Result

Search results for the GEO ID: GSE20570

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GSM ID

GPL ID

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Title

Source name

Description

Characteristics

GSM516900

GPL1261

PTIP KO1

Female mouse heart 5 days after PTIP deletion (tamoxifen injection)

background: mixed C57B6 and B6129 ptip status: : PTIP KO tissue: heart gender: female

Dressler 10663

Sample_geo_accession	GSM516900
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516900/suppl/GSM516900.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

GSM516901

GPL1261

PTIP KO2

Female mouse heart 5 days after PTIP deletion (tamoxifen injection)

background: mixed C57B6 and B6129 ptip status: : PTIP KO tissue: heart gender: female

Dressler 10666

Sample_geo_accession	GSM516901
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516901/suppl/GSM516901.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

GSM516902

GPL1261

PTIP KO3

Female mouse heart 5 days after PTIP deletion (tamoxifen injection)

background: mixed C57B6 and B6129 ptip status: : PTIP KO tissue: heart gender: female

Dressler 10728

Sample_geo_accession	GSM516902
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516902/suppl/GSM516902.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

GSM516903

GPL1261

PTIP+ 1

Female mouse heart 5 days after tamoxifen injection

background: mixed C57B6 and B6129 ptip status: : PTIP+ tissue: heart gender: female

Dressler 10721

Sample_geo_accession	GSM516903
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516903/suppl/GSM516903.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

GSM516904

GPL1261

PTIP+ 2

Female mouse heart 5 days after tamoxifen injection

background: mixed C57B6 and B6129 ptip status: : PTIP+ tissue: heart gender: female

Dressler 10730

Sample_geo_accession	GSM516904
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516904/suppl/GSM516904.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

GSM516905

GPL1261

PTIP+ 3

Female mouse heart 5 days after tamoxifen injection

background: mixed C57B6 and B6129 ptip status: : PTIP+ tissue: heart gender: female

Dressler 10731

Sample_geo_accession	GSM516905
Sample_status	Public on Mar 02 2010
Sample_submission_date	Mar 01 2010
Sample_last_update_date	Mar 01 2010
Sample_type	RNA
Sample_channel_count	1
Sample_organism_ch1	Mus musculus
Sample_taxid_ch1	10090
Sample_treatment_protocol_ch1	8-week-old female mice that were a cross between C57BL6 and B6129 were treated with tamoxifen (20mg/kg intraperitoneal) for 5 days.
Sample_growth_protocol_ch1	Mice were maintained in microisolator cages according to the standard protocols outlined by the University of Michigan ULAM.
Sample_molecule_ch1	total RNA
Sample_extract_protocol_ch1	Hearts were removed after euthanasia and left ventricle (LV) apices were used to prepare RNA. Total RNA was extracted using the Qiagen RNA extraction kit.
Sample_label_ch1	biotin
Sample_label_protocol_ch1	RNA was prepared using the NuGen Ovation Biotin labeling system.
Sample_hyb_protocol	Standard Affymetrix protocol.
Sample_scan_protocol	Affymetrix Scanner 3000.
Sample_data_processing	We fit a weighted linear model designed specifically for microarray analysis to the data (Smyth (2004)) and then computed the contrasts of interest. This technique is almost identical to fitting individual t-statistics to each probeset, but we increase our power to detect differences by improving the accuracy of our variance estimate by pooling information from all the probesets to adjust the variance estimate. Also, by using weights we can downweight chips that are considered less reproducible by an efficiency estimate of their array variances (Ritchie et al. (2006)). This technique will not hurt the results if all the chips are of good quality because the weights for each chip can be equal. We filtered out those probesets that had a variance over all samples less than 0.1. This removes probesets that don't change expression in any sample, which by definition are not interesting to us. We then selected probesets based on an adjusted p-value of 0.05 (adjusted for multiple comparisons using false discovery rate (FDR) Benjamini and Hochberg (1995)). This adjustment is different from say, a Bonferroni, which aims to keep the overall p-value at 0.05 (e.g., a Bonferroni correction attempts to limit the probability of any false positives at 5%). Instead, with FDR we choose the number of probesets we think are significantly different, and estimate how many are false positives. So a FDR of 0.05 indicates that we think there are at most 5% false positives.
Sample_data_processing	We calculated expression values for each gene using a robust multi-array average (RMA) Irizarry et al. (2003). This is a modeling strategy that converts the PM probe values into an expression value for each gene. Note that the expression values are log2 transformed data. These data can be converted to the natural scale by exponentiating (e.g., convert by using 2x, where x is the expression value).
Sample_platform_id	GPL1261
Sample_contact_name	Adam,B.,Stein
Sample_contact_email	adamstei@umich.edu
Sample_contact_department	Medicine
Sample_contact_institute	University of Michigan
Sample_contact_address	108 Zina Pitcher Dr
Sample_contact_city	Ann Arbor
Sample_contact_state	MI
Sample_contact_zip/postal_code	48109
Sample_contact_country	USA
Sample_supplementary_file	ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM516nnn/GSM516905/suppl/GSM516905.CEL.gz
Sample_series_id	GSE20570
Sample_data_row_count	45101

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