysis of intact biological systems can pro
157 Differential gene
expression analysis of joint neutrophils showed a switch
158 h based on the differential single-cell gene
expression analysis of mesenchymal osteolineage cells cl
159 Expression analysis of MiHA-encoding genes showed that s
160 Our approach to differential
expression analysis of minor intron-containing genes is
161 Moreover,
expression analysis of mRNA from similar tissues and tre
162 Comparative gene
expression analysis of neuronal derivatives from these i
163 mRNA
expression analysis of proteins involved in establishmen
164 Gene
expression analysis of rat brains following neonatal inf
165 Expression analysis of ravA and viaA genes showed that b
166 Expression analysis of rice ARFs and ARLs in different t
167 Differential
expression analysis of RNA sequencing (RNA-seq) data typ
168 Therefore, we performed a longitudinal gene
expression analysis of samples collected from controls a
169 Gene
expression analysis of sensitive and resistant cell line
170 RT-PCR based
expression analysis of seven glucosinolate biosynthetic
171 Gene
expression analysis of several pathogenesis-related gene
172 Gene
expression analysis of single cell-derived, adapted tetr
173 and phospho-Jun N-terminal kinase, and gene
expression analysis of splenic CD19(+) B cells demonstra
174 RNA-seq has been used to perform global
expression analysis of the achene and the receptacle at
175 Gene
expression analysis of the brain implicates altered inna
176 Epigenetic and gene
expression analysis of The Cancer Genome Atlas AML data
177 ed generalized linear model for differential
expression analysis of the count-based sequencing data f
178 Gene
expression analysis of the dorsal hippocampus of PC4(f/f
179 We report the first gene
expression analysis of the human host response to experi
180 Gene
expression analysis of the hypoxia-interconnected pathwa
181 Gene
expression analysis of the liver and ileum indicated alt
182 Expression analysis of the main responsible genes for Na
183 Parallel RNA-Seq
expression analysis of the plasmid reporter identifies n
184 Differential
expression analysis of the RNA sequencing datasets revea
185 A systematic gene
expression analysis of the SiOBP repertoire was performe
186 Differential
expression analysis of transcriptomes from four paired s
187 e improve the interpretation of differential
expression analysis of transcriptomic data from human ti
188 However,
expression analysis of voltage-gated sodium channel alph
189 y Ilk in ureteric cells using a whole-genome
expression analysis of whole-kidney mRNA in mice with Il
190 We performed gene
expression analysis on whole peripheral blood RNA sample
191 In a gene
expression analysis performed to identify novel gene mod
192 profiled using RNASeq and differential gene
expression analysis performed.
193 In this study we propose a new differential
expression analysis pipeline, dubbed as super-delta, tha
194 data sets and with alternative differential
expression analysis pipelines.
195 yers of genomic analysis (e.g., differential
expression analysis,
principal component analysis, gene
196 ustomizable functions including differential
expression analysis,
profiling plotting, correlation ana
197 Further
expression analysis provided the functional annotation o
198 Differential gene
expression analysis provides evidence that root-knot nem
199 0.91, accuracy = 0.90), as compared to gene
expression analysis results without TIN correction (sens
200 Differential
expression analysis revealed 128 proteins that are selec
201 For all tissues sampled,
expression analysis revealed 831, 674 and 648 differenti
202 r in vitro cell culture experiments and gene
expression analysis revealed a major defect in the proli
203 Last, allelic-specific
expression analysis revealed a significant but modest im
204 Gene
expression analysis revealed a significant decrease in t
205 Gene
expression analysis revealed an upregulation of gene sig
206 Intriguingly, co-
expression analysis revealed blood gene modules highly e
207 Gene
expression analysis revealed downregulation of ion trans
208 Differential
expression analysis revealed hundreds of genes modulated
209 Differential gene and exon
expression analysis revealed pervasive alterations in AP
210 In confirmation, gene
expression analysis revealed reduced tyrosine hydroxylas
211 Proteomic and gene
expression analysis revealed significant alterations in
212 Gene
expression analysis revealed significant differences in
213 Expression analysis revealed that a Diedel gene homolog
214 Genome-wide
expression analysis revealed that a set of cro-miRNAs ar
215 Gene
expression analysis revealed that all the mate transcrip
216 Gene
expression analysis revealed that anti-inflammatory cyto
217 Importantly,
expression analysis revealed that both SNHG6-003 and TAK
218 Expression analysis revealed that Bph32 was highly expre
219 Gene
expression analysis revealed that CD172a(+) and CD172a(-
220 Gene
expression analysis revealed that CML36 and ACA8 are co-
221 Gene
expression analysis revealed that CtHsfA2b was heat-indu
222 Global gene
expression analysis revealed that genes upregulated in R
223 Global
expression analysis revealed that LIF-independent iOCT4
224 Genome-wide gene
expression analysis revealed that PHF8 overexpression in
225 Expression analysis revealed that rs9517723 TT homozygot
226 Global mRNA
expression analysis revealed that SMCR8 regulates transc
227 Differential gene
expression analysis revealed that strain SN2 displayed s
228 Expression analysis revealed that TERMINAL FLOWER1 (TgTF
229 r-initiating cells in vivo Furthermore, gene
expression analysis revealed that the epithelium-specifi
230 Systematic gene
expression analysis revealed that the time elapsed, much
231 An integrative
expression analysis revealed that the WNT pathway antago
232 Cross-species gene
expression analysis revealed that these novel MBG3 model
233 Complementation test and gene
expression analysis revealed that this non-coding region
234 Expression analysis reveals both diverse expression patt
235 Furthermore, genome-wide
expression analysis reveals that Jarid2 is required for
236 Surpassing the
expression analysis scope, our work also includes assess
237 Moreover, gene
expression analysis showed a lack of active transcriptio
238 Gene
expression analysis showed a relative fold reductions of
239 The gene
expression analysis showed an anabolic activity of Nandr
240 Gene
expression analysis showed differential treatment- and r
241 An allele-specific
expression analysis showed overwhelmingly more cis-diver
242 Gene
expression analysis showed significant upregulation of c
243 In vivo gene
expression analysis showed that (p)ppGpp positively regu
244 Expression analysis showed that Aglianico is able to acc
245 Differential
expression analysis showed that G. muris infection evoke
246 Gene
expression analysis showed that MB treatment altered the
247 Gene and protein
expression analysis showed that neuropilin 2 (NRP2), a k
248 RNA-seq and qRT-PCR
expression analysis showed that overexpression of SeCsp
249 Whole-genome
expression analysis showed the upregulation of genes inv
250 Gene
expression analysis showed upregulation of T follicular
251 Furthermore, gene
expression analysis shows that the Hox gene Abdominal-B
252 Most methods for mRNA
expression analysis start with the reverse transcription
253 Gene
expression analysis suggested that CHD4 coregulatory act
254 Expression analysis suggests differential expression of
255 Gene
expression analysis supports a stepwise process of matur
256 Forty-two patients underwent gene
expression analysis to assess the functional consequence
257 combined metabolomics, proteomics, and gene
expression analysis to characterize the effects of mono-
258 ic screens with large-scale single-cell gene
expression analysis to define the heterogeneity within t
259 , signaling modulation, and single-cell gene
expression analysis to delineate a developmental traject
260 enome-wide histone modification mapping, and
expression analysis to examine colorectal cancer cells l
261 We also employed allele-specific
expression analysis to find potential regulatory variant
262 use genome-wide association mapping and gene-
expression analysis to map the Mendelian blue locus, whi
263 and perform network-based differential gene
expression analysis to select biomarker candidates by co
264 pplications that range from multiplexed gene
expression analysis to the sensitive detection of infect
265 ((13)C-MFA), and RNA-sequencing differential
expression analysis to uncover the following metabolic t
266 edicated to symbiosis and used cell-specific
expression analysis together with protein localization t
267 Differential
expression analysis uncovered new immune-cell-specific g
268 Microarray mRNA
expression analysis uncovered oxidative stress and DNA d
269 needle kidney biopsy specimens for microRNA
expression analysis using deep sequencing.
270 However, co-
expression analysis using human cancer transcriptomic da
271 CR platforms were directly compared for gene
expression analysis using low amounts of purified, synth
272 Genome-wide gene
expression analysis using RNA-seq identified an array of
273 Differential gene
expression analysis using RNA-Seq showed consistent expr
274 trapping, while IsoDE2 performs differential
expression analysis using the bootstrap samples generate
275 y to three existing methods for differential
expression analysis using three data examples of technic
276 Genome-wide
expression analysis using transgenic lines suggested tha
277 Whole genome microarray
expression analysis using tumor and OSE-derived cell lin
278 For differential
expression analysis,
voom with quality weights marginall
279 ulation of RGC physiology, differential gene
expression analysis was conducted to identify transcript
280 Gene
expression analysis was done with both quantitative real
281 The gene
expression analysis was limited to 174 individuals with
282 Previous co-
expression analysis was mainly conducted at bulk tissue
283 First, gene
expression analysis was performed in mice after hepatic
284 to earlier onset of disease, microarray gene
expression analysis was performed on B cells from 4-week
285 Global gene
expression analysis was performed on HMEC lines expressi
286 sncRNA identification and differential
expression analysis was performed with iMir software.
287 Using a RhoGTPase mRNA
expression analysis we identified RhoC as the highest ex
288 Using high resolution global methylation and
expression analysis we show that whereas patterns of met
289 Using gene
expression analysis,
we identified mRNAs proportionally
290 Using genome-wide
expression analysis,
we observed upregulation of genes i
291 Based on pathology and gene
expression analysis,
we report the first successful tran
292 osphoproteomics dataset in concert with gene
expression analysis,
we selected over 100 kinases potent
293 By Affymetrix
expression analysis,
we show that the cytochrome P450 en
294 statistical methods for RNA-seq differential
expression analysis were designed for individual genes,
295 learning methodology for robust differential
expression analysis,
which can be a new avenue to signif
296 osensor, we perform dynamic single cell gene
expression analysis while simultaneously characterizing
297 A differential
expression analysis with Cufflinks and Stringtie was the
298 Gene
expression analysis with linear mixed-effects model show
299 e challenging in the context of differential
expression analysis with RNA-seq data.
300 Results Differential
expression analysis yielded a 14-gene radiogenomic signa